WO2023216380A1 - Oil port resource optimization scheduling method - Google Patents

Oil port resource optimization scheduling method Download PDF

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WO2023216380A1
WO2023216380A1 PCT/CN2022/100941 CN2022100941W WO2023216380A1 WO 2023216380 A1 WO2023216380 A1 WO 2023216380A1 CN 2022100941 W CN2022100941 W CN 2022100941W WO 2023216380 A1 WO2023216380 A1 WO 2023216380A1
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oil
tanker
berth
scheduling model
individual
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PCT/CN2022/100941
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French (fr)
Chinese (zh)
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张素杰
郑玉洁
薛宝龙
邢东亮
赵冰
于守水
杨献鹏
于志涛
赵洋
韩锋
郝为建
李婷婷
王德利
宋京晖
王元波
王瑾
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青岛港国际股份有限公司
青岛实华原油码头有限公司
山东科技大学
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Priority to AU2022457517A priority Critical patent/AU2022457517A1/en
Publication of WO2023216380A1 publication Critical patent/WO2023216380A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping

Definitions

  • the invention belongs to the technical field of port equipment, and in particular relates to an oil port resource optimization and dispatching method.
  • Oil port refers to a port dedicated to loading and unloading crude oil or refined oil products. Oil port resource dispatching is of great significance to the construction of smart ports. Reasonable scheduling can balance the berth occupancy time, increase oil port profits, and optimize oil port operating efficiency.
  • oil port resource scheduling usually adopts the form of manual preparation of unloading plans: before the tanker enters the port, the shore provides the ship with information such as the number of ship-to-shore connecting berths and oil pipelines; the ship provides the shore with the types of oil products to be shipped, Information such as the tonnage of oil products; and the ship and shore will formulate and implement an unloading plan after confirming the type and quantity of crude oil, unloading sequence, unloading rate and other information.
  • the ship and shore will formulate and implement an unloading plan after confirming the type and quantity of crude oil, unloading sequence, unloading rate and other information.
  • This invention is aimed at the existing manual preparation of unloading operation plan technology in oil ports. It is difficult to optimize the allocation of oil port resources, cannot make full use of oil port resources, and has problems that need to be improved in intelligence and rationalization. It designs and provides an oil port resource optimization method. Scheduling method.
  • the present invention adopts the following technical solutions to achieve it:
  • An oil port resource optimization dispatching method includes the following steps:
  • Step S11 Establish a lower-layer scheduling model.
  • the lower-layer scheduling model is:
  • min G 2 max ⁇ TR i,j,p +TZ i,j,p
  • T k represents the usage time of berth k during the set planning period:
  • TZ i,j,p represents the transportation time of the jth oil product transported by oil tanker i through oil pipeline p:
  • E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p;
  • v j,p represents the flow rate of the jth oil product transported in oil pipeline p;
  • TR i,j,p +TZ i,j,p means that when the conditions for the jth oil product to be transported by oil pipeline p to tanker i are met, the sum of the preparation time and the transportation time is TR i,j,p +TZ i,j,p ;
  • p ⁇ represents the maximum value of the sum of the preparation time and the transportation time of the jth oil product transported by the oil pipeline to the tanker i;
  • p ⁇ represents the minimum value of the sum of the preparation time and the delivery time of the jth oil product transported by the oil pipeline to the tanker i;
  • Step S12 Establish an upper-layer scheduling model.
  • the upper-layer scheduling model is:
  • u i,j represents the cost per ton paid by tanker i to unload oil product j;
  • E i,j represents the tonnage of oil product j unloaded by tanker i;
  • L p represents the length of the oil pipeline p
  • PC j,p represents the unit cost of oil pipeline p transporting oil product j
  • E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p;
  • DC i represents the penalty unit cost of the detention period of tanker i
  • TL i,k represents the departure time of tanker i after completing operations at berth k
  • TD i represents the latest allowed departure time of tanker i
  • TA i represents the entry time of tanker i
  • Step S13 Use a multi-objective evolutionary algorithm to solve the lower-layer scheduling model and the upper-layer scheduling model;
  • Step S14 Determine the target berth and berthing sequence of the tanker based on the solution results.
  • the invention can ensure that the operation of the entire oil port is relatively balanced and stable under the factors of multiple storage tanks and multiple oil pipelines; on the other hand, it can achieve the control goals of maximizing the profit of the oil port terminal and minimizing the total time of the oil tanker in port. , is the most optimized scheduling scheme. Compared with the traditional manual compilation method, its intelligence and rationality are significantly improved.
  • Figure 1 is a schematic structural diagram of an oil port suitable for the resource optimization dispatching method provided by the present invention
  • Figure 2 is a flow chart of the oil port resource optimization and dispatching method provided by the present invention.
  • Figure 3 is a flow chart when using a multi-objective evolutionary algorithm to solve the lower-level scheduling model and the upper-level scheduling model.
  • the terms “upper”, “lower”, “left”, “right”, “vertical”, “horizontal”, “inner”, “outer”, etc. indicate the direction or position.
  • the terms of relationship are based on the orientation or positional relationship shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as Limitations on the invention.
  • the terms “first” and “second” are used for descriptive purposes only and are not to be understood as indicating or implying relative importance.
  • oil port resources can be divided into tanker layer, berth layer and oil tank layer; in Figure 1, the tanker layer exemplarily includes oil tanker 1, oil tanker 2,..., oil tanker n, and the berth layer is an example It exemplarily includes berth 1, berth 2,..., berth m, and the oil tank layer exemplarily includes oil tank 1, oil tank 2,..., and oil tank r.
  • the berth layer and the oil tank layer are connected through a number of oil pipelines.
  • the unloaded oil products can be stored in target oil tanks.
  • the oil tanks are set according to the type of oil. Each type of oil tank is used to store one type of oil product. Several of each type of oil tank can be optionally set according to needs.
  • the oil port resource optimization dispatching method provided by the present invention aims to allocate berths, select a berth according to the principle of efficiency for incoming oil tankers, and allocate the berths to the oil tankers in the optimal order for the oil tankers to berth and perform unloading operations. , control the total time of oil tankers in port, and increase the profits of oil ports.
  • the basis for berth allocation to oil port resource dispatch is the core of the entire optimized dispatch method.
  • the oil transportation volume of the pipeline will affect the oil transportation time: the greater the oil transportation volume of the pipeline, the shorter the oil transportation time; conversely, if the pipeline oil transportation volume is smaller, the oil transportation time will be longer; on the other hand, , the berth and berthing sequence of the tanker will also affect the berth occupancy time and oil product delivery time: if the berth and berthing sequence of the tanker are reasonable, the berth occupancy time and the oil product delivery time will be shorter; conversely, if the tanker berths at the berth The berthing sequence is unreasonable, and the berth occupancy time and oil product delivery time are long.
  • the total time an oil tanker is in port is the sum of the oil tanker's waiting time, crude oil delivery preparation time and crude oil delivery time.
  • Reasonable berth allocation can minimize the oil tanker's waiting time, crude oil delivery preparation time and crude oil delivery time, shortening the tanker's waiting time.
  • the total time in port increases the effective unloading operation time of the berth and increases the profits of the oil port.
  • the oil port resource optimization dispatching method provided by the present invention includes multiple steps as described in detail below.
  • the oil port resource optimization dispatching method provided by the present invention can be run in the oil port resource management system.
  • the oil port resource management system can be implemented by a controller with a processor.
  • the controller also includes a storage unit connected to the controller and necessary Peripheral circuits.
  • Step S11 Establish a lower-layer dispatching model; the control objectives of the lower-layer dispatching model include: the highest berth utilization equilibrium rate (minimum standard deviation) and the highest transmission duration equilibrium rate of the oil pipeline.
  • the lower-layer scheduling model can be expressed by the following formula:
  • p represents the condition that oil tanker i is transported by oil pipeline
  • the usage time T k of the berth k during the set planning period is calculated by the following formula:
  • the average usage time of all berths during the set planning period is calculated by the following formula:
  • the preparation time TR i, j, p of the j-th oil product transported by oil tanker i through oil pipeline p is calculated by the following formula:
  • TR p represents the transportation preparation time of the transportation pipeline p, including but not limited to the startup preparation time of fluid control components such as pumps and valves installed on the transportation pipeline p.
  • TR p can be generated based on the historical data of the detection and stored in the storage in advance. unit for call at any time;
  • the transportation time of the jth oil product transported by oil tanker i through oil pipeline p is calculated by the following formula:
  • E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p;
  • v j,p represents the flow rate of the jth oil product transported in oil pipeline p;
  • the feasible solution obtained through the lower-level dispatch model that is, the ideal tanker berth, can maintain the berth occupancy time and the transportation time of the oil pipeline in a balanced state, that is, under the multi-factor conditions of multiple storage tanks and multiple oil pipelines, the tanker, berth , oil pipelines and oil tanks are performing unloading operations in correlation with each other, and are in a relatively balanced and stable state.
  • the lower-level scheduling model also includes the following constraints:
  • Tanker berthing constraints Oil tankers can only perform oil unloading operations when entering the oil port, and tankers can only berth at one berth.
  • the tanker berthing constraints can be expressed by the following formula:
  • i the oil tanker
  • i 1,2...,n
  • n the number of oil tankers.
  • Tanker load capacity constraint The tanker is allowed to enter the target berth if and only if the tanker load capacity is less than the berthing capacity of the target berth.
  • the tanker load capacity constraint can be expressed by the following formula:
  • Oil pipeline constraints In order to realize oil unloading operations, at least one oil pipeline is set up between the target berth and the oil tank.
  • the constraints of the oil pipeline can be expressed as:
  • q is the number of oil pipelines
  • j represents the type of oil
  • j 1, 2..., u
  • u represents the number of oil types
  • s represents the oil tank
  • s 1, 2..., r
  • r is the oil tank quantity
  • Unloading capacity constraints During the set planning period, the type of oil to be unloaded needs to match the type of oil that can be accommodated in the target tank, and the target unloading volume of oil in the tanker is less than the remaining capacity of the target tank. .
  • the offloading capability constraints can be expressed as:
  • F s represents the remaining storage capacity of oil tank s
  • E i,j represents the tonnage of oil product j unloaded by tanker i.
  • the tonnage of oil product j unloaded by tanker i is a known number and can be obtained from the tanker; the remaining storage capacity of oil tank s can be detected in real time, for example, using The radar level sensor detects the real-time level of the oil tank s.
  • Unloading target constraints All oil products in the tanker need to be unloaded.
  • the offloading target constraints can be expressed as:
  • E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p.
  • the feasible solution obtained by the lower-level scheduling model on the premise of meeting the requirements of the tanker unloading operation, that is, the tanker berth, as well as the determined oil storage tank, oil transportation pipeline, and pipeline oil transportation volume, can ensure the effective implementation of the unloading operation, and at the same time
  • the operations of the entire oil port are effective and stable, and are in a relatively balanced and stable state.
  • Based on the lower-level scheduling model further build an upper-level scheduling model.
  • the oil port resource optimization dispatching method provided by the present invention further includes the following steps:
  • Step S12 Establish an upper-level dispatch model; the control objectives of the upper-level dispatch model include: maximizing the profit of the oil port terminal and minimizing the total time of the oil tanker in port.
  • the upper-layer scheduling model can be expressed by the following formula:
  • u i,j represents the fee (unit price per ton) paid by tanker i to unload oil product j, u i,j is the set value and is a callable constant;
  • E i,j represents the tonnage of oil product j unloaded by tanker i;
  • L p represents the length of the oil pipeline p, L p is the set value and is a callable constant;
  • PC j,p represents the unit cost of oil pipeline p transporting oil product j
  • E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p;
  • DC i represents the penalty cost of the detention period of tanker i (unit is time), DC i is the set value and is a callable constant;
  • TL i,k represents the departure time of tanker i after completing operations at berth k
  • TD i represents the latest allowed departure time of tanker i
  • TA i represents the entry time of tanker i. It can be the time when tanker i has completed the entry procedures and is allowed to enter the port.
