CN112884383B - Container port emergency material optimizing and transferring method considering time window constraint - Google Patents

Container port emergency material optimizing and transferring method considering time window constraint Download PDF

Info

Publication number
CN112884383B
CN112884383B CN202110420026.1A CN202110420026A CN112884383B CN 112884383 B CN112884383 B CN 112884383B CN 202110420026 A CN202110420026 A CN 202110420026A CN 112884383 B CN112884383 B CN 112884383B
Authority
CN
China
Prior art keywords
time
time window
container
port
card
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110420026.1A
Other languages
Chinese (zh)
Other versions
CN112884383A (en
Inventor
许波桅
汪雨晴
李军军
杨勇生
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Maritime University
Original Assignee
Shanghai Maritime University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Maritime University filed Critical Shanghai Maritime University
Priority to CN202110420026.1A priority Critical patent/CN112884383B/en
Publication of CN112884383A publication Critical patent/CN112884383A/en
Application granted granted Critical
Publication of CN112884383B publication Critical patent/CN112884383B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • G06Q10/06313—Resource planning in a project environment
    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • 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
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • 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
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G06Q50/40—
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Game Theory and Decision Science (AREA)
  • Biophysics (AREA)
  • Health & Medical Sciences (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Educational Administration (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Finance (AREA)
  • Accounting & Taxation (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Physiology (AREA)
  • Genetics & Genomics (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides a container port emergency material optimizing and transferring method considering time window constraint, which comprises the following steps: s1, establishing an objective function with the aim of minimizing the total cost of the container in the port, taking port collecting time, the number of outer collection cards distributed by a gate as a ship, queuing length and average waiting time of the outer collection cards, occupied space and occupied time of the container in a storage yard as constraints, and establishing an upper model to plan a time window for the corresponding outer collection card task of the ship, wherein the time window is a starting and stopping time period of the outer collection card for conveying the container; s2, based on the priority of the ship corresponding to the external set card task, aiming at minimizing the time window adjustment cost, establishing a lower model to adjust the time window; s3, based on an L-CGA algorithm, the adjusted time window is further optimized to be a corresponding real time window, and the external collector card executes tasks based on the real time window of the ship. The invention can effectively reduce the total cost of the container in the port, optimize the arrival mode of the external collecting card and ensure the preferential allocation and transportation of emergency materials.

Description

Container port emergency material optimizing and transferring method considering time window constraint
Technical Field
The invention relates to the field of container port material allocation and transportation, in particular to a container port emergency material optimization and transportation method considering time window constraint.
Background
With the development of economy, container ports are busy in production operation and have high loading and unloading pressure, and particularly, the transportation of emergency materials faces more serious tests. Due to time urgency, the high-efficiency and rapid development of each link of emergency logistics needs to be ensured, and for the situations, the transportation departments actively develop work to ensure the smooth transportation of emergency materials. The heavy logistics task of the port easily causes the interruption of the normal operation of the container port, influences the original planned production operation of the port ship, and needs to take corresponding emergency measures to ensure the smooth transportation of emergency materials. In order to reduce the influence on the life of people in China, the production of industries and the economic development as far as possible, an effective container port emergency material priority allocation and transportation scheme must be constructed, and when the container port has an emergency event and normal production operation is influenced, the container port should carry out emergency allocation on materials, personnel, equipment and the like so as to ensure the normal operation of the container port.
In recent years, the government of China increasingly pays attention to the development of container port emergency logistics and also issues a management method of container port emergency logistics successively, but the emergency capability of the container port of China is still lacking.
The container port carries out the preferential transportation of the emergency materials, and the problem that the port is jammed due to the fact that the outer collection card for transporting the emergency materials is concentrated to the port is solved, and the preferential transportation is carried out on the outer collection card for transporting the emergency materials. At present, research is mainly conducted from two parts of container port congestion and emergency logistics.
1) Congestion-related research of container port
Container port congestion has become a hot spot of concern for current businesses and academia, and many studies have proposed measures to address container port congestion, such as AGV path planning, card reservation systems, congestion charging, and some other solutions.
Yang, Y and the like consider the integrated scheduling of the quay bridge and the AGV, and construct a double-layer planning model. He, J, etc., studied a yard planning problem that takes into account uncertainty and traffic congestion, and built a two-stage stochastic programming model to minimize total transportation distance. However, the constraint problem of the time window is not considered, the problem of the time window exists objectively, the constraint of the time window is not considered, and the time window is an ideal state and does not accord with the actual situation.
Therefore, the scholars at home and abroad have made the following study on the time window problem. Ding, L and the like mainly study the problem of transportation organization coordination considering scale effect under the constraint of a mixed time window in the article, and an electronic platform suitable for information interconnection of multi-type intermodal stations is established by combining a traditional information exchange mode. Ku, D in the study, taking into account the departure time window of the container, a random dynamic programming model was proposed for calculating the minimum expected number of reorganizations of a group of containers with the departure time window. Ng, M replaces the individual limit on the number of deployable vessels by looking at the vessel deployment method of a container port with a time window. Nossack, J, etc. solves the truck scheduling problem that arises in intermodal container transportation where the transported truck must be routed and scheduled under the hard time window constraints imposed by the customer and dock to minimize the overall run time of the truck. Shiri, S, etc. indicate that the intermodal terminal requires a pick-up reservation and that each truck must handle the container at the customer' S location within a specified time window, and as a result, the developed integrated model can find the best solution. The above research uses a time window to solve the problems of port integrated scheduling, transportation scheduling and the like.
Still other scholars further apply time window constraints to solving the problem of port congestion, e.g. Chen, G et al propose a method called "ship dependent time window" to control truck arrival. Ma, M and the like establish a two-stage queuing model describing a gate and yard vehicle queuing process, and establish a reservation system based on a ship related time window. Chen, G et al, in order to solve the problem of congestion at the dock gate, propose a solution for managing truck arrivals with a time window based on truck-ship service relationships. Assadipour, G, etc. consider the specific time window available for each crane to minimize the time that the container spends on the quay.
Although the prior art also uses a time window to solve the problem of the congestion of the container port, only the related constraint of the land-side gate and the storage yard is considered, but the cooperative planning of the land side and the seashore is not comprehensively considered, so as to solve the problems of container pressing, busy production operation, and the congestion of the container port.