  • the feasible solution obtained by the upper-level scheduling model that is, the berthing sequence of oil tankers, can ensure the maximum profit of the oil port terminal and the minimum total time of oil tankers in port.
  • the upper-level scheduling model also includes the following constraints:
  • Oil pipeline constraints In order to realize oil unloading operations, at least one oil pipeline is set up between the target berth and the oil tank.
  • the constraints of the oil pipeline can be expressed as:
  • TB i+1,k represents the time when the i+1th oil tanker berths into berth k
  • TL i,k represents the departure time after oil tanker i completes its operation at berth k, that is, when the i+1th oil tanker berths.
  • the time at berth k is later than the departure time of tanker i after completing operations at berth k.
  • TL i,k represents the departure time of tanker i after completing its operation at berth k
  • TB i,k represents the time for the i-th oil tanker to berth at berth k
  • p ⁇ represents the maximum value of the sum of the preparation time and delivery time of the jth oil product transported by oil tanker i through the oil pipeline;
  • the preparation time TR i,j,p of the jth oil product transported by the oil tanker i through the oil pipeline p is calculated by the following formula:
  • TR p represents the transportation preparation time of the transportation pipeline p, including but not limited to the startup preparation time of fluid control components such as pumps and valves installed on the transportation pipeline p.
  • TR p can be generated based on the historical data of the detection and stored in the storage in advance. unit for call at any time;
  • the transportation time of the jth oil product transported by oil tanker i through oil pipeline p is calculated by the following formula:
  • E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p;
  • v j,p represents the flow rate of the jth oil product transported in oil pipeline p;
  • Step S13 Use a multi-objective evolutionary algorithm to solve the lower-layer scheduling model and the upper-layer scheduling model.
  • the above-mentioned upper-layer scheduling model and lower-layer scheduling model can be solved by software, such as Matlab, etc.
  • Step S14 Determine the target berth and berthing sequence of the tanker based on the solution results.
  • a multi-objective evolutionary algorithm is used to solve the lower-layer scheduling model.
  • using a multi-objective evolutionary algorithm to solve the lower-level scheduling model includes the following steps:
  • Step S21 Individual coding, that is, the berth number corresponding to the feasible solution of the lower-layer scheduling model is expressed in binary.
  • the tanker berth corresponding to the feasible solution (also called an individual) of the lower-level dispatch model has a unique berth number, and the berth number is expressed in binary.
  • the berth number expressed in binary constitutes the genotype of the individual (i.e., the genotype of the feasible solution).
  • Genotype and phenotype (berth number) can be converted into each other through encoding and decoding procedures.
  • Step S22 Generate initial population
  • Random selection can be implemented by a random algorithm, and the initial population is defined as pop1.
  • Step S23 Fitness calculation: Calculate the fitness of the sample individual.
  • TOPSIS Technique for Order Preference by Similarity to an Ideal Solution
  • the fitness function is expressed as:
  • g l represents the fitness value of the l-th individual in the initial population of the lower-layer scheduling model.
  • the objective function is optionally set to the model function of the underlying scheduling model.
  • Step S24 Selection operation; perform selection operation according to the individual fitness of the sample to select individual samples.
  • the relative fitness is the ratio of the individual fitness to the sum of fitness; each relative fitness can be expressed by probability, because The sum of all probability representations is 1, which can be regarded as the area corresponding to the probability values corresponding to all relative fitnesses can form a complete area (such as a disk).
  • the selection mark is randomly placed within this complete area, and an independent area selected represents the selected individual. That is, it is similar to turning a disk to select an individual from the population. Select the target number of individuals from the initial population and rotate the target sub-disk, which is the standard roulette wheel method.
  • Step S25 Crossover operation; perform crossover operation on the selected individual samples.
  • Randomly select two individuals from the selected individual sample and perform crossover with a certain crossover probability linearly interpolate the corresponding genes at random positions according to the coefficients, and keep the remaining positions unchanged, thus obtaining two new individuals.
  • the crossover probability is adjusted according to the fitness of the two individuals participating in the crossover operation. First, set the crossover probability interval [pc min , pc max ], and then calculate the individual fitness, average fitness f avg , and minimum fitness f min of the population.
  • the crossover probability is determined by the following formula:
  • f' is the one with greater fitness among the two individuals participating in the crossover operation.
  • Step S26 Mutation operation; perform mutation operation on the individual samples after the crossover operation.
  • the value of one or more mutation points in the crossed individual sample is inverted according to the mutation probability.
  • the mutation probability is determined by the following formula:
  • pm represents the adaptive mutation probability
  • pm min represents the minimum mutation probability
  • pm max represents the maximum mutation probability
  • f min represents the minimum fitness value in the individual sample population after crossover
  • f avg represents the adaptation in the individual sample population after crossover Average degree
  • f represents the fitness value of the mutated individual.
  • Step S27 Determine whether the iteration conditions are met: if the iteration conditions are met, exit the calculation and obtain the optimal population pop1-opt of the underlying scheduling model; if the iteration conditions are not met, the individual samples after the mutation operation are used as the next generation population. Step S23 to step S26 are executed in a loop until the iteration condition is met.
  • the next generation population is obtained.
  • the process of selection calculation, crossover operation and mutation operation is further cyclically executed until the number of iterations reaches the upper limit (the iteration conditions are met), the calculation is exited, and the optimal population pop1-opt corresponding to the maximum fitness of the lower scheduling model is obtained.
  • the oil port berth is obtained, and then the oil storage tank, oil transportation pipeline, and pipeline oil volume are determined.
  • Step S28 Calculate E i,j,p corresponding to each individual in the optimal population pop1-out.
  • E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p.
  • Step S29 Substitute the calculated E i,j,p into the upper-layer scheduling model.
  • a multi-objective evolutionary algorithm is used to solve the upper-layer scheduling model substituted into E i,j,p .
  • using a multi-objective evolutionary algorithm to solve the upper-layer scheduling model substituted into E i,j,p includes the following steps:
  • Step S30 Individual coding, that is, the berth number corresponding to the feasible solution of the upper-layer scheduling model is expressed in binary.
  • the tanker berthing sequence corresponding to the feasible solution (also called an individual) of the upper-level dispatch model has a unique sequence number, and the berthing sequence is expressed in binary.
  • the genotype of the individual (that is, the genotype of the feasible solution) is composed of the docking sequence number represented in binary. Genotypes and phenotypes (docking sequence numbers) can be converted into each other through encoding and decoding procedures.
  • Step S31 Generate an initial population.
  • Random selection can be implemented by a random algorithm, and the initial population is defined as pop2.
  • Step S32 Fitness calculation: Calculate the fitness of the sample individual.
  • TOPSIS Technique for Order Preference by Similarity to an Ideal Solution
  • the fitness function is expressed as:
  • g l represents the fitness value of the l-th individual in the initial population of the upper-level scheduling model. The smaller g l is, the better the individual is; Represents the distance from the objective function value of the l-th individual of the initial population of the upper-level scheduling model to the negative ideal solution of the upper-level scheduling model; Indicates the distance from the objective function value of the l-th individual in the initial population of the upper-level scheduling model to the positive ideal solution of the upper-level scheduling model.
  • the objective function is optionally set to the model function of the upper-layer scheduling model. For the function that takes the maximum value, it can be converted into the minimum value by using the reciprocal form, and 1 can be added to the denominator to avoid function overflow.
  • Step S33 Selection operation; perform selection operation according to the individual fitness of the sample to select individual samples.
  • the selection operation preferably adopts the standard roulette wheel method.
  • Step S34 Crossover operation; perform crossover operation on the selected individual samples.
  • Randomly select two individuals from the selected individual sample and perform crossover with a certain crossover probability linearly interpolate the corresponding genes at random positions according to the coefficients, and keep the remaining positions unchanged, thus obtaining two new individuals.
  • the crossover probability is adjusted according to the fitness of the two individuals participating in the crossover operation. First, set the crossover probability interval [pc min , pc max ], and then calculate the individual fitness, average fitness f avg , and minimum fitness f min of the population.
  • the crossover probability is determined by the following formula:
  • f' is the one with greater fitness among the two individuals participating in the crossover operation.
  • Step S35 Mutation operation; perform mutation operation on the individual samples after the crossover operation.
  • the value of one or more mutation points in the crossed individual sample is inverted according to the mutation probability.
  • the mutation probability is determined by the following formula:
  • pm represents the adaptive mutation probability
  • pm min represents the minimum mutation probability
  • pm max represents the maximum mutation probability
  • f min represents the minimum fitness value in the individual sample population after crossover
  • f avg represents the adaptation in the individual sample population after crossover Average degree
  • f represents the fitness value of the mutated individual.
  • Step S36 Determine whether the iteration conditions are met: if the iteration conditions are met, exit the calculation and obtain the optimal population pop1-opt of the upper-layer scheduling model; if the iteration conditions are not met, use the individual samples after the mutation operation as the next generation population, and loop Steps S32 to S35 are executed until the iteration conditions are met.
  • the optimal solution of the upper-layer dispatch model is obtained based on the optimal solution of the lower-layer dispatch model, the final obtained oil port berths, oil storage tanks, oil transportation pipelines, pipeline oil transportation volume,
  • the berthing sequence of the oil port can, on the one hand, ensure that the operation of the entire oil port is relatively balanced and stable under the factors of multiple storage tanks and multiple oil pipelines. On the other hand, it can achieve the maximum profit of the oil port terminal and the total time of the oil tanker in port.
  • the smallest control target is the most optimized scheduling plan. Compared with the traditional manual programming method, the intelligence and rationality are significantly improved.

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Abstract

Disclosed in the present invention is an oil port resource optimization scheduling method, comprising: step S11: establishing a lower-level scheduling model; step S12: establishing an upper-level scheduling module; step S13: using a multi-objective evolutionary algorithm to solve the lower-level scheduling model and the upper-level scheduling model; and step S14: determining a target berth and a berthing sequence of an oil tanker according to a solving result. According to the present invention, on one hand, the operation of the whole oil port can be ensured to be relatively balanced and relatively stable under the factor conditions of multiple storage tanks and multiple oil pipelines, and on the other hand, control targets of maximum profit of an oil port wharf and minimum total in-port time of the oil tanker can be achieved, the present invention is an optimal scheduling solution, and compared with a traditional manual compilation method, the intelligence and rationality are both obviously improved.

Description

一种油港资源优化调度方法An oil port resource optimization dispatching method 技术领域Technical field
本发明属于港口设备技术领域，尤其涉及一种油港资源优化调度方法。The invention belongs to the technical field of port equipment, and in particular relates to an oil port resource optimization and dispatching method.
背景技术Background technique
油港，是指专门装卸原油或成品油的港口。油港资源调度对于智慧港口建设具有重要意义。合理的调度可以均衡泊位占用时长，提高油港利润，优化油港作业效率。Oil port refers to a port dedicated to loading and unloading crude oil or refined oil products. Oil port resource dispatching is of great significance to the construction of smart ports. Reasonable scheduling can balance the berth occupancy time, increase oil port profits, and optimize oil port operating efficiency.
当前油港资源调度通常还是采用人工编制卸载计划的形式：在油轮入港前由岸方向船方提供船岸连接泊位、输油管道的数量等信息；船方向岸方提供航次所装运的油品品种、油品吨数等信息；并由船岸双方确认原油种类和数量、卸载顺序、卸载速率等信息后制定卸载计划并执行。但是，由于卸载作业中可能存在多个输油管道同时输送，多个油罐同时存储的情况，传统的人工编制卸载计划技术很难最优化油港资源分配，无法充分利用油港资源，存在智能化和合理化有待提高的问题。At present, oil port resource scheduling usually adopts the form of manual preparation of unloading plans: before the tanker enters the port, the shore provides the ship with information such as the number of ship-to-shore connecting berths and oil pipelines; the ship provides the shore with the types of oil products to be shipped, Information such as the tonnage of oil products; and the ship and shore will formulate and implement an unloading plan after confirming the type and quantity of crude oil, unloading sequence, unloading rate and other information. However, since there may be multiple oil pipelines transporting oil at the same time and multiple oil tanks storing oil at the same time during the unloading operation, it is difficult for the traditional manual unloading planning technology to optimize the allocation of oil port resources and make full use of the oil port resources. and rationalization issues that need to be improved.
发明内容Contents of the invention
本发明针对油港现有的人工编制卸载作业计划技术，很难最优化油港资源分配，无法充分利用油港资源，存在智能化和合理化有待提高的问题，设计并提供一种油港资源优化调度方法。This invention is aimed at the existing manual preparation of unloading operation plan technology in oil ports. It is difficult to optimize the allocation of oil port resources, cannot make full use of oil port resources, and has problems that need to be improved in intelligence and rationalization. It designs and provides an oil port resource optimization method. Scheduling method.
为实现上述发明目的，本发明采用下述技术方案予以实现：In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions to achieve it:
一种油港资源优化调度方法，包括以下步骤：An oil port resource optimization dispatching method includes the following steps:
步骤S11：建立下层调度模型，所述下层调度模型为：Step S11: Establish a lower-layer scheduling model. The lower-layer scheduling model is:
Figure PCTCN2022100941-appb-000001
Figure PCTCN2022100941-appb-000001
min G 2＝max{TR i,j,p+TZ i,j,p|p}-min{TR i,j,p+TZ i,j,p|p}； min G 2 =max{TR i,j,p +TZ i,j,p |p}-min{TR i,j,p +TZ i,j,p |p};
其中：in:
k代表泊位，k＝1,2…,m,m代表油港泊位的数量；k represents the berth, k=1,2...,m, m represents the number of oil port berths;
T k代表设定计划期内泊位k的使用时长： T k represents the usage time of berth k during the set planning period:
Figure PCTCN2022100941-appb-000002
Figure PCTCN2022100941-appb-000002
Figure PCTCN2022100941-appb-000003
代表设定计划期内所有m个泊位的平均使用时长：
Figure PCTCN2022100941-appb-000003
Represents the average usage time of all m berths during the set planning period:
Figure PCTCN2022100941-appb-000004
Figure PCTCN2022100941-appb-000004
TR i,j,p表示通过输油管道p输送油轮i的第j种油品的准备时长；p代表输油管道，p＝1,2…,q,q代表输油管道的数量；j代表油品种类，j＝1,2…,u,u代表油品种类的数量；i代表油轮，i＝1,2…,n，n代表油轮的数量；TR i,j,p＝w i,j,pTR p，TR p表示输送管道p的输送准备时间，w i,j,p满足： TR i,j,p represents the preparation time for the jth oil product transported by tanker i through oil pipeline p; p represents the oil pipeline, p=1,2...,q,q represents the number of oil pipelines; j represents the type of oil , j=1,2...,u,u represents the number of oil types; i represents the oil tanker, i=1,2...,n, n represents the number of oil tankers; TR i,j,p = w i,j,p TR p , TR p represents the transportation preparation time of the transportation pipeline p, w i, j, p satisfy:
Figure PCTCN2022100941-appb-000005
Figure PCTCN2022100941-appb-000005
TZ i,j,p表示通过输油管道p输送油轮i的第j种油品的输送时长： TZ i,j,p represents the transportation time of the jth oil product transported by oil tanker i through oil pipeline p:
Figure PCTCN2022100941-appb-000006
其中，E i,j,p表示通过输油管道p输送的油轮i的油品j的吨数；v j,p表示输油管道p中输送的第j种油品的流量；
Figure PCTCN2022100941-appb-000006
Among them, E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p; v j,p represents the flow rate of the jth oil product transported in oil pipeline p;
TR i,j,p+TZ i,j,p|p表示满足由输油管道p输送油轮i的第j种油品的条件时，准备时长和输送时长之和为TR i,j,p+TZ i,j,p； TR i,j,p +TZ i,j,p |p means that when the conditions for the jth oil product to be transported by oil pipeline p to tanker i are met, the sum of the preparation time and the transportation time is TR i,j,p +TZ i,j,p ;
max{TR i,j,p+TZ i,j,p|p}代表输油管道输送油轮i的第j种油品的准备时长和输送时长之和的最大值； max{TR i,j,p +TZ i,j,p |p} represents the maximum value of the sum of the preparation time and the transportation time of the jth oil product transported by the oil pipeline to the tanker i;
min{TR i,j,p+TZ i,j,p|p}代表输油管道输送油轮i的第j种油品的准备时长和输送时长之和的最小值； min{TR i,j,p +TZ i,j,p |p} represents the minimum value of the sum of the preparation time and the delivery time of the jth oil product transported by the oil pipeline to the tanker i;
步骤S12：建立上层调度模型，所述上层调度模型为：Step S12: Establish an upper-layer scheduling model. The upper-layer scheduling model is:
maxF 1＝∑ i∑ ju i,jE i,j-∑ i∑ j∑ py i,kw i,j,pL pPC j,pE i,j,p-∑ iDC i×max{((TL i,k-TD i)),0}； maxF 1 ＝∑ i ∑ j u i,j E i,j -∑ i ∑ j ∑ p y i,k w i,j,p L p PC j,p E i,j,p -∑ i DC i × max{((TL i,k -TD i )),0};
minF 2＝∑ k(TL i,k-TA i)； minF 2 =∑ k (TL i,k -TA i );
其中，u i,j表示油轮i卸载油品j所支付的每吨费用； Among them, u i,j represents the cost per ton paid by tanker i to unload oil product j;
E i,j表示油轮i卸载油品j的吨数； E i,j represents the tonnage of oil product j unloaded by tanker i;
Figure PCTCN2022100941-appb-000007
Figure PCTCN2022100941-appb-000007
Figure PCTCN2022100941-appb-000008
Figure PCTCN2022100941-appb-000008
L p表示输油管道p的长度； L p represents the length of the oil pipeline p;
PC j,p表示输油管道p输送油品j的单位成本； PC j,p represents the unit cost of oil pipeline p transporting oil product j;
E i,j,p表示通过输油管道p输送的油轮i的油品j的吨数； E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p;
DC i表示油轮i滞留期惩罚单位成本； DC i represents the penalty unit cost of the detention period of tanker i;
TL i,k表示油轮i在泊位k上作业完毕后的离港时间； TL i,k represents the departure time of tanker i after completing operations at berth k;
TD i表示油轮i的允许最晚离港时间； TD i represents the latest allowed departure time of tanker i;
TA i表示油轮i的进港时间； TA i represents the entry time of tanker i;
步骤S13:采用多目标进化算法求解下层调度模型和上层调度模型；Step S13: Use a multi-objective evolutionary algorithm to solve the lower-layer scheduling model and the upper-layer scheduling model;
步骤S14：根据求解结果确定油轮的目标泊位和靠泊顺序。Step S14: Determine the target berth and berthing sequence of the tanker based on the solution results.
与现有技术相比，本发明的优点和积极效果是：Compared with the existing technology, the advantages and positive effects of the present invention are:
本发明一方面可以确保在多储罐、多输油管道的因素条件下，整个油港的运行相对平衡且相对稳定，另一方面可以达到油港码头利润最大且油轮总在港时间最小的控制目标，是最为优化的调度方案，相较于传统的人工编制方法，智能性和合理性均明显提高。On the one hand, the invention can ensure that the operation of the entire oil port is relatively balanced and stable under the factors of multiple storage tanks and multiple oil pipelines; on the other hand, it can achieve the control goals of maximizing the profit of the oil port terminal and minimizing the total time of the oil tanker in port. , is the most optimized scheduling scheme. Compared with the traditional manual compilation method, its intelligence and rationality are significantly improved.
结合附图阅读本发明的具体实施方式后，本发明的其他特点和优点将变得更加清楚。Other features and advantages of the present invention will become more apparent after reading the detailed description of the invention in conjunction with the accompanying drawings.
附图说明Description of the drawings
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. Those of ordinary skill in the art can also obtain other drawings based on these drawings without exerting creative efforts.
图1为适用于本发明所提供的资源优化调度方法的油港的结构示意图；Figure 1 is a schematic structural diagram of an oil port suitable for the resource optimization dispatching method provided by the present invention;
图2为本发明所提供的油港资源优化调度方法的流程图；Figure 2 is a flow chart of the oil port resource optimization and dispatching method provided by the present invention;
图3为采用多目标进化算法求解下层调度模型和上层调度模型时的流程图。Figure 3 is a flow chart when using a multi-objective evolutionary algorithm to solve the lower-level scheduling model and the upper-level scheduling model.
具体实施方式Detailed ways
为了使本发明的目的、技术方案及优点更加清楚明白，以下将结合附图和实施例，对本发明作进一步详细说明。In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the drawings and embodiments.
需要说明的是，在本发明的描述中，术语“上”、“下”、“左”、“右”、“竖”、“横”、“内”、“外”等指示的方向或位置关系的术语是基于附图所示的方向或位置关系，这仅仅是为了便于描述，而不是指示或暗示所述装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性。It should be noted that in the description of the present invention, the terms "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate the direction or position. The terms of relationship are based on the orientation or positional relationship shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as Limitations on the invention. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance.
参见图1所示为适用于本发明所提供的资源优化调度方法的油港的结构示意图。如图1所示，执行卸载作业时，油港资源可分为油轮层、泊位层和油罐层；图1中油轮层示例性地包括油轮1、油轮2、…、油轮n，泊位层示例性地包括泊位1、泊位2、…、泊位m，油罐层示例性地包括油罐1、油罐2、…、油罐r。泊位层和油罐层之间通过若干输油管道连通，在卸载同一种油品时，多条输油管道可以并行同时输送。卸载的油品可以存储在目标油罐中，油罐按照油品种类设置，每一种油罐用于存储一种油品，每一种油罐根据需求可选地设置若干个。Referring to FIG. 1 , a schematic structural diagram of an oil port suitable for the resource optimization dispatching method provided by the present invention is shown. As shown in Figure 1, when performing unloading operations, oil port resources can be divided into tanker layer, berth layer and oil tank layer; in Figure 1, the tanker layer exemplarily includes oil tanker 1, oil tanker 2,..., oil tanker n, and the berth layer is an example It exemplarily includes berth 1, berth 2,..., berth m, and the oil tank layer exemplarily includes oil tank 1, oil tank 2,..., and oil tank r. The berth layer and the oil tank layer are connected through a number of oil pipelines. When unloading the same oil product, multiple oil pipelines can be transported in parallel and at the same time. The unloaded oil products can be stored in target oil tanks. The oil tanks are set according to the type of oil. Each type of oil tank is used to store one type of oil product. Several of each type of oil tank can be optionally set according to needs.
本发明所提供的油港资源优化调度方法，旨在对泊位进行分配，针对进港油轮按照高效的原则选择一个泊位并按照最优顺序将泊位分配给油轮以供油轮泊入并执行卸载作业，控制油轮总在港时间，提高油港利润。泊位分配至油港资源调度的基础，是整个优化调度方法的核心。The oil port resource optimization dispatching method provided by the present invention aims to allocate berths, select a berth according to the principle of efficiency for incoming oil tankers, and allocate the berths to the oil tankers in the optimal order for the oil tankers to berth and perform unloading operations. , control the total time of oil tankers in port, and increase the profits of oil ports. The basis for berth allocation to oil port resource dispatch is the core of the entire optimized dispatch method.