2) Research on emergency logistics
Emergency logistics scheduling is becoming more and more important in modern society, and many studies have proposed solutions. P.minas et al have fully investigated the emergency response operation literature in the paper. The theoretical basis that helps to strengthen the action of the emergency response is studied. Bingsheng Liu et al developed an efficiency-based path model to determine the feasible rescue route for the entire transport network, thereby achieving the goal of maximizing overall rescue efficiency. The purpose of the study by Pedram Memari et al is to distribute some temporary emergency stations throughout the area with maximum coverage after a natural disaster has occurred. Qi, c, etc. in order to reduce the loss caused by the earthquake, a post-earthquake emergency material distribution and transportation decision support system based on a geographic information system is designed and realized.
However, most of the existing researches only consider an emergency logistics scheduling model under a special condition, and most of the existing researches are based on ideal scenes, and in consideration of uncertainty of information, many students conduct the following researches on the uncertain scenes, lin Lu and Xiaochun Luo indicates that an emergency event is full of a large amount of uncertain information in the researches, so a new emergency transportation model is proposed in the text, and the emergency transportation scenes from a logistics center to disaster areas and among the disaster areas are simulated. Wang F and the like establish a multi-target multi-period emergency resource allocation model based on the uncertainty and persistence characteristics of the natural rescue process, and realize effective allocation of rescue materials and reasonable selection of transportation routes.
The following researches are also carried out by students aiming at the field of transportation, chen Y and the like review urban public transportation safety problems, transportation control methods and emergency public transportation planning, and emergency transportation control measures are proposed on the basis. The Ziyuan Liu and the like establish a transportation efficiency model of medical waste between a hospital and a temporary storage station by adopting an ant colony-tabu mixing algorithm, so that the medical waste disposal problem based on the transfer temporary storage station is solved to a certain extent. Pacheco, J. And Lagura, M. Considering that as the number of emergency supplies increases rapidly, a vehicle path problem arises, layering the objective functions, the first objective is to minimize the transit time of the longest route, and the second objective is to minimize the total travel distance.
None of the above relates to specific scheduling of container port operations and priority for transportation management of emergency materials. Therefore, in consideration of urgency of emergency material transportation, the container port emergency material optimizing and transporting method considering time window constraint is provided for carrying out preferential transportation on the container port emergency material, and has important research significance and research value.
Disclosure of Invention
The invention aims to provide a container port emergency material optimizing and transferring method taking time window constraint into consideration, which comprises the steps of firstly, establishing an upper model with the aim of minimizing the total cost of a container in a port, and planning a port arrival time window (the earliest port arrival time to the latest port arrival time) for an outer container; then, based on the transportation task priority of the ship corresponding to the external set card, aiming at minimizing the time window adjustment cost, establishing a lower model to adjust the time window; and finally, further optimizing the adjusted time window to be a corresponding real time window based on a chaotic genetic algorithm of Logistic mapping, and enabling the outer collector card to arrive at the gate for operation based on the real time window. The invention can reduce the congestion of the harbor area, reduce the occupation cost of the storage yard and carry out priority allocation and transportation on the corresponding materials.
In order to achieve the above purpose, a container port emergency material optimizing and transferring method taking time window constraint into consideration includes the steps:
s1, establishing an objective function with the aim of minimizing the total cost of the container in the port, taking port collecting time, the number of outer collection cards distributed by a gate for a ship, queuing length and average waiting time of the outer collection cards, occupied space and occupied time of the container in a storage yard as constraints, and establishing an upper model to plan a time window for the arrival of the outer collection cards for the task of the outer collection cards; the time window is a period from the earliest container arrival time of the outer collector to the latest container arrival time of the outer collector;
s2, based on the priority of the ship corresponding to the external set card task, aiming at minimizing the time window adjustment cost, establishing a lower model to adjust the time window;
s3, based on an L-CGA algorithm, the time window adjusted by the lower model is further optimized to be a corresponding real time window, and the outer set card arrives at the port based on the real time window of the ship.
Optionally, the objective function in step S1 is:
minTC＝∑ i ∑ t W 1 +∑ i ∑ t W 2 +∑ t W 3 ＝∑ i ∑ t WCO it +∑ i ∑ t YCD it +∑ t PC t ； (1)
wherein,
W2＝YCD it ＝(TV+AC)×D it ×f' it ； (3)
TC is the total cost of the container in port; i is the ship number, i=1, 2 … I; t represents time, t=1, 2 … T; WCO (Wireless control unit) it Indicating the waiting expense and the oil consumption of the ith ship at time t; a represents the unit cost of waiting time per hour per truck; b represents the unit cost of hourly combustion consumption per truck; f's' it The arrival times of the outer set card redistributed for the ith ship at the time t are shown;the average waiting time of the outer set card is the arrival time t, and the unit is hour; YCD (YCD) it Yard cost and storage time cost for the ith vessel at time t; TV per hourA storage time value for each standard bin of goods; AC is yard cost per container per hour; d (D) it The average storage time length of the containers which are reached by the ith ship in the time t is given in hours; PC (personal computer) t A penalty fee representing insufficient storage space at time t; o (O) t The storage space occupied at time t is; y is the total stock capacity of the storage yard.
Optionally, the constraint in step S1 includes:
TB it -TA it ＝T l ； (5)
TA it +T k ≥ER it ； (6)
TB it +6≤ER it ； (7)
T l ≥6； (8)
TA it ,TB it is a positive integer; (9)
f' it ＝f it +f i(t+24×7) +f i(t-24×7) ； (11)
n t ＝max(n t-1 +∑ i f' it -H,0)； (12)
O t-1 TL it +∑ i Q i ≤Y； (15)
O t ＝O t-1 +∑ i Q i (TF it -TL it )； (16)
TV＝VC×r； (18)
Wherein ER it Representing the estimated arrival time of the ith vessel; t (T) l Is the duration of the time window; t (T) k Indicating that the start of the port collection is not earlier than the arrival time T of the ship k Hours; TA (TA) it For the start of the time window, TB it Is the end of the time window; h is gate treatment rate, and the unit is: vehicle/hour; f (f) it The arrival times of the external collector card of the ith ship at time t are represented; n is n t The queuing length of the outer set card of the time t is represented; g is gate number, g=1, 2 … G; v (V) ii For the number of containers loaded on the ith vessel; ER (ER) it Representing the estimated arrival time of the ith vessel; q (Q) i The container load for the ith ship; VC is the average value of a piece of container cargo; r is the hourly interest rate; d (D) it Average storage time in units of time t for the containers of the ith ship to be terminated: hours; TV is the time cost per hour per standard box of cargo storage;
λ it the number of the external collection cards reaching the storage yard at the time t; d, d it The outer collecting card leaving quantity of the gate g at time t is obtained; AQ it The number of outer collector cards for transporting the ith ship container to the gate; e (E) i Representing the number of containers corresponding to the ith ship.
Optionally, in step S2, the objective function of the lower layer model is:
wherein, L is the container number, l=1, 2, …, L; j is the outer set card number j=1, 2, …, J;
TA it ' as the start of the adjusted time window, TB it ' is the end of the adjusted time window; n is the number of divided time windows;to adjust the cost.
Optionally, in step S2, the lower layer model includes constraints:
TA it ≤TR it ≤TB it ； (27)
TA it -TA it ′≤2； (33)
wherein,representing a priority level; t is port working time; k is the service sequence to the port outside set card; m represents a set unit time window step, m=1, 2 … M; TR (TR) it The time when the gate actually starts to process the external collection card task is represented;indicating the terminal time of shipping the first container at the port; />Representing the start time of shipping the first +1th container at the port; TR (TR) it The time when the gate actually starts to process the external collection card task is the time;
Z jm the outer set card j arrives in a time window m;
Y im the tasks of the ship i are processed within the time window m.
Optionally, step S3 includes:
s31, let T a ＝(TA 1t a ,TA 2t a ,…,TA Rt a ,TB 1t a ,TB 2t a ,…TB Rt a ) The method is characterized in that the method is a feasible solution of a lower model, the feasible solution is taken as a chromosome, the length of the chromosome is L, L=2R, R is the total number of external collector card tasks, and one external collector card has and only has one external collector card task; TA (TA) it a Starting point, TB of time window of ith external set card task output for lower layer model it a The time window end point of the ith external set card task is output for the lower layer model; TA (TA) it a 、TB it a I and i+R genes as the chromosome, respectively; i epsilon [1, R]The method comprises the steps of carrying out a first treatment on the surface of the Set KXJ = [ T ] of all feasible solutions of the underlying model 1 ,…,T M′ ]Forming a population; a E [1, M ]']M' is the number of all feasible solutions of the lower model;
s32, in L1 0 Is based on L1 as a first initial value 0 Generating a corresponding first chaotic sequence log 1= { L1 1 ,L1 2 … }, wherein L1 p+1 ＝μL1 p (1-L1 p ) The method comprises the steps of carrying out a first treatment on the surface of the By L2 0 Is based on L2 for a second initial value 0 Generating a second chaotic sequence log2= { L2 1 ,L2 2 … }, wherein L2 p+1 ＝μL2 p (1-L2 p )；0＜L1 0 ,L2 0 < 1; p=1, 2, …; recording CS=1, wherein CS is the iteration number; DDYZ is a preset iteration number threshold; mu is a set constant;
s33, calculating chromosome T a The fitness value f of (2) a ，a∈[1,M′]The method comprises the steps of carrying out a first treatment on the surface of the The fitness value is the reciprocal of the total cost of the container in the port calculated based on the corresponding chromosome; let f b ＝max(f 1 ,…,f M′ )，b∈[1,M′]Will T b Making optimal chromosomes;
s34, generating a random number x corresponding to the iteration number CS CS ，0＜x CS < 1; let P c The cross probability is preset; when x is cs ＞P c S35, entering; otherwise, enter S36;
s35, selecting KXJ- { T by roulette b Two chromosomes T are selected a1 、T a2 ；a1,a2∈[1,M′]The method comprises the steps of carrying out a first treatment on the surface of the Wherein chromosome T a1 、T a2 The probabilities of being chosen are P a1 、P a2 ：
f a1 、f a2 Respectively T a1 、T a2 Is a fitness value of (a); based on L1 CS Generating an intersection jc, for T a1 、T a2 Perform the interleaving operation, update T a1 、T a2 The method comprises the steps of carrying out a first treatment on the surface of the Wherein L1 CS Epsilon log1; enter S36;
s36, order P m The variation probability is preset; when x is CS ＞P m S37 is entered; otherwise, enter S38;
s37, selecting KXJ- { T by roulette b Selecting a chromosome T a3 ，a3∈[1,M′]The method comprises the steps of carrying out a first treatment on the surface of the Chromosome T a3 The probability of being selected is
Wherein f a3 Is T a3 Is a fitness value of (a); based on L2 CS Generating a variation point by for T a3 Performing a mutation operation update T a3 The method comprises the steps of carrying out a first treatment on the surface of the Wherein L2 CS ∈log2；
S38, CS is added with 1; when CS is less than DDYZ, entering S32; otherwise, calculating fitness values of all chromosomes in the population, outputting one chromosome with the highest fitness value, and recording the chromosome as T a4 ＝(TA 1t a4 ,TA 2t a4 ,…,TA Rt a4 ,TB 1t a4 ,TB 2t a4 ,…TB Rt a4 )；a4∈[1,M′]The method comprises the steps of carrying out a first treatment on the surface of the Make TA it a4 The real time window starting point, TB, of the ith external set card task it a4 The real time window end point of the ith external set card task.
Optionally, in step S35, L1 is used CS The method for generating the intersection point comprises the following steps: jc=l1 CS+1 * L is; the interleaving operation includes: will T a1 P' th gene of (A) and T a2 P' th gene exchange of (a); p' ∈ [ jc, L]。
Optionally, the step S37 is based on L2 CS Generating a variation point by for T a3 Performing a mutation operation update T a3 Comprising:
s371, based on L2 CS Generating a variation point by: by=l2 CS+1 * L is; if by is less than R, the process proceeds to S372; otherwise, enter S373;
s372, chromosome T a3 By gene TA in (2) byt a3 For the start of the time window, TB byt a3 Is in combination with TA byt a3 A corresponding time window endpoint; updating TA byt a3 So that the updated TA byt a3 The constraint is satisfied:
TB byt a3 -TA byt a3 ＝T l ；
TA byt a3 +T k ≥ER it ；
TB byt a3 +6≤ER it ；
T l ≥6；
TA byt a3 、TB byt a3 is a positive integer;
s373, chromosome T a3 By gene TB in (B) by′t a3 For the start of the time window, by' =by-R, TA by′t a3 Is of the type with TB by′t a3 A corresponding time window start point; updating TB by′t a3 So that updated TB by′t a3 The constraint is satisfied:
TB by′t a3 -TA by′t a3 ＝T l ；
TA by′t a3 +T k ≥ER it ；
TB by′t a3 +6≤ER it ；
T l ≥6；
TA by′t a3 、TB by′t a3 is a positive integer.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the arrival time window of the outer collecting card is optimized through the double-layer model and the L-CGA algorithm, so that the total cost of the container in the port can be effectively reduced, the occupied cost and space of a storage yard are reduced, meanwhile, the preferential transportation of emergency materials is ensured, the overlapping part of the time windows of the outer collecting cards corresponding to all ships is reduced, the arrival mode of the outer collecting card is optimized, the queuing waiting time of the outer collecting card is reduced, and the preferential transportation of the emergency materials is realized while the port congestion problem is effectively relieved.