泊位分配具有以下特点：Berth allocation has the following characteristics:
(1)油轮到港后，判断是否有可用空闲泊位；若有，可以进行下一步泊位分配，若没有，油轮需要在锚地等待。(1) After the oil tanker arrives at the port, it is determined whether there are available free berths; if so, the next step of berth allocation can be made; if not, the oil tanker needs to wait at the anchorage.
(2)任意时刻，一个泊位只能停靠一艘油轮，而一艘油轮也只能占用一个泊位。(2) At any time, only one oil tanker can dock at one berth, and one oil tanker can only occupy one berth.
(3)油轮完成卸载作业之后准许离港，单次卸载作业结束。若油轮离港后再进港靠泊作业，则认定 为执行下一次卸载作业。(3) The tanker is allowed to leave the port after completing the unloading operation, and the single unloading operation ends. If the tanker leaves the port and then enters the port for berthing operations, it will be deemed to be performing the next unloading operation.
(4)如果油轮中存储有多种油品，只能依次卸载，不能同时卸载多种。(4) If there are multiple types of oil products stored in the tanker, they can only be unloaded in sequence, not multiple types at the same time.
(5)不考虑油轮因天气、设备故障等不可抗力因素延迟到达的因素。(5) The delayed arrival of the tanker due to weather, equipment failure and other force majeure factors is not considered.
从图1的油港结构示意图和泊位分配的特点可以得出，在卸载原油时，多条输油管道可以同时输送油品，多个油罐可以同时存储油品，不同种油罐可以存储不同种油品。所以，一方面，管道输油量会影响油品输送时长：管道输油量越大，油品输送时长越短；反之，如果管道输油量越小，油品输送时长越长；另一方面，油轮停靠的泊位和靠泊顺序也会影响泊位占用时长和油品输送时长：如果油轮停靠的泊位和靠泊顺序合理，泊位占用时长和油品输送时长较短；反之，如果油轮停靠的泊位和靠泊顺序不合理，泊位占用时长和油品输送时长较长。通常来说，油轮总在港时长为油轮待泊时长、原油输送准备时长和原油输送时长的总和，合理的泊位分配可以将油轮待泊时长、原油输送准备时长和原油输送时长最小化，缩短油轮总在港时长，提高泊位的有效卸载作业时长，提升油港利润。From the schematic diagram of the oil port structure and the characteristics of berth allocation in Figure 1, it can be concluded that when unloading crude oil, multiple oil pipelines can transport oil products at the same time, multiple oil tanks can store oil products at the same time, and different types of oil tanks can store different types of oil products. Oil products. Therefore, on the one hand, the oil transportation volume of the pipeline will affect the oil transportation time: the greater the oil transportation volume of the pipeline, the shorter the oil transportation time; conversely, if the pipeline oil transportation volume is smaller, the oil transportation time will be longer; on the other hand, , the berth and berthing sequence of the tanker will also affect the berth occupancy time and oil product delivery time: if the berth and berthing sequence of the tanker are reasonable, the berth occupancy time and the oil product delivery time will be shorter; conversely, if the tanker berths at the berth The berthing sequence is unreasonable, and the berth occupancy time and oil product delivery time are long. Generally speaking, the total time an oil tanker is in port is the sum of the oil tanker's waiting time, crude oil delivery preparation time and crude oil delivery time. Reasonable berth allocation can minimize the oil tanker's waiting time, crude oil delivery preparation time and crude oil delivery time, shortening the tanker's waiting time. The total time in port increases the effective unloading operation time of the berth and increases the profits of the oil port.
为达到上述目的，本发明所提供的油港资源优化调度方法包括如以下详细描述的多个步骤。本发明所提供的油港资源优化调度方法可以在油港资源管理系统中运行，油港资源管理系统可以由具有处理器的控制器实现，控制器还包括与控制器连接的存储单元以及必要的外围电路。In order to achieve the above object, the oil port resource optimization dispatching method provided by the present invention includes multiple steps as described in detail below. The oil port resource optimization dispatching method provided by the present invention can be run in the oil port resource management system. The oil port resource management system can be implemented by a controller with a processor. The controller also includes a storage unit connected to the controller and necessary Peripheral circuits.
步骤S11：建立下层调度模型；下层调度模型的控制目标包括：泊位利用均衡率最高(标准差最小)以及输油管路的输送时长均衡率最高。Step S11: Establish a lower-layer dispatching model; the control objectives of the lower-layer dispatching model include: the highest berth utilization equilibrium rate (minimum standard deviation) and the highest transmission duration equilibrium rate of the oil pipeline.
下层调度模型可以通过下式表示：The lower-layer scheduling model can be expressed by the following formula:
Figure PCTCN2022100941-appb-000009
Figure PCTCN2022100941-appb-000009
min G 2＝max{TR i,j,p+TZ i,j,p|p}-min{TR i,j,p+TZ i,j,p|p} min G 2 =max{TR i,j,p +TZ i,j,p |p}-min{TR i,j,p +TZ i,j,p |p}
在以上两式中，k代表泊位，k＝1,2…,m,m代表油港泊位的数量；T k代表设定计划期内泊位k的使用时长，
Figure PCTCN2022100941-appb-000010
代表设定计划期内所有m个泊位的平均使用时长；p代表输油管道，p＝1,2…,q,q代表输油管道的数量；j代表油品种类，j＝1,2…,u,u代表油品种类的数量；i代表油轮，i＝1,2…,n，n代表油轮的数量；TR i,j,p表示通过输油管道p输送油轮i的第j种油品的准备时长；TZ i,j,p表示通过输油管道p输送油轮i的第j种油品的输送时长；TR i,j,p+TZ i,j,p|p表示满足由输油管道p输送油轮i的第j种油品的条件时，准备时长和输送时长之和为TR i,j,p+TZ i,j,p，max{TR i,j,p+TZ i,j,p|p}代表输油管道输送油轮i的第j种油品的准备时长和输送时长之和的最大值；min{TR i,j,p+TZ i,j,p|p}代表输油管道输送油轮i的第j种油品的准备时长和输送时长之和的最小值。 In the above two equations, k represents the berth, k = 1, 2..., m, m represents the number of oil port berths; T k represents the usage time of berth k during the set planning period,
Figure PCTCN2022100941-appb-000010
represents the average usage time of all m berths within the set planning period; p represents the oil pipeline, p=1,2...,q,q represents the number of oil pipelines; j represents the oil type, j=1,2...,u , u represents the number of oil types; i represents the oil tanker, i = 1, 2..., n, n represents the number of oil tankers; TR i, j, p represents the preparation of the jth oil product transported by the oil tanker i through the oil pipeline p Duration; TZ i,j,p represents the transportation time of the jth oil product transported by oil tanker i through oil pipeline p; TR i,j,p +TZ i,j,p |p represents the condition that oil tanker i is transported by oil pipeline p When the condition of the jth oil product is, the sum of the preparation time and the transportation time is TR i,j,p +TZ i,j,p, max{TR i,j,p +TZ i,j,p |p} Represents the maximum value of the sum of the preparation time and delivery time of the jth oil product transported by the oil pipeline to the tanker i; min{TR i,j,p +TZ i,j,p |p} represents the jth oil product transported by the oil pipeline to the tanker i The minimum value of the sum of the preparation time and delivery time of j oil products.
当通过输油管道p输油时，设定计划期内泊位k的使用时长T k由下式计算： When oil is transported through the oil pipeline p, the usage time T k of the berth k during the set planning period is calculated by the following formula:
Figure PCTCN2022100941-appb-000011
Figure PCTCN2022100941-appb-000011
设定计划期内所有泊位的平均使用时长由下式计算：The average usage time of all berths during the set planning period is calculated by the following formula:
Figure PCTCN2022100941-appb-000012
Figure PCTCN2022100941-appb-000012
通过输油管道p输送油轮i的第j种油品的准备时长TR i,j,p由下式计算： The preparation time TR i, j, p of the j-th oil product transported by oil tanker i through oil pipeline p is calculated by the following formula:
TR i,j,p＝w i,j,pTR p； TR i,j,p = w i,j,p TR p ;
其中，TR p表示输送管道p的输送准备时间，包括但不限于设置于输送管道p上的泵、阀等流体控制部件的启动准备时间，TR p可以根据检测的历史数据生成并预先存储在存储单元中以供随时调用； Among them, TR p represents the transportation preparation time of the transportation pipeline p, including but not limited to the startup preparation time of fluid control components such as pumps and valves installed on the transportation pipeline p. TR p can be generated based on the historical data of the detection and stored in the storage in advance. unit for call at any time;
通过输油管道p输送油轮i的第j种油品的输送时长由下式计算：The transportation time of the jth oil product transported by oil tanker i through oil pipeline p is calculated by the following formula:
Figure PCTCN2022100941-appb-000013
Figure PCTCN2022100941-appb-000013
其中，E i,j,p表示通过输油管道p输送的油轮i的油品j的吨数；v j,p表示输油管道p中输送的第j种油品的流量； Among them, E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p; v j,p represents the flow rate of the jth oil product transported in oil pipeline p;
在以上两式中：In the above two equations:
w i,j,p满足： w i,j,p satisfies:
Figure PCTCN2022100941-appb-000014
即由输油管道p将油轮i的第j种油品输送到油罐中时，w i,j,p为1；否则，则w i,j,p为0。
Figure PCTCN2022100941-appb-000014
That is, when the jth oil product from tanker i is transported to the oil tank by the oil pipeline p, w i,j,p is 1; otherwise, w i,j,p is 0.
通过下层调度模型得到的可行解，即理想的油轮泊位，可以实现泊位占用时长和输油管道的输送时长均保持在均衡状态，即在多储罐、多输油管道的多因素条件下，油轮、泊位、输油管道以及油罐之间在相互关联执行卸载作业，并处于相对平衡和相对稳定的状态。The feasible solution obtained through the lower-level dispatch model, that is, the ideal tanker berth, can maintain the berth occupancy time and the transportation time of the oil pipeline in a balanced state, that is, under the multi-factor conditions of multiple storage tanks and multiple oil pipelines, the tanker, berth , oil pipelines and oil tanks are performing unloading operations in correlation with each other, and are in a relatively balanced and stable state.
考虑到油港卸载作业的特点，下层调度模型还包括以下约束条件：Taking into account the characteristics of oil port unloading operations, the lower-level scheduling model also includes the following constraints:
(1)油轮靠泊约束条件：油轮在进入油港时才能执行卸油作业，且油轮只能靠泊在一个泊位。油轮靠泊约束条件可以由以下公式表示：(1) Tanker berthing constraints: Oil tankers can only perform oil unloading operations when entering the oil port, and tankers can only berth at one berth. The tanker berthing constraints can be expressed by the following formula:
Figure PCTCN2022100941-appb-000015
Figure PCTCN2022100941-appb-000015
其中i代表油轮，i＝1,2…,n，n代表油轮的数量。Among them, i represents the oil tanker, i=1,2...,n, and n represents the number of oil tankers.
(2)油轮载重量约束条件：当且仅当油轮载重量小于目标泊位的靠泊能力时，油轮才允许进入目标泊位。油轮载重量约束条件可以由以下公式表示：(2) Tanker load capacity constraint: The tanker is allowed to enter the target berth if and only if the tanker load capacity is less than the berthing capacity of the target berth. The tanker load capacity constraint can be expressed by the following formula:
y i,k(W i-W k)≤0,i＝1,2,...,n；k＝1,2,...,m， y i,k (W i -W k )≤0,i＝1,2,...,n; k＝1,2,...,m,
W i表示油轮i的载重量；W k表示泊位k的靠泊能力。 W i represents the load capacity of tanker i; W k represents the berthing capacity of berth k.