Drawings
For a clearer description of the technical solutions of the present invention, the drawings that are needed in the description will be briefly introduced below, it being obvious that the drawings in the following description are one embodiment of the present invention, and that, without inventive effort, other drawings can be obtained by those skilled in the art from these drawings:
FIG. 1 is a schematic diagram of an upper model of the present invention;
FIG. 2 is a schematic view of an underlying model of the present invention;
FIG. 3 is a schematic representation of crossing two chromosomes according to an embodiment of the present invention;
FIG. 4 is a diagram showing chromosomal variation according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of optimizing a time window by an L-CGA algorithm in an embodiment of the present invention;
FIG. 6 is a flow chart of an external set card operation;
FIG. 7 is a graph comparing convergence curves of population optimal values obtained by iteration of a genetic algorithm, an adaptive genetic algorithm and an L-CGA algorithm of the invention;
FIG. 8 is a graph comparing time durations required for obtaining an optimal value of a population through iteration of a genetic algorithm, an adaptive genetic algorithm and an L-CGA algorithm of the invention;
FIG. 9 is a comparison graph of iterative solutions of genetic algorithms, adaptive genetic algorithms, and the L-CGA algorithm of the present invention;
FIG. 10 is a graph comparing the time window of the upper model output with the time window generated by the L-CGA algorithm;
FIG. 11 is a graph showing two contrasts of the genetic algorithm, the adaptive genetic algorithm and the outer set card under the L-CGA algorithm of the present invention;
fig. 12 is a flow chart of the container port emergency material optimizing and transporting method considering time window constraint.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Aiming at the transportation requirement of emergency materials, the invention provides an external collection card arrival time window optimizing strategy and an emergency material priority allocation strategy which are implemented in a container port, so as to carry out priority allocation and transportation on the emergency materials and ensure that the emergency materials are delivered to a destination at the first time. According to the method, a double-layer optimization model (comprising an upper layer model and a lower layer model) for preferentially transferring emergency materials of the container port is constructed to plan a time window of an external collection card task; and the optimal solution is obtained through iteration based on the output result of the double-layer optimization model by an L-CGA (logical mapping-based chaotic genetic algorithm) algorithm and serves as a real time window for the work of the outer set card, so that an optimal scheme for preferentially transferring emergency materials is obtained.
The precondition for establishing the upper layer model and the lower layer model in the invention is as follows:
(1) Before the time window plan is generated, upcoming vessel information is known;
(2) Since the container ship is Zhou Banlun, a weekly arrival mode is used, so that the port collecting operation of the arrival ship in the week of the container terminal is studied;
(3) The number of container loads per ship is known, and the container specification is assumed to be a standard container of 20 feet in length;
(4) The processing speed of each gate of the wharf is consistent;
(5) The service efficiency of each field bridge is consistent, and the energy consumed by each field bridge is the same.
Definition of parameters related to the upper layer model:
input variables of the upper model:
i: vessel number, i=1, 2 … I;
t: time, t=1, 2 … T;
g, a gate; g=1, 2 … G;
ER it : the i-th ship estimated time of arrival;
EL it : the ith ship's predicted departure time (h);
Q i : ith shipping container loading;
h: gate treatment rate (vehicle/hour);
a: a unit cost of waiting time per hour per truck;
b: unit cost per hour of burn consumption per truck;
AC: a yard fee per container per hour;
VC: average value of a piece of container cargo;
r: the hourly interest rate;
Y: total stock capacity of the yard;
T l : the length of the time window;
T k : the starting port collecting time is not earlier than the ship arrival time T k Hours;
derived variables of upper layer model:
WCO it : equal cost and oil consumption of the ith ship at time t;
YCD it : yard cost and storage time cost of the ith vessel at time t;
PC t : punishment cost of insufficient storage yard space at t;
f it : the number of times of arrival of the outer collector card of the ith ship at t;
f' it : the arrival times of the outer set card redistributed for the ith ship at t;
n t : queuing length at t (measured as number of vehicles queued);
truck average waiting time (hours) for arrival time t;
D it : average storage time (hours) of containers arrived at by the ith ship within time t;
TV: the cost per hour of storage time per standard case of goods;
O t : the storage space occupied at t;
λ it : the quantity of the collection cards reaching the storage yard in the period represented by the moment t;
d it : the amount of the card leaving of the gate channel g in the period represented by the moment t;
AQ it : to the point ofThe number of the outer collecting cards for conveying the ith ship container at the port gate is reached;
E i : the number of containers corresponding to the ith ship;
the number of containers loaded on the ith vessel;
TA it : the start of the time window (the start time of the outer header shipping container task);
TB it the end of the time window (the end time of the container transporting task of the outer collector card, namely the end of the time window);
ER it Indicating the expected arrival time of the ith vessel.
TC is the total cost of the container in port;
definition of lower model parameters:
l, a container; l e L;
j, an outer collection card; j e J;
k, the service sequence of the port outside collection card;
s: time window capacity (maximum number of tasks that can be processed);
t: port working time;
m: a window step length per unit time (in time);
n: the number of the time windows after division;
TR it the time when the gate actually starts to process the external card collecting task;
TA it ' the starting point of the time window after adjustment;
TB it ' end point of time window after adjustment;
Z jm the outer set card j arrives in a time window m;
Y im processing the tasks of the ship i in a time window m;
adjusting the cost;
priority level;
the terminal time of shipping the first container at the port;
the start time of port shipment of the 1+1th container;
as shown in fig. 12, the invention provides a container port emergency material optimizing and transporting method considering time window constraint, comprising the following steps:
s1, taking the minimum total cost of the container in the port as a target, taking port collecting time, the number of outer collection cards distributed by a gate as a ship, queuing length and average waiting time of the outer collection cards, occupied space and occupied time of the container in a storage yard as constraints, establishing an upper model, and planning a time window for the arrival of the outer collection cards for the task of the outer collection cards; the time window is a period from the earliest container arrival time of the outer collector to the latest container arrival time of the outer collector;
The external set card task time window solved by the upper model is used as input of the lower model, the upper model frame is shown in fig. 1, when a container port receives a ship arrival notification, the cooperative planning of land side and seashore is comprehensively considered, and the corresponding task time window is arranged. For all processes of container in port, the total cost of the container in port is the waiting time of the external collecting card, the cost of fuel consumption, the cost of storage yard and the cost of storage time, in addition, when the storage yard is seriously pressed, in order to prevent the container from being stored in the storage yard without empty space when the external collecting card is sent to the container, unnecessary waiting time and additional cost are caused, and therefore, a punishment cost of insufficient storage yard space is set. The arrival of the outer header meets the time window constraints associated with the ship while ensuring that the total cost of the container in the port is minimized, and the objective function of the upper model can be expressed by equation (1):
minTC＝∑ i ∑ t W 1 +Σ i Σ t W 2 +Σ t W 3 ＝Σ i Σ t WCO it +Σ i Σ t YCD it +Σ t PC t ； (1)
wherein,
W2＝YCD it ＝(TV+AC)×D it ×f' it ； (3)
equation (2) is the total cost of the external header, including the latency of the external header and the cost of fuel consumption. Equation (3) is the yard cost and the storage time cost of the container in the port. W3 is punishment cost of insufficient storage yard space at t, and a plurality of shippers cannot lift containers in time in special weather or busy operation, so that the backlog and large-batch containers in the harbor storage yard are caused The phenomenon of port blockage causes stock rising, and is very likely to cause congestion. Therefore, a punishment cost is set to avoid that the storage yard has no storage position when the external collection card is sent to the box, wherein O t And when the required storage space is smaller than the total storage capacity of the storage yard, the punishment cost is 0.
Constraints of the upper layer model include:
TB it -TA it ＝T l ； (5)
TA it +T k ≥ER it ； (6)
TB it +6≤ER it ； (7)
T l ≥6； (8)
TA it ,TB it is an integer; (9)
Equation (5) shows that the length of the time window from the outer collector card to the task is T l Hours; equation (6) shows that the start of the port collection time is not earlier than the arrival time T of the ship k Hours; equation (7) indicates that the port-gathering work is to be completed 6 hours before the arrival of the ship. Equation (8) means that the start point of each time window should be at least 6 hours earlier than the end point, i.e. the minimum length of the time window in practice is 6 hours; equation (9) ensures that the start and end times of the outer header shipping time associated with the ith vessel are integers.
f' it ＝f it +f i(t+24×7) +f i(t-24×7) ； (11)
n t ＝max(n t-1 +∑ i f' it -H,0)； (12)
Formulas (10) - (13) are related constraints at the gate, and formula (10) is the outer collector arrival of the ith vessel at t; equation (11) represents the truck arrival amount for the i-th ship reassignment at t; the queuing length of the outer collector card at the port gate at t is measured by the number of queuing vehicles according to the formula (12); equation (13) refers to the average waiting time of a truck at gate for a time of arrival t.
O t-1 TL it +∑ i Q i ≤Y； (15)
O t ＝O t-1 +∑ i Q i (TF it -TL it )； (16)
TV＝VC×r； (18)
Equation (14) is the arrival amount of the collector card at the storage yard in the period represented by time t; equation (15) ensures that the occupied yard space does not exceed the total storage capacity of the dock at any time; the formula (16) refers to the storage space occupied at t; equation (17) refers to the average storage time of containers that the ith ship arrives at within time t; equation (18) is the storage time cost per standard container cargo per hour, which is equal to the average value per container cargo multiplied by the hourly interest rate; equation (19) calculates the number of containers corresponding to the ith ship arriving at the port at time t.
S2, based on the priority of the ship corresponding to the external set card task, aiming at minimizing the time window adjustment cost, establishing a lower model to adjust the time window;
the time window output by the upper model is also an initial time window, and because the time window solved by the upper model has a large span, the time windows of different external set card tasks are likely to have overlapping parts. When the overlapped part has more external card collecting tasks, the port congestion can be caused, so that an initial time window is required to be input into a lower model, and the initial time window is adjusted through the lower model.
And the lower layer model aims at minimizing the cost of the external set card task adjusting time window, and according to the priority level of the external set card task, the higher the external set card adjusting cost with high level is, the priority of emergency materials is ensured to be transferred, the time window of the task with low priority is adjusted backwards, and finally, the external set card task time window scheme with the lowest adjusting cost is obtained. The lower layer model in the present invention is shown in fig. 2.
The objective function of the underlying model is:
preferably, the lower model in step S2 includes constraints:
formulas (21) - (22) are used to define priorities, and the container priorities for the outer header loads remain unchanged throughout the port operations. Dividing task class into I, II and III, and expressing the priority of the corresponding external set card loading container by a specific small numberThe higher the priority level, the greater the weight and the smaller the corresponding value of its priority.
The formula (23) is used for calculating the number of subdivided time windows, wherein T is port working time, and M is unit time window step length; equation (24) is used to ensure that, in the overlapping range of the time windows, the time of entering the port of the outer set card with high priority is earlier than that of the outer set card with low priority through adjustment: the formula (25) ensures that the adjusted task time window of the external collecting card is still in an initial time window obtained by an upper layer model; equation (26) is used to determine whether there is an overlap of time windows, indicating that two task time windows overlap when the start time of the next task is earlier than the end time of the previous task.
TA it ≤TR it ≤TB it ； (27)
Equation (27) -equation (30) represents constraints associated with the gate, wherein equation (27) ensures that the time at which the gate actually begins processing an external set card task is within an initial time window; equation (28) ensures that there is and only one external set card task per external set card; equation (29) ensures that each external set card has and only one gate is serviced, only once; equation (30) shows that at most one external header is processed at the same time for any gate; equation (31) indicates that the number of external cards entering the port at the same time does not exceed the total number of gates.
/>
TA it -TA it ′≤2； (33)
Equation (32) shows that the outer cluster card serving the same ship enters the port in the same time window; equation (33) shows that the outer set card task adjustment range with overlapping time windows is within the interval of two time windows.
S3, based on an L-CGA algorithm, the adjusted time window is further optimized to be a corresponding real time window, and the external collector card executes tasks based on the real time window of the ship. Flow of the L-CGA algorithm.
Because the local searching capability of the genetic algorithm is poor, the simple genetic algorithm is time-consuming, and the searching efficiency is low in the later period of evolution; the method is easy to sink into local convergence, buffeting is easy to occur in the optimizing process, and the algorithm convergence precision is not high; and is prone to premature convergence problems. According to the characteristics of the container port emergency material priority transportation double-layer optimization model, a Logistic chaotic sequence is introduced into a genetic algorithm aiming at the problems of the genetic algorithm.