在油轮靠泊约束条件和油轮载重量约束条件中，y i,k满足： Among the tanker berthing constraints and tanker load capacity constraints, y i,k satisfies:
Figure PCTCN2022100941-appb-000016
Figure PCTCN2022100941-appb-000016
(3)输油管道约束条件：为实现卸油作业，目标泊位和油罐之间设置有至少一条输油管道。输油管道约束条件可以表示为:(3) Oil pipeline constraints: In order to realize oil unloading operations, at least one oil pipeline is set up between the target berth and the oil tank. The constraints of the oil pipeline can be expressed as:
Figure PCTCN2022100941-appb-000017
Figure PCTCN2022100941-appb-000017
其中，q为输油管道的数量，j代表油品种类，j＝1,2…,u,u代表油品种类的数量；s代表油罐，s＝1,2…,r，r为油罐的数量；Among them, q is the number of oil pipelines, j represents the type of oil, j = 1, 2..., u, u represents the number of oil types; s represents the oil tank, s = 1, 2..., r, r is the oil tank quantity;
Figure PCTCN2022100941-appb-000018
Figure PCTCN2022100941-appb-000018
(4)卸载能力约束条件：在设定计划期内，所卸载的油品的种类需要与目标油罐可容纳的油品种类匹配，且油轮中油品的目标卸载量小于目标油罐的剩余容量。卸载能力约束条件可以表示为：(4) Unloading capacity constraints: During the set planning period, the type of oil to be unloaded needs to match the type of oil that can be accommodated in the target tank, and the target unloading volume of oil in the tanker is less than the remaining capacity of the target tank. . The offloading capability constraints can be expressed as:
Figure PCTCN2022100941-appb-000019
Figure PCTCN2022100941-appb-000019
其中F s表示油罐s的剩余存储容量； where F s represents the remaining storage capacity of oil tank s;
Figure PCTCN2022100941-appb-000020
E i,j表示油轮i卸载油品j的吨数，油轮i卸载油品j的吨数为已知数，可以从油轮方调用得到；油罐s的剩余存储容量可以实时检测得到，例如利用雷达液位传感器检测油罐s的实时液位。
Figure PCTCN2022100941-appb-000020
E i,j represents the tonnage of oil product j unloaded by tanker i. The tonnage of oil product j unloaded by tanker i is a known number and can be obtained from the tanker; the remaining storage capacity of oil tank s can be detected in real time, for example, using The radar level sensor detects the real-time level of the oil tank s.
(5)卸载目标约束条件：油轮中的油品需要全部卸载完。卸载目标约束条件可以表示为：(5) Unloading target constraints: All oil products in the tanker need to be unloaded. The offloading target constraints can be expressed as:
Figure PCTCN2022100941-appb-000021
Figure PCTCN2022100941-appb-000021
其中，E i,j,p表示通过输油管道p输送的油轮i的油品j的吨数。 Among them, E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p.
下层调度模型在满足油轮卸载作业要求的前提下得到的可行解，即油轮停靠泊位，以及确定的油品存储油罐、油品输送管道、管道输油量，可以确保卸载作业有效实施，同时可以确保泊位占用时长和输油管道的输送时长均保持在均衡状态，整个油港的作业有效且稳定，处于相对平衡和相对稳定的状态，在下层调度模型的基础上，进一步构建上层调度模型。The feasible solution obtained by the lower-level scheduling model on the premise of meeting the requirements of the tanker unloading operation, that is, the tanker berth, as well as the determined oil storage tank, oil transportation pipeline, and pipeline oil transportation volume, can ensure the effective implementation of the unloading operation, and at the same time To ensure that the berth occupancy time and the transportation time of the oil pipeline are maintained in a balanced state, the operations of the entire oil port are effective and stable, and are in a relatively balanced and stable state. Based on the lower-level scheduling model, further build an upper-level scheduling model.
本发明所提供的油港资源优化调度方法进一步包括以下步骤：The oil port resource optimization dispatching method provided by the present invention further includes the following steps:
步骤S12：建立上层调度模型；上层调度模型的控制目标包括：油港码头利润最大以及油轮总在港时间最小。Step S12: Establish an upper-level dispatch model; the control objectives of the upper-level dispatch model include: maximizing the profit of the oil port terminal and minimizing the total time of the oil tanker in port.
上层调度模型可以通过下式表示：The upper-layer scheduling model can be expressed by the following formula:
maxF 1＝∑ i∑ ju i,jE i,j-∑ i∑ j∑ py i,kw i,j,pL pPC j,pE i,j,p-∑ iDC i×max{((TL i,k-TD i)),0}； maxF 1 ＝∑ i ∑ j u i,j E i,j -∑ i ∑ j ∑ p y i,k w i,j,p L p PC j,p E i,j,p -∑ i DC i × max{((TL i,k -TD i )),0};
minF 2＝∑ k(TL i,k-TA i)； minF 2 =∑ k (TL i,k -TA i );
其中：in:
u i,j表示油轮i卸载油品j所支付的费用(吨单价)，u i,j为设定值，是可调用的常数； u i,j represents the fee (unit price per ton) paid by tanker i to unload oil product j, u i,j is the set value and is a callable constant;
E i,j表示油轮i卸载油品j的吨数； E i,j represents the tonnage of oil product j unloaded by tanker i;
Figure PCTCN2022100941-appb-000022
Figure PCTCN2022100941-appb-000022
Figure PCTCN2022100941-appb-000023
Figure PCTCN2022100941-appb-000023
L p表示输油管道p的长度，L p为设定值，是可调用的常数； L p represents the length of the oil pipeline p, L p is the set value and is a callable constant;
PC j,p表示输油管道p输送油品j的单位成本； PC j,p represents the unit cost of oil pipeline p transporting oil product j;
E i,j,p表示通过输油管道p输送的油轮i的油品j的吨数； E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p;
DC i表示油轮i滞留期惩罚成本(时间为单位)，DC i为设定值，是可调用的常数； DC i represents the penalty cost of the detention period of tanker i (unit is time), DC i is the set value and is a callable constant;
TL i,k表示油轮i在泊位k上作业完毕后的离港时间； TL i,k represents the departure time of tanker i after completing operations at berth k;
TD i表示油轮i的允许最晚离港时间； TD i represents the latest allowed departure time of tanker i;
TA i表示油轮i的进港时间，可以为油轮i办理好进港手续允许进港的时间。 TA i represents the entry time of tanker i. It can be the time when tanker i has completed the entry procedures and is allowed to enter the port.
上层调度模型得到的可行解，即油轮靠泊顺序，可以确保油港码头利润最大以及油轮总在港时间最小。The feasible solution obtained by the upper-level scheduling model, that is, the berthing sequence of oil tankers, can ensure the maximum profit of the oil port terminal and the minimum total time of oil tankers in port.
考虑到油港卸载作业的特点，上层调度模型还包括以下约束条件：Taking into account the characteristics of oil port unloading operations, the upper-level scheduling model also includes the following constraints:
(1)输油管道约束条件：为实现卸油作业，目标泊位和油罐之间设置有至少一条输油管道。输油管道约束条件可以表示为：(1) Oil pipeline constraints: In order to realize oil unloading operations, at least one oil pipeline is set up between the target berth and the oil tank. The constraints of the oil pipeline can be expressed as:
Figure PCTCN2022100941-appb-000024
Figure PCTCN2022100941-appb-000024
其中：in:
k代表泊位，k＝1,2…,m,m代表油港泊位的数量；k represents the berth, k=1,2...,m, m represents the number of oil port berths;
p代表输油管道，p＝1,2…,q,q代表输油管道的数量；p represents the oil pipeline, p=1,2...,q,q represents the number of oil pipelines;
j代表油品种类，j＝1,2…,u,u代表油品种类的数量；j represents the oil type, j=1,2...,u, u represents the number of oil types;
Figure PCTCN2022100941-appb-000025
Figure PCTCN2022100941-appb-000025
(2)泊位准入允许约束条件：一个泊位只能停靠一艘油轮。泊位准入允许条件可以表示为：(2) Berth access permission constraints: Only one oil tanker can dock at one berth. The berth access permission conditions can be expressed as:
TB i+1,k>TL i,k；
Figure PCTCN2022100941-appb-000026
TB i+1,k >TL i,k ;
Figure PCTCN2022100941-appb-000026
其中，TB i+1,k表示第i+1个油轮泊入泊位k的时间，TL i,k表示油轮i在泊位k上作业完毕后的离港时间，即第i+1个油轮泊入泊位k的时间晚于油轮i在泊位k上作业完毕后的离港时间。 Among them, TB i+1,k represents the time when the i+1th oil tanker berths into berth k, and TL i,k represents the departure time after oil tanker i completes its operation at berth k, that is, when the i+1th oil tanker berths. The time at berth k is later than the departure time of tanker i after completing operations at berth k.
(3)卸载完毕离港约束条件：油轮完成卸载作业之后准许离港，单次卸载作业结束。卸载完毕离港约束条件可以表示为：(3) Departure constraints after unloading: The tanker is allowed to leave the port after completing the unloading operation, and the single unloading operation ends. The departure constraint after unloading can be expressed as:
Figure PCTCN2022100941-appb-000027
Figure PCTCN2022100941-appb-000027
TL i,k表示油轮i在泊位k上作业完毕后的离港时间，TB i,k表示第i个油轮泊入泊位k的时间，max{TR i,j,p+TZ i,j,p|p}代表输油管道输送油轮i的第j种油品的准备时长和输送时长之和的最大值； TL i,k represents the departure time of tanker i after completing its operation at berth k, TB i,k represents the time for the i-th oil tanker to berth at berth k, max{TR i,j,p +TZ i,j,p |p} represents the maximum value of the sum of the preparation time and delivery time of the jth oil product transported by oil tanker i through the oil pipeline;
其中，通过输油管道p输送油轮i的第j种油品的准备时长TR i,j,p由下式计算： Among them, the preparation time TR i,j,p of the jth oil product transported by the oil tanker i through the oil pipeline p is calculated by the following formula:
TR i,j,p＝w i,j,pTR p； TR i,j,p = w i,j,p TR p ;
其中，TR p表示输送管道p的输送准备时间，包括但不限于设置于输送管道p上的泵、阀等流体控制部件的启动准备时间，TR p可以根据检测的历史数据生成并预先存储在存储单元中以供随时调用； Among them, TR p represents the transportation preparation time of the transportation pipeline p, including but not limited to the startup preparation time of fluid control components such as pumps and valves installed on the transportation pipeline p. TR p can be generated based on the historical data of the detection and stored in the storage in advance. unit for call at any time;
通过输油管道p输送油轮i的第j种油品的输送时长由下式计算：The transportation time of the jth oil product transported by oil tanker i through oil pipeline p is calculated by the following formula:
Figure PCTCN2022100941-appb-000028
Figure PCTCN2022100941-appb-000028
其中，E i,j,p表示通过输油管道p输送的油轮i的油品j的吨数；v j,p表示输油管道p中输送的第j种油品的流量； Among them, E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p; v j,p represents the flow rate of the jth oil product transported in oil pipeline p;
在以上两式中：In the above two equations:
w i,j,p满足： w i,j,p satisfies:
Figure PCTCN2022100941-appb-000029
即由输油管道p将油轮i的第j种油品输送到油罐中时，w i,j,p为1；否则，则w i,j,p为0。
Figure PCTCN2022100941-appb-000029
That is, when the jth oil product from tanker i is transported to the oil tank by the oil pipeline p, w i,j,p is 1; otherwise, w i,j,p is 0.
步骤S13：采用多目标进化算法求解下层调度模型和上层调度模型。上述上层调度模型和下层调度模型可以由软件求解，例如Matlab等。Step S13: Use a multi-objective evolutionary algorithm to solve the lower-layer scheduling model and the upper-layer scheduling model. The above-mentioned upper-layer scheduling model and lower-layer scheduling model can be solved by software, such as Matlab, etc.
步骤S14:根据求解结果确定油轮的目标泊位和靠泊顺序。Step S14: Determine the target berth and berthing sequence of the tanker based on the solution results.