As shown in fig. 5, step S3 includes:
s31, let T a ＝(TA 1t a ,TA 2t a ,…,TA Rt a ,TB 1t a ,TB 2t a ,…TB Rt a ) The method is characterized in that the method is a feasible solution of a lower model, the feasible solution is taken as a chromosome, the length of the chromosome is L, L=2R, R is the total number of external collector card tasks, and one external collector card has and only has one external collector card task; TA (TA) it a Ith external collector card for lower model output Starting point of time window of task, TB it a The time window end point of the ith external set card task is output for the lower layer model; TA (TA) it a 、TB it a I and i+R genes as the chromosome, respectively; i epsilon [1, R]The method comprises the steps of carrying out a first treatment on the surface of the Set KXJ = [ T ] of all feasible solutions of the underlying model 1 ,…,T M′ ]Constructing a population, a chromosome in the population also being referred to as an individual of the population; a E [1, M ]']M' is the number of all feasible solutions of the lower model; in some embodiments of the invention, the individuals in the population are also randomly initialized based on constraints (5) through (9); ensuring that every individual is viable.
S32, in L1 0 Is based on L1 as a first initial value 0 Generating a corresponding first chaotic sequence log 1= { L1 1 ,L1 2 … }, wherein L1 p+1 ＝μL1 p (1-L1 p ) The method comprises the steps of carrying out a first treatment on the surface of the By L2 0 Is based on L2 for a second initial value 0 Generating a second chaotic sequence log2= { L2 1 ,L2 2 … }, wherein L2 p+1 ＝μL2 p (1-L2 p )；0＜L1 0 ,L2 0 < 1; p=1, 2, …; recording CS=1, wherein CS is the iteration number; DDYZ is a preset iteration number threshold; mu is a set constant.
Chaos is an aperiodic cyclic behavior in a deterministic system, can not repeatedly go through all states within a certain range, has extremely large ergodic performance, utilizes the randomness and ergodic performance of chaos to construct a chaos operator, introduces a chaos sequence into a genetic algorithm, can prevent the problem of population aggregation of the traditional genetic algorithm due to random characteristics, and enhances global search characteristics. In an embodiment of the present invention, μ=4 causes the first and second chaotic sequences to reach full chaos.
S33, calculating chromosome T a The fitness value f of (2) a ，a∈[1,M′]The method comprises the steps of carrying out a first treatment on the surface of the The fitness value is the reciprocal of the total cost TC of the container in the port calculated based on the corresponding chromosome; the reciprocal passes through formulas (1) - (5) and formula (10)- (13) wherein T is employed when applying the formula (10) a The time window start/end contained in the model replaces the initial time window start/end output by the upper layer model. And will not be described in detail herein.
S34, generating a random number x corresponding to the iteration number CS CS ，0＜x CS < 1; it is emphasized that x CS Is not a constant value, and is updated in each iteration; let P c The cross probability is preset; when x is cs ＞P c S35 (cross operation is performed); otherwise, enter S36;
s35, selecting KXJ- { T by roulette b Two chromosomes T are selected a1 、T a2 ；a1,a2∈[1,M′]The method comprises the steps of carrying out a first treatment on the surface of the Wherein chromosome T a1 、T a2 The probabilities of being chosen are P a1 、P a2 ：
f a1 、f a2 Respectively T a1 、T a2 Is a fitness value of (a); based on L1 CS Generating an intersection jc, jc=l1 CS+1 *L；
For T a1 、T a2 Performs a cross operation (T at this time) a1 、T a2 Also referred to as parent chromosomes), the crossover operation comprising: will T a1 P' th gene of (A) and T a2 P' th gene exchange of (a); p' ∈ [ jc, L]. Update T a1 、T a2 (updated T) a1 、T a2 Also known as offspring chromosomes); wherein L1 CS Epsilon log1. And proceeds to S36.
In the first embodiment of the invention, the total number of the external set card tasks is 10, and the chromosome length is 20.
FIG. 3 is a schematic representation of chromosome crossover, with father 1 and father 2 being chromosomes generated based on two possible solutions of the underlying model. And crossing the parent 1 and the parent 2 to generate corresponding next generation chromosome offspring 1 and offspring 2 respectively. In fig. 3, TA is the start time of a task, TB is the end time of the task, for example, ta1=8, tb1=21 indicates that the time window of the external card task 1 (abbreviated as task one) is (8, 21), and the earliest time for the external card to transport the corresponding container task is 8 and the latest time is 21. The crossover point 13 is generated by the chaotic sequence, and then the crossover starts at the position corresponding to TB 3. TA1 and TB1 correspond to the 13 th to 20 th genes. The single-point cross has little change to the original solution, can weaken and avoid the problem of optimizing buffeting generated in the combined optimization application of the genetic algorithm, and can improve the convergence precision of the algorithm.
S36, order P m The variation probability is preset; when x is CS ＞P m S37 is entered; otherwise, enter S38;
s37, selecting KXJ- { T by roulette b Selecting a chromosome T a3 ，a3∈[1,M′]The method comprises the steps of carrying out a first treatment on the surface of the Chromosome T a3 The probability of being selected is
Wherein f a3 Is T a3 Is a fitness value of (a); based on L2 CS Generating a variation point by=l2 CS+1 * L is; the mutation operation comprises: altering chromosome T a4 The by gene in (2) is And->Meets the set constraint:
for T a3 Performing mutation operation and updating T a3 The method comprises the steps of carrying out a first treatment on the surface of the Wherein L2 CS ∈log2；
As shown in fig. 4, x CS When=0.3, x CS+1 =4×0.3 (1-0.3) =0.84 > 0.1, using c= (int) x CS+1 * L determines the mutation point position, by=int (0.84×20) =16, so that mutation operation is performed at the position of TB6 to obtain a new gene value after mutation, thereby obtaining a new chromosome.
Ta6=79, er6=104, and according to formulas (5) - (9), TB6-79 is equal to or greater than 6, tb6+6 is equal to or less than 104, resulting in 85 is equal to or less than 98, and thus mutated TB6 is a random integer value in (85, 98).
S38、CS is added with 1; when CS is less than DDYZ, entering S32; otherwise, calculating fitness values of all chromosomes in the population, outputting one chromosome with the highest fitness value, and recording the chromosome as T a4 ＝(TA 1t a4 ,TA 2t a4 ,…,TA Rt a4 ,TB 1t a4 ,TB 2t a4 ,…TB Rt a4 )；a4∈[1,M′]The method comprises the steps of carrying out a first treatment on the surface of the Make TA it a4 The real time window starting point, TB, of the ith external set card task it a4 The real time window end point of the ith external set card task.