在优选的实施方式中，采用多目标进化算法求解下层调度模型。In a preferred implementation, a multi-objective evolutionary algorithm is used to solve the lower-layer scheduling model.
可选的，采用多目标进化算法求解下层调度模型包括以下步骤：Optionally, using a multi-objective evolutionary algorithm to solve the lower-level scheduling model includes the following steps:
步骤S21:个体编码，即将下层调度模型可行解对应的泊位编号采用二进制表示。Step S21: Individual coding, that is, the berth number corresponding to the feasible solution of the lower-layer scheduling model is expressed in binary.
在本实施方式中，下层调度模型可行解(也称个体)所对应的油轮停靠泊位具有唯一的泊位编号，将泊位编号用二进制来表示。用二进制表示的泊位编号构成个体的基因型(即可行解的基因型)。基因型和表现型(泊位编号)可以通过编码和解码程序相互转换。In this implementation, the tanker berth corresponding to the feasible solution (also called an individual) of the lower-level dispatch model has a unique berth number, and the berth number is expressed in binary. The berth number expressed in binary constitutes the genotype of the individual (i.e., the genotype of the feasible solution). Genotype and phenotype (berth number) can be converted into each other through encoding and decoding procedures.
步骤S22：产生初始种群Step S22: Generate initial population
设定初始种群群体规模并在下层调度模型可行解中随机选择初始种群中的个体，随机选择可以由随机算法实现，初始种群定义为pop1。Set the initial population size and randomly select individuals in the initial population from the feasible solution of the lower scheduling model. Random selection can be implemented by a random algorithm, and the initial population is defined as pop1.
步骤S23：适应度计算：计算样本个体的适应度。Step S23: Fitness calculation: Calculate the fitness of the sample individual.
由于下层调度模型中各目标不满足线性独立，优选采用逼近于理想解的排序法(Technique for Order Preference by Similarity to an Ideal Solution,TOPSIS)建立适应度函数。Since each goal in the lower-layer scheduling model does not satisfy linear independence, it is preferred to use the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to establish the fitness function.
适应度函数表示为：The fitness function is expressed as:
Figure PCTCN2022100941-appb-000030
Figure PCTCN2022100941-appb-000030
其中，g l表示下层调度模型初始种群第l个个体的适应度值，g l越小说明个体越优；
Figure PCTCN2022100941-appb-000031
表示下层调度模型初始种群第l个个体的目标函数值到下层调度模型负理想解的距离；
Figure PCTCN2022100941-appb-000032
表示下层调度模型初始种群第l个个体的目标函数值到下层调度模型正理想解的距离。目标函数可选的设置为下层调度模型的 模型函数。 Among them, g l represents the fitness value of the l-th individual in the initial population of the lower-layer scheduling model. The smaller g l means the better the individual;
Figure PCTCN2022100941-appb-000031
Represents the distance from the objective function value of the l-th individual of the initial population of the lower-level scheduling model to the negative ideal solution of the lower-level scheduling model;
Figure PCTCN2022100941-appb-000032
Indicates the distance from the objective function value of the l-th individual of the initial population of the lower-level scheduling model to the positive ideal solution of the lower-level scheduling model. The objective function is optionally set to the model function of the underlying scheduling model.
步骤S24：选择运算；根据样本个体适应度进行选择运算，选择个体样本。Step S24: Selection operation; perform selection operation according to the individual fitness of the sample to select individual samples.
计算出初始种群中所有个体的适应度总和；计算出初始种群中每个个体的相对适应度，相对适应度为个体适应度与适应度总和的比值；每个相对适应度可以由概率表示，由于所有概率表示之和为1，可以看作全部相对适应度对应的概率值所对应的区域可以组成一个完整区域(例如一个圆盘)。将选择标识随机地置于这个完整区域内，所选中的一个独立区域即代表所选中的个体。即类似转动圆盘，从种群中选出一个个体。从初始种群中选出目标数量的个体即旋转目标次圆盘，即标准的轮盘赌(Roulette wheel)方式。Calculate the sum of fitness of all individuals in the initial population; calculate the relative fitness of each individual in the initial population. The relative fitness is the ratio of the individual fitness to the sum of fitness; each relative fitness can be expressed by probability, because The sum of all probability representations is 1, which can be regarded as the area corresponding to the probability values corresponding to all relative fitnesses can form a complete area (such as a disk). The selection mark is randomly placed within this complete area, and an independent area selected represents the selected individual. That is, it is similar to turning a disk to select an individual from the population. Select the target number of individuals from the initial population and rotate the target sub-disk, which is the standard roulette wheel method.
当在步骤中个体的适应度已经被归一化，也可以直接作为轮盘赌的概率。When the individual fitness has been normalized in the step, it can also be directly used as the probability of roulette.
步骤S25：交叉运算；将选择运算后的个体样本进行交叉运算。Step S25: Crossover operation; perform crossover operation on the selected individual samples.
从选择后的个体样本中随机选择两个个体，以一定的交叉概率进行交叉：将随机位置的对应基因按系数进行线性插值，其余位置保持不变，从而得到两个新的个体。Randomly select two individuals from the selected individual sample and perform crossover with a certain crossover probability: linearly interpolate the corresponding genes at random positions according to the coefficients, and keep the remaining positions unchanged, thus obtaining two new individuals.
根据参与交叉操作的两个个体的适应度调整交叉概率。首先设定交叉概率区间[pc min，pc max]，然后计算种群个体适应度、平均适应度f avg、最小适应度f min，交叉概率由下式确定: The crossover probability is adjusted according to the fitness of the two individuals participating in the crossover operation. First, set the crossover probability interval [pc min , pc max ], and then calculate the individual fitness, average fitness f avg , and minimum fitness f min of the population. The crossover probability is determined by the following formula:
Figure PCTCN2022100941-appb-000033
Figure PCTCN2022100941-appb-000033
其中，f'为参与交叉操作的两个个体中适应度较大者。Among them, f' is the one with greater fitness among the two individuals participating in the crossover operation.
步骤S26：变异运算；将交叉运算后的个体样本进行变异运算。Step S26: Mutation operation; perform mutation operation on the individual samples after the crossover operation.
按照变异概率对交叉后的个体样本中的一个或多个变异点的数值取反。The value of one or more mutation points in the crossed individual sample is inverted according to the mutation probability.
首先设定变异概率区间[pm min，pm max]，根据个体适应度调整变异概率，变异概率由下式确定： First, set the mutation probability interval [pm min , pm max ], and adjust the mutation probability according to individual fitness. The mutation probability is determined by the following formula:
Figure PCTCN2022100941-appb-000034
Figure PCTCN2022100941-appb-000034
其中，pm表示自适应变异概率；pm min表示变异概率最小值；pm max表示变异概率最大值；f min表示交叉后的个体样本种群中适应度最小值；f avg表示交叉后个体样本种群中适应度平均值；f表示变异个体的适应度值。 Among them, pm represents the adaptive mutation probability; pm min represents the minimum mutation probability; pm max represents the maximum mutation probability; f min represents the minimum fitness value in the individual sample population after crossover; f avg represents the adaptation in the individual sample population after crossover Average degree; f represents the fitness value of the mutated individual.
步骤S27：判断是否满足迭代条件：如果满足迭代条件，则退出计算，得到所下层调度模型的最优种群pop1-opt；如果不满足迭代条件，则将变异运算后的个体样本作为下一代种群，循环执行步骤S23至步骤S26，直至满足迭代条件。Step S27: Determine whether the iteration conditions are met: if the iteration conditions are met, exit the calculation and obtain the optimal population pop1-opt of the underlying scheduling model; if the iteration conditions are not met, the individual samples after the mutation operation are used as the next generation population. Step S23 to step S26 are executed in a loop until the iteration condition is met.
具体来说，经过选择计算、交叉运算和变异运算后，即得到下一代的种群。进一步循环执行选择计算、交叉运算和变异运算的过程，直至迭代次数达到上限(满足迭代条件)，退出计算，得到下层调度模型适应度最大时对应的最优种群pop1-opt。对最优种群pop1-opt中的个体基因型进行解码，即得到油港 停靠泊位，进而确定油品存储油罐、油品输送管道、管道输油量。Specifically, after selection calculation, crossover operation and mutation operation, the next generation population is obtained. The process of selection calculation, crossover operation and mutation operation is further cyclically executed until the number of iterations reaches the upper limit (the iteration conditions are met), the calculation is exited, and the optimal population pop1-opt corresponding to the maximum fitness of the lower scheduling model is obtained. By decoding the individual genotypes in the optimal population pop1-opt, the oil port berth is obtained, and then the oil storage tank, oil transportation pipeline, and pipeline oil volume are determined.
步骤S28：对应计算与最优种群pop1-out中每一个个体所对应的E i,j,p，E i,j,p表示通过输油管道p输送的油轮i的油品j的吨数。 Step S28: Calculate E i,j,p corresponding to each individual in the optimal population pop1-out. E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p.
步骤S29:将计算出的E i,j,p代入上层调度模型。 Step S29: Substitute the calculated E i,j,p into the upper-layer scheduling model.
采用多目标进化算法对代入E i,j,p的上层调度模型求解。 A multi-objective evolutionary algorithm is used to solve the upper-layer scheduling model substituted into E i,j,p .
可选的，采用多目标进化算法求解代入E i,j,p上层调度模型包括以下步骤： Optionally, using a multi-objective evolutionary algorithm to solve the upper-layer scheduling model substituted into E i,j,p includes the following steps:
步骤S30:个体编码,即将上层调度模型可行解对应的泊位编号采用二进制表示。Step S30: Individual coding, that is, the berth number corresponding to the feasible solution of the upper-layer scheduling model is expressed in binary.
在本实施方式中，上层调度模型可行解(也称个体)所对应的油轮靠泊顺序具有唯一的顺序编号，将靠泊顺序用二进制来表示。用二进制表示的靠泊顺序编号构成个体的基因型(即可行解的基因型)。基因型和表现型(靠泊顺序编号)可以通过编码和解码程序相互转换。In this implementation, the tanker berthing sequence corresponding to the feasible solution (also called an individual) of the upper-level dispatch model has a unique sequence number, and the berthing sequence is expressed in binary. The genotype of the individual (that is, the genotype of the feasible solution) is composed of the docking sequence number represented in binary. Genotypes and phenotypes (docking sequence numbers) can be converted into each other through encoding and decoding procedures.
步骤S31：产生初始种群。Step S31: Generate an initial population.
设定初始种群规模并在上层调度模型可行解中随机选择初始种群中的个体，随机选择可以由随机算法实现，初始种群定义为pop2。Set the initial population size and randomly select individuals in the initial population from the feasible solution of the upper-level scheduling model. Random selection can be implemented by a random algorithm, and the initial population is defined as pop2.
步骤S32：适应度计算:计算样本个体的适应度。Step S32: Fitness calculation: Calculate the fitness of the sample individual.
由于上层调度模型中各目标不满足线性独立，优选采用逼近于理想解的排序法(Technique for Order Preference by Similarity to an Ideal Solution,TOPSIS)建立适应度函数。Since each goal in the upper-layer scheduling model does not satisfy linear independence, it is preferred to use the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to establish the fitness function.