In the L-CGA algorithm, the most suitable solution in the current feasible time window is reserved to the next generation, the crossover and mutation operations are carried out, the crossover can help to inherit fragments of excellent chromosomes to offspring, the global searching function is also realized, the mutation is also a means for realizing population diversity, the important guarantee of local optimum and global optimum is obtained, and finally the arrival time window of the outer set card with the optimum output is ensured.
Examples
The proposed model is applied to actual data prediction, the actual data is derived from a China container port with large throughput and serious road congestion, a week is taken as a period, the period comprises a weekly ship berthing plan, related container and card collecting information, and fig. 6 is the operation flow of the external card collecting in the port. Taking 10 ships berthed within 7 days as an example, the transceiving time of the ships, the system data of a container port and the cost data are collected, and the proposed optimization model is verified. Table 1 shows the input variables and input values of the upper and lower models; TABLE 2 predicted arrival and departure times of vessels
Table 1 input variables and input values
TABLE 2 predicted arrival and departure times of vessels
Ship numbering Estimated time of arrival Estimated departure time
1 Cycle 4:00 Cycle 18:00
2 Zhou 22:00 Zhou three 21:00
3 Zhou 3 20:00 Thursday 9:00
4 Thursday 2:00 Thursday 18:00
5 Thursday 16:00 Friday 2:00
6 Thursday 20:00 Friday 16:00
7 Friday 22:00 Saturday 13:00
8 Saturday 2:00 Saturday 16:00
9 Saturday 8:00 Saturday 23:00
10 Day 0:00 Day 9:00
Analysis of results:
in order to test the applicability of the adopted port emergency material priority allocation double-layer optimization model and the proposed Logistic mapping-based chaotic genetic algorithm (L-CGA), simulation experiments are realized in Matlab2018b, and the population size, the crossover probability, the mutation probability and the maximum iteration number of the L-CGA are respectively set to 20, 0.9, 0.1 and 200, and the experimental results are shown in figures 7 to 9.
FIG. 7 shows the convergence process of the genetic algorithm, the adaptive genetic algorithm, and the Logistic map-based chaotic genetic algorithm population optimal value, and the Logistic map-based chaotic genetic algorithm (L-CGA) converges after the 35 th generation to obtain the minimum target value. Fig. 8 is a comparison of solving time of three algorithms, fig. 9 is a comparison of solving results of the three algorithms, the solving speed of the optimized chaotic genetic algorithm is faster, and the obtained results are better. In summary, the L-CGA convergence rate is high, so that the best solution can be converged, the population diversity is maintained, and the global convergence of the genetic algorithm is enhanced.
TABLE 3 Ship-related external collector task time window
/>
In table 3, ta1=12, tb1=18 means that the start time of task 1 is 12 and the end time is 18. It is a task for an external header to carry a given container to the port.
Table 4 shows the operation time window of the outer collecting card after the emergency materials are preferentially transferred
/>
Table 3 shows the time window of the external collector card task related to the ship, TA is the starting point of the time window, TB is the end point of the time window, and the specific operation time windows (the output result of the upper model) of all tasks are shown in the table. Table 4 is a working time window of the outer collector card after the emergency materials are preferentially dispatched, namely, an optimal scheme (output result of the lower model) for preferentially dispatching the emergency materials.
Fig. 10 shows the comparison result of the arrival time windows of the external collector card before and after the optimization of the L-CGA algorithm, wherein the rectangle with diagonal lines is the time window before the optimization, the dark gray rectangle is the time window after the optimization, and the overlapping part of the time windows is obviously reduced, from 124 hours before the optimization to 33 hours, and is reduced by about 73.39%. At the same time, the total task time window was reduced from the previous 264 hours to the current 121 hours by about 54.17%.
The result of comparing the arrival conditions of the outer collector cards under different algorithms shows that the arrival quantity of the outer collector cards solved by using the chaotic genetic algorithm based on Logistic mapping is more uniform, the concentrated arrival quantity of the outer collector cards is reduced, and the peak value is reduced.
According to the invention, the container port emergency material priority dispatching considering time window constraint is researched, and the priority dispatching of the emergency material is ensured by constructing a container port emergency material priority dispatching double-layer optimization model. Because irregular arrival of the collection card is a key factor causing dock gate and yard congestion and low port collection efficiency, meanwhile, the serious problem of yard pressing boxes is considered, and when the outer collection card is sent to the boxes, the storage yard is prevented from storing without empty space by adding punishment cost of insufficient yard space, so that port congestion is aggravated. And solving the double-layer optimization model by using a chaotic genetic algorithm based on Logistic mapping. Experimental results show that the double-layer optimization model and the chaotic genetic algorithm based on Logistic mapping provided by the method improve the speed and the precision of time window solving. The total cost of the outer collector card in the port is reduced, the preferential transportation of emergency materials is ensured, the overlapping part of time windows is reduced, the arrival mode of the outer collector card is optimized, and the queuing waiting time of the outer collector card is reduced.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.