适应度函数表示为：The fitness function is expressed as:
Figure PCTCN2022100941-appb-000035
Figure PCTCN2022100941-appb-000035
其中，g l表示上层调度模型初始种群第l个个体的适应度值，g l越小说明个体越优；
Figure PCTCN2022100941-appb-000036
表示上层调度模型初始种群第l个个体的目标函数值到上层调度模型负理想解的距离；
Figure PCTCN2022100941-appb-000037
表示上层调度模型初始种群第l个个体的目标函数值到上层调度模型正理想解的距离。目标函数可选地设置为上层调度模型的模型函数。对于取最大值的函数，可以通过倒数的形式转换为取最小值，并可在分母上加1避免函数溢出。 Among them, g l represents the fitness value of the l-th individual in the initial population of the upper-level scheduling model. The smaller g l is, the better the individual is;
Figure PCTCN2022100941-appb-000036
Represents the distance from the objective function value of the l-th individual of the initial population of the upper-level scheduling model to the negative ideal solution of the upper-level scheduling model;
Figure PCTCN2022100941-appb-000037
Indicates the distance from the objective function value of the l-th individual in the initial population of the upper-level scheduling model to the positive ideal solution of the upper-level scheduling model. The objective function is optionally set to the model function of the upper-layer scheduling model. For the function that takes the maximum value, it can be converted into the minimum value by using the reciprocal form, and 1 can be added to the denominator to avoid function overflow.
步骤S33：选择运算；根据样本个体适应度进行选择运算，选择个体样本。Step S33: Selection operation; perform selection operation according to the individual fitness of the sample to select individual samples.
选择运算优选采用标准的轮盘赌(Roulette wheel)方式。The selection operation preferably adopts the standard roulette wheel method.
当在步骤中个体的适应度已经被归一化，也可以直接作为轮盘赌的概率。When the individual fitness has been normalized in the step, it can also be directly used as the probability of roulette.
步骤S34：交叉运算；将选择运算后的个体样本进行交叉运算。Step S34: Crossover operation; perform crossover operation on the selected individual samples.
从选择后的个体样本中随机选择两个个体，以一定的交叉概率进行交叉：将随机位置的对应基因按系数进行线性插值，其余位置保持不变，从而得到两个新的个体。Randomly select two individuals from the selected individual sample and perform crossover with a certain crossover probability: linearly interpolate the corresponding genes at random positions according to the coefficients, and keep the remaining positions unchanged, thus obtaining two new individuals.
根据参与交叉操作的两个个体的适应度调整交叉概率。首先设定交叉概率区间[pc min，pc max]，然后计算种群个体适应度、平均适应度f avg、最小适应度f min，交叉概率由下式确定: The crossover probability is adjusted according to the fitness of the two individuals participating in the crossover operation. First, set the crossover probability interval [pc min , pc max ], and then calculate the individual fitness, average fitness f avg , and minimum fitness f min of the population. The crossover probability is determined by the following formula:
Figure PCTCN2022100941-appb-000038
Figure PCTCN2022100941-appb-000038
其中，f'为参与交叉操作的两个个体中适应度较大者。Among them, f' is the one with greater fitness among the two individuals participating in the crossover operation.
步骤S35：变异运算；将交叉运算后的个体样本进行变异运算。Step S35: Mutation operation; perform mutation operation on the individual samples after the crossover operation.
按照变异概率对交叉后的个体样本中的一个或多个变异点的数值取反。The value of one or more mutation points in the crossed individual sample is inverted according to the mutation probability.
首先设定变异概率区间[pm min，pm max]，根据个体适应度调整变异概率，变异概率由下式确定： First, set the mutation probability interval [pm min , pm max ], and adjust the mutation probability according to individual fitness. The mutation probability is determined by the following formula:
Figure PCTCN2022100941-appb-000039
Figure PCTCN2022100941-appb-000039
其中，pm表示自适应变异概率；pm min表示变异概率最小值；pm max表示变异概率最大值；f min表示交叉后的个体样本种群中适应度最小值；f avg表示交叉后个体样本种群中适应度平均值；f表示变异个体的适应度值。 Among them, pm represents the adaptive mutation probability; pm min represents the minimum mutation probability; pm max represents the maximum mutation probability; f min represents the minimum fitness value in the individual sample population after crossover; f avg represents the adaptation in the individual sample population after crossover Average degree; f represents the fitness value of the mutated individual.
步骤S36：判断是否满足迭代条件：如果满足迭代条件，则退出计算，得到上层调度模型的最优种群pop1-opt；如果不满足迭代条件，则将变异运算后的个体样本作为下一代种群，循环执行步骤S32至步骤S35，直至满足迭代条件。Step S36: Determine whether the iteration conditions are met: if the iteration conditions are met, exit the calculation and obtain the optimal population pop1-opt of the upper-layer scheduling model; if the iteration conditions are not met, use the individual samples after the mutation operation as the next generation population, and loop Steps S32 to S35 are executed until the iteration conditions are met.
对最优种群pop2-opt中的个体基因型进行解码，即得到油港靠泊顺序。Decode the individual genotypes in the optimal population pop2-opt to obtain the oil port berthing sequence.
通过两次遗传算法的求解，且上层调度模型的最优解基于下层调度模型的最优解获得，最终得到的油港停靠泊位、油品存储油罐、油品输送管道、管道输油量、油港靠泊顺序，一方面可以确保在多储罐、多输油管道的因素条件下，整个油港的运行相对平衡且相对稳定，另一方面可以达到油港码头利润最大且油轮总在港时间最小的控制目标，是最为优化的调度方案，相较于传统的人工编制方法，智能性和合理性均明显提高。Through two genetic algorithm solutions, and the optimal solution of the upper-layer dispatch model is obtained based on the optimal solution of the lower-layer dispatch model, the final obtained oil port berths, oil storage tanks, oil transportation pipelines, pipeline oil transportation volume, The berthing sequence of the oil port can, on the one hand, ensure that the operation of the entire oil port is relatively balanced and stable under the factors of multiple storage tanks and multiple oil pipelines. On the other hand, it can achieve the maximum profit of the oil port terminal and the total time of the oil tanker in port. The smallest control target is the most optimized scheduling plan. Compared with the traditional manual programming method, the intelligence and rationality are significantly improved.
以上实施例仅用以说明本发明的技术方案，而非对其进行限制；尽管参照前述实施例对本发明进行了详细的说明，对于本领域的普通技术人员来说，依然可以对前述实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或替换，并不使相应技术方案的本质脱离本发明所要求保护的技术方案的精神和范围。The above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art can still make modifications to the foregoing embodiments. Modifications are made to the recorded technical solutions, or equivalent substitutions are made to some of the technical features; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions claimed by the present invention.

Claims (7)

  1. 一种油港资源优化调度方法，其特征在于，包括以下步骤：An oil port resource optimization dispatching method is characterized by including the following steps:
    步骤S11：建立下层调度模型，所述下层调度模型为：Step S11: Establish a lower-layer scheduling model. The lower-layer scheduling model is:
    Figure PCTCN2022100941-appb-100001
    Figure PCTCN2022100941-appb-100001
    min G 2＝max{TR i,j,p+TZ i,j,p|p}-min{TR i,j,p+TZ i,j,p|p}； min G 2 =max{TR i,j,p +TZ i,j,p |p}-min{TR i,j,p +TZ i,j,p |p};
    其中：in:
    k代表泊位，k＝1,2…,m,m代表油港泊位的数量；k represents the berth, k=1,2...,m, m represents the number of oil port berths;
    T k代表设定计划期内泊位k的使用时长： T k represents the usage time of berth k during the set planning period:
    Figure PCTCN2022100941-appb-100002
    Figure PCTCN2022100941-appb-100002
    Figure PCTCN2022100941-appb-100003
    代表设定计划期内所有m个泊位的平均使用时长：
    Figure PCTCN2022100941-appb-100003
    Represents the average usage time of all m berths during the set planning period:
    Figure PCTCN2022100941-appb-100004
    Figure PCTCN2022100941-appb-100004
    TR i,j,p表示通过输油管道p输送油轮i的第j种油品的准备时长；p代表输油管道，p＝1,2…,q,q代表输油管道的数量；j代表油品种类，j＝1,2…,u,u代表油品种类的数量；i代表油轮，i＝1,2…,n，n代表油轮的数量；TR i,j,p＝w i,j,pTR p，TR p表示输送管道p的输送准备时间，w i,j,p满足： TR i,j,p represents the preparation time for the jth oil product transported by tanker i through oil pipeline p; p represents the oil pipeline, p=1,2...,q,q represents the number of oil pipelines; j represents the type of oil , j=1,2...,u,u represents the number of oil types; i represents the oil tanker, i=1,2...,n, n represents the number of oil tankers; TR i,j,p =w i,j,p TR p , TR p represents the transportation preparation time of the transportation pipeline p, w i, j, p satisfy:
    Figure PCTCN2022100941-appb-100005
    Figure PCTCN2022100941-appb-100005
    TZ i,j,p表示通过输油管道p输送油轮i的第j种油品的输送时长： TZ i,j,p represents the transportation time of the jth oil product transported by oil tanker i through oil pipeline p:
    Figure PCTCN2022100941-appb-100006
    其中，E i,j,p表示通过输油管道p输送的油轮i的油品j的吨数；v j,p表示输油管道p中输送的第j种油品的流量；
    Figure PCTCN2022100941-appb-100006
    Among them, E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p; v j,p represents the flow rate of the jth oil product transported in oil pipeline p;
    TR i,j,p+TZ i,j,p|p表示满足由输油管道p输送油轮i的第j种油品的条件时，准备时长和输送时长之和为TR i,j,p+TZ i,j,p； TR i,j,p +TZ i,j,p |p means that when the conditions for the jth oil product to be transported by oil pipeline p to tanker i are met, the sum of the preparation time and the transportation time is TR i,j,p +TZ i,j,p ;
    max{TR i,j,p+TZ i,j,p|p}代表输油管道输送油轮i的第j种油品的准备时长和输送时长之和的最大值； max{TR i,j,p +TZ i,j,p |p} represents the maximum value of the sum of the preparation time and the transportation time of the jth oil product transported by the oil pipeline to the tanker i;
    min{TR i,j,p+TZ i,j,p|p}代表输油管道输送油轮i的第j种油品的准备时长和输送时长之和的最小值； min{TR i,j,p +TZ i,j,p |p} represents the minimum value of the sum of the preparation time and the delivery time of the jth oil product transported by the oil pipeline to the tanker i;
    步骤S12：建立上层调度模型，所述上层调度模型为：Step S12: Establish an upper-layer scheduling model. The upper-layer scheduling model is:
    maxF 1＝∑ i∑ ju i,jE i,j-∑ i∑ j∑ py i,kw i,j,pL pPC j,pE i,j,p-∑ iDC i×max{((TL i,k-TD i)),0}； maxF 1 ＝∑ i ∑ j u i,j E i,j -∑ i ∑ j ∑ p y i,k w i,j,p L p PC j,p E i,j,p -∑ i DC i × max{((TL i,k -TD i )),0};
    minF 2＝∑ k(TL i,k-TA i)； minF 2 =∑ k (TL i,k -TA i );
    其中，u i,j表示油轮i卸载油品j所支付的每吨费用； Among them, u i,j represents the cost per ton paid by tanker i to unload oil product j;
    E i,j表示油轮i卸载油品j的吨数； E i,j represents the tonnage of oil product j unloaded by tanker i;
    Figure PCTCN2022100941-appb-100007
    Figure PCTCN2022100941-appb-100007
    Figure PCTCN2022100941-appb-100008
    Figure PCTCN2022100941-appb-100008
    L p表示输油管道p的长度； L p represents the length of the oil pipeline p;
    PC j,p表示输油管道p输送油品j的单位成本； PC j,p represents the unit cost of oil pipeline p transporting oil product j;
    E i,j,p表示通过输油管道p输送的油轮i的油品j的吨数； E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p;
    DC i表示油轮i滞留期惩罚单位成本； DC i represents the penalty unit cost of the detention period of tanker i;
    TL i,k表示油轮i在泊位k上作业完毕后的离港时间； TL i,k represents the departure time of tanker i after completing operations at berth k;
    TD i表示油轮i的允许最晚离港时间； TD i represents the latest allowed departure time of tanker i;
    TA i表示油轮i的进港时间； TA i represents the entry time of tanker i;
    步骤S13:采用多目标进化算法求解下层调度模型和上层调度模型；Step S13: Use a multi-objective evolutionary algorithm to solve the lower-layer scheduling model and the upper-layer scheduling model;
    步骤S14：根据求解结果确定油轮的目标泊位和靠泊顺序。Step S14: Determine the target berth and berthing sequence of the tanker based on the solution results.
  2. 根据权利要求1所述的油港资源优化调度方法，其特征在于，所述下层调度模型还包括以下约束条件：The oil port resource optimization dispatching method according to claim 1, characterized in that the lower-layer dispatching model also includes the following constraints:
    油轮靠泊约束条件：
    Figure PCTCN2022100941-appb-100009
    Tanker berthing constraints:
    Figure PCTCN2022100941-appb-100009
    油轮载重量约束条件：y i,k(W i-W k)≤0,i＝1,2,...,n；k＝1,2,...,m，W i表示油轮i的载重量；W k表示泊位k的靠泊能力； Tanker load capacity constraint: y i,k (W i -W k ) ≤ 0, i = 1, 2,..., n; k = 1, 2,..., m, W i represents the load of tanker i Load capacity; W k represents the berthing capacity of berth k;
    输油管道约束条件：
    Figure PCTCN2022100941-appb-100010
    s代表油罐，s＝1,2…,r，r为油罐的数量；     Oil pipeline constraints:
    Figure PCTCN2022100941-appb-100010
    s represents the oil tank, s=1,2...,r, r is the number of oil tanks;
    Figure PCTCN2022100941-appb-100011
    Figure PCTCN2022100941-appb-100011
    卸载能力约束条件：
    Figure PCTCN2022100941-appb-100012
    F s表示油罐s的剩余存储容量；
    Figure PCTCN2022100941-appb-100013
    E i,j表示油轮i卸载油品j的吨数；和     Unloading capability constraints:
    Figure PCTCN2022100941-appb-100012
    F s represents the remaining storage capacity of oil tank s;
    Figure PCTCN2022100941-appb-100013
    E i,j represents the tonnage of oil product j unloaded by tanker i; and
    卸载目标约束条件：
    Figure PCTCN2022100941-appb-100014
    Uninstall target constraints:
    Figure PCTCN2022100941-appb-100014
  3. 根据权利要求1或2所述的油港资源优化调度方法，其特征在于，所述下层调度模型还包括以下约束条件：The oil port resource optimization dispatching method according to claim 1 or 2, characterized in that the lower-layer dispatching model also includes the following constraints:
    输油管道约束条件：
    Figure PCTCN2022100941-appb-100015
    其中
    Figure PCTCN2022100941-appb-100016
    Figure PCTCN2022100941-appb-100017
    Oil pipeline constraints:
    Figure PCTCN2022100941-appb-100015
    in
    Figure PCTCN2022100941-appb-100016
    Figure PCTCN2022100941-appb-100017
    泊位准入允许约束条件：
    Figure PCTCN2022100941-appb-100018
    Berth access permission constraints:
    Figure PCTCN2022100941-appb-100018
    其中，TB i+1,k表示第i+1个油轮泊入泊位k的时间，TL i,k表示油轮i在泊位k上作业完毕后的离港时间；和 Among them, TB i+1,k represents the time when the i+1th oil tanker berths into berth k, and TL i,k represents the departure time of oil tanker i after completing its operation at berth k; and
    卸载完毕离港约束条件：Departure constraints after unloading:
    TL i,k≥TB i,k+∑ jmax{TR i,j,p+TZ i,j,p|p}； TL i,k ≥TB i,k +∑ j max{TR i,j,p +TZ i,j,p |p};
    TL i,k表示油轮i在泊位k上作业完毕后的离港时间，TB i,k表示第i个油轮泊入泊位k的时间，max{TR i,j,p+TZ i,j,p|p}代表输油管道输送油轮i的第j种油品的准备时长和输送时长之和的最大值。 TL i,k represents the departure time of tanker i after completing its operation at berth k, TB i,k represents the time for the i-th oil tanker to berth at berth k, max{TR i,j,p +TZ i,j,p |p} represents the maximum value of the sum of the preparation time and the delivery time of the jth oil product transported by the oil pipeline to the tanker i.
  4. 根据权利要求1所述的油港资源优化调度方法，其特征在于，采用多目标进化算法求解下层调度模型和上层调度模型包括以下步骤：The oil port resource optimization dispatching method according to claim 1, characterized in that using a multi-objective evolutionary algorithm to solve the lower-level dispatching model and the upper-layer dispatching model includes the following steps:
    步骤S21：将所述下层调度模型可行解对应的泊位编号采用二进制表示；Step S21: Use binary representation of the berth number corresponding to the feasible solution of the lower-layer dispatch model;
    步骤S22：设定初始种群规模并在所述下层调度模型可行解中随机选择初始种群中的个体，定义为初始种群pop1；Step S22: Set the initial population size and randomly select individuals in the initial population from the feasible solutions of the lower-layer scheduling model, defined as the initial population pop1;
    步骤S23：计算样本个体的适应度；Step S23: Calculate the fitness of the sample individual;
    步骤S24：根据样本个体适应度进行选择运算，选择个体样本；Step S24: Perform a selection operation based on the individual fitness of the sample to select individual samples;
    步骤S25：将选择运算后的个体样本进行交叉运算；Step S25: Perform crossover operation on the selected individual samples;
    步骤S26：将交叉运算后的个体样本进行变异运算；Step S26: Perform mutation operation on the individual samples after the crossover operation;
    步骤S27：判断是否满足迭代条件：如果满足迭代条件，则退出计算，得到所述下层调度模型的最优种群pop1-opt；如果不满足迭代条件，则将变异运算后的个体样本作为下一代种群，循环执行步骤S23至步骤S26，直至满足迭代条件；Step S27: Determine whether the iteration conditions are met: if the iteration conditions are met, exit the calculation and obtain the optimal population pop1-opt of the lower-layer scheduling model; if the iteration conditions are not met, use the individual samples after the mutation operation as the next generation population. , execute step S23 to step S26 in a loop until the iteration conditions are met;
    步骤S28：对应计算与最优种群pop1-opt中每一个个体所对应的E i,j,p，E i,j,p表示通过输油管道p输送的油轮i的油品j的吨数； Step S28: Correspondingly calculate E i,j,p corresponding to each individual in the optimal population pop1-opt. E i,j,p represents the tonnage of oil product j transported by oil tanker i through oil pipeline p;
    步骤S29：将计算出的E i,j,p代入上层调度模型； Step S29: Substitute the calculated E i,j,p into the upper-layer scheduling model;
    步骤S30：将所述上层调度模型可行解对应的油轮靠泊顺序采用二进制表示；Step S30: Use binary representation of the tanker berthing sequence corresponding to the feasible solution of the upper-level dispatch model;
    步骤S31：设定初始种群规模并在所述上层调度模型可行解中随机选择初始种群中的个体，定义为初始种群pop2；Step S31: Set the initial population size and randomly select individuals in the initial population from the feasible solution of the upper-layer scheduling model, which is defined as the initial population pop2;
    步骤S32：计算样本个体的适应度；Step S32: Calculate the fitness of the sample individual;
    步骤S33:根据样本个体适应度进行选择运算，选择个体样本；Step S33: Perform a selection operation according to the individual fitness of the sample, and select individual samples;
    步骤S34:将选择运算后的个体样本进行交叉运算；Step S34: Perform crossover operation on the selected individual samples;
    步骤S35:将交叉运算后的个体样本进行变异运算；Step S35: Perform mutation operation on the individual samples after the crossover operation;
    步骤S36：判断是否满足迭代条件：如果满足迭代条件，则退出计算，得到所述上层调度模型的最优 种群pop2-opt；如果不满足迭代条件，则将变异运算后的个体样本作为下一代种群，循环执行步骤S32至步骤S36,直至满足迭代条件；Step S36: Determine whether the iteration conditions are met: if the iteration conditions are met, exit the calculation and obtain the optimal population pop2-opt of the upper-layer scheduling model; if the iteration conditions are not met, use the individual samples after the mutation operation as the next generation population. , execute step S32 to step S36 in a loop until the iteration conditions are met;
    步骤S37：对所述上层调度模型的最优种群pop2-opt解码，得到油轮目标泊位和靠泊顺序。Step S37: Decode the optimal population pop2-opt of the upper-layer scheduling model to obtain the tanker target berth and berthing sequence.
  5. 根据权利要求4所述的油港资源优化调度方法，其特征在于，The oil port resource optimization dispatching method according to claim 4, characterized in that:
    步骤S23或步骤S32中执行适应度计算时的适应度函数为：The fitness function when performing fitness calculation in step S23 or step S32 is:
    Figure PCTCN2022100941-appb-100019
    Figure PCTCN2022100941-appb-100019
    其中，g l表示上层调度模型初始种群第l个个体的适应度值，g l越小说明个体越优；
    Figure PCTCN2022100941-appb-100020
    表示上层调度模型初始种群第l个个体的目标函数值到上层调度模型负理想解的距离；
    Figure PCTCN2022100941-appb-100021
    表示上层调度模型初始种群第l个个体的目标函数值到上层调度模型正理想解的距离；     Among them, g l represents the fitness value of the l-th individual in the initial population of the upper-level scheduling model. The smaller g l is, the better the individual is;
    Figure PCTCN2022100941-appb-100020
    Represents the distance from the objective function value of the l-th individual of the initial population of the upper-level scheduling model to the negative ideal solution of the upper-level scheduling model;
    Figure PCTCN2022100941-appb-100021
    Represents the distance from the objective function value of the l-th individual in the initial population of the upper-level scheduling model to the positive ideal solution of the upper-level scheduling model;
    步骤S23中执行适应度计算时，目标函数为所述下层调度模型；步骤S32中执行适应度计算时，目标函数为所述上层调度模型。When the fitness calculation is performed in step S23, the objective function is the lower-layer scheduling model; when the fitness calculation is performed in step S32, the objective function is the upper-layer scheduling model.
  6. 根据权利要求4所述的油港资源优化调度方法，其特征在于，The oil port resource optimization dispatching method according to claim 4, characterized in that:
    步骤S25或步骤S34中执行交叉运算时，交叉概率由下式确定：When the crossover operation is performed in step S25 or step S34, the crossover probability is determined by the following formula:
    Figure PCTCN2022100941-appb-100022
    Figure PCTCN2022100941-appb-100022
    其中pc为交叉概率，pc min为设定交叉概率区间的最小值，pc max为设定交叉概率区间的最大值，f'为参与交叉操作的两个个体中适应度较大者，f avg为平均适应度。 where pc is the crossover probability, pc min is the minimum value of the set crossover probability interval, pc max is the maximum value of the set crossover probability interval, f' is the one with greater fitness among the two individuals participating in the crossover operation, f avg is average fitness.
  7. 根据权利要求4所述的油港资源优化调度方法，其特征在于，The oil port resource optimization dispatching method according to claim 4, characterized in that:
    步骤S26或步骤S35中执行变异运算时，变异概率由下式确定：When the mutation operation is performed in step S26 or step S35, the mutation probability is determined by the following formula:
    Figure PCTCN2022100941-appb-100023
    Figure PCTCN2022100941-appb-100023
    其中pm为变异概率，pm min表示设定变异概率区间的最小值，pm max表示设定变异概率区间的最大值；f min表示交叉后的个体样本种群中适应度最小值；f avg表示交叉后个体样本种群中适应度平均值；f表示变异个体的适应度值。 where pm is the mutation probability, pm min represents the minimum value of the set mutation probability interval, pm max represents the maximum value of the set mutation probability interval; f min represents the minimum fitness value in the individual sample population after crossover; f avg represents after crossover The average fitness value in the individual sample population; f represents the fitness value of the mutated individual.
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