Claims (1)

1. The container port emergency material optimizing and transferring method taking time window constraint into consideration is characterized by comprising the following steps:
s1, establishing an objective function with the aim of minimizing the total cost of the container in the port, taking port collecting time, the number of outer collection cards distributed by a gate for a ship, queuing length and average waiting time of the outer collection cards, occupied space and occupied time of the container in a storage yard as constraints, and establishing an upper model to plan a time window for the arrival of the outer collection cards for the task of the outer collection cards; the time window is a period from the earliest container arrival time of the outer collector to the latest container arrival time of the outer collector;
s2, based on the priority of the ship corresponding to the external set card task, aiming at minimizing the time window adjustment cost, establishing a lower model to adjust the time window;
S3, a chaos genetic algorithm based on Logistic mapping is used for further optimizing a time window adjusted by a lower model to be a corresponding real time window, and the outer collector card arrives at a port based on the real time window of the ship;
the objective function in step S1 is:
minTC＝∑ i ∑ t W 1 +∑ i ∑ t W 2 +∑ t W 3 ＝∑ i ∑ t WCO it +∑ i ∑ t YCD it +∑ t PC t ； (1)
wherein,
W2＝YCD it ＝(TV+AC)×D it ×f′ it ； (3)
TC is the total cost of the container in port; i is the ship number, i=1, 2 … I; t represents time, t=1, 2 … T; WCO (Wireless control unit) it Indicating the waiting expense and the oil consumption of the ith ship at time t; a represents the unit cost of waiting time per hour per truck; b represents the unit cost of hourly combustion consumption per truck; f's' it The arrival times of the outer set card redistributed for the ith ship at the time t are shown;the average waiting time of the outer set card is the arrival time t, and the unit is hour; YCD (YCD) it Yard cost and storage time cost for the ith vessel at time t; TV is the storage time cost per standard box cargo per hour; AC is yard cost per container per hour; d (D) it The average storage time length of the containers which are reached by the ith ship in the time t is given in hours; PC (personal computer) t A penalty fee representing insufficient storage space at time t; o (O) t The storage space occupied at time t is; y is the total stock capacity of the storage yard;
the constraint in step S1 includes:
TB it -TA it ＝T l ； (5)
TA it +T k ≥ER it ； (6)
TB it +6≤ER it ； (7)
T l ≥6； (8)
TA it ,TB it Is a positive integer; (9)
f′ it ＝f it +f i(t+24×7) +f i(t-24×7) ； (11)
n t ＝max(n t-1 +∑ i f′ it -H,0)； (12)
O t-1 TL it +∑ i Q i ≤Y； (15)
O t ＝O t-1 +∑ i Q i (TF it -TL it )； (16)
TV＝VC×r； (18)
Wherein ER it Representing the estimated arrival time of the ith vessel; t (T) l Is the duration of the time window; t (T) k Indicating that the start of the port collection is not earlier than the arrival time T of the ship k Hours; TA (TA) it For the start of the time window, TB it Is the end of the time window; h is gate treatment rate, and the unit is: vehicle/hour; f (f) it The arrival times of the external collector card of the ith ship at time t are represented; n is n t The queuing length of the outer set card of the time t is represented; g is gate number, g=1, 2 … G;for the number of containers loaded on the ith vessel; q (Q) i The container load for the ith ship; VC is the average value of a piece of container cargo; r is the hourly interest rate; d (D) it Average storage time in units of time t for the containers of the ith ship to be terminated: hours; TV is the time cost per hour per standard box of cargo storage;
λ it the number of the external collection cards reaching the storage yard at the time t; d, d it The outer collecting card leaving quantity of the gate g at time t is obtained; AQ it The number of outer collector cards for transporting the ith ship container to the gate; e (E) i Representing the number of containers corresponding to the ith ship;
in step S2, the objective function of the lower model is:
wherein, L is the container number, l=1, 2, …, L; j is the outer set card number j=1, 2, …, J;
TA it ' as the start of the adjusted time window, TB it ' is the end of the adjusted time window; n is the number of divided time windows;to adjust the cost;
the lower model in step S2 contains constraints:
TA it ≤TR it ≤TB it ； (27)
TA it -TA it ′≤2； (33)
wherein,representing a priority level; t is port working time; k is the service sequence to the port outside set card;
m represents a set unit time window step, m=1, 2 … M; TR (TR) it The time when the gate actually starts to process the external collection card task is represented;indicating the terminal time of shipping the first container at the port; />Representing the start time of shipping the first +1th container at the port; TR (TR) it The time when the gate actually starts to process the external collection card task is the time;
Z jm the outer set card j arrives in a time window m;
Y im processing the tasks of the ship i in a time window m;
step S3 includes:
s31, let T a ＝(TA 1t a ,TA 2t a ,…,TA Rt a ,TB 1t a ,TB 2t a ,…TB Rt a ) The method is characterized in that the method is a feasible solution of a lower model, the feasible solution is taken as a chromosome, the length of the chromosome is L, L=2R, R is the total number of external collector card tasks, and one external collector card has and only has one external collector card task; TA (TA) it a Output for the lower modelStarting point of time window, TB of i external set card tasks it a The time window end point of the ith external set card task is output for the lower layer model; TA (TA) it a 、TB it a I and i+R genes as the chromosome, respectively; i epsilon [1, R]The method comprises the steps of carrying out a first treatment on the surface of the Set KXJ = [ T ] of all feasible solutions of the underlying model 1 ,…,T M′ ]Forming a population; a E [1, M ]']M' is the number of all feasible solutions of the lower model;
s32, in L1 0 Is based on L1 as a first initial value 0 Generating a corresponding first chaotic sequence log 1= { L1 1 ,L1 2 … }, wherein L1 p+1 ＝μL1 p (1-L1 p ) The method comprises the steps of carrying out a first treatment on the surface of the By L2 0 Is based on L2 for a second initial value 0 Generating a second chaotic sequence log2= { L2 1 ,L2 2 … }, wherein L2 p+1 ＝μL2 p (1-L2 p )；0＜L1 0 ,L2 0 < 1; p=1, 2, …; recording CS=1, wherein CS is the iteration number; DDYZ is a preset iteration number threshold; mu is a set constant;
s33, calculating chromosome T a The fitness value f of (2) a ，a∈[1,M′]The method comprises the steps of carrying out a first treatment on the surface of the The fitness value is the reciprocal of the total cost of the container in the port calculated based on the corresponding chromosome; let f b ＝max(f 1 ,…,f M′ )，b∈[1,M′]Will T b Making optimal chromosomes;
s34, generating a random number x corresponding to the iteration number CS CS ，0＜x CS < 1; let P c The cross probability is preset; when x is cs ＞P c S35, entering; otherwise, enter S36;
s35, selecting KXJ- { T by roulette b Two chromosomes T are selected a1 、T a2 ；a1,a2∈[1,M′]The method comprises the steps of carrying out a first treatment on the surface of the Wherein chromosome T a1 、T a2 The probabilities of being chosen are P a1 、P a2 ：
f a1 、f a2 Respectively T a1 、T a2 Is a fitness value of (a); based on L1 CS Generating an intersection jc, for T a1 、T a2 Perform the interleaving operation, update T a1 、T a2 The method comprises the steps of carrying out a first treatment on the surface of the Wherein L1 CS Epsilon log1; enter S36;
s36, order P m The variation probability is preset; when x is CS ＞P m S37 is entered; otherwise, enter S38;
s37, selecting KXJ- { T by roulette b Selecting a chromosome T a3 ，a3∈[1,M′]The method comprises the steps of carrying out a first treatment on the surface of the Chromosome T a3 The probability of being selected is
Wherein f a3 Is T a3 Is a fitness value of (a); based on L2 CS Generating a variation point by for T a3 Performing a mutation operation update T a3 The method comprises the steps of carrying out a first treatment on the surface of the Wherein L2 CS ∈log2；
S38, CS is added with 1; when CS is less than DDYZ, entering S32; otherwise, calculating fitness values of all chromosomes in the population, outputting one chromosome with the highest fitness value, and recording the chromosome as T a4 ＝(TA 1t a4 ,TA 2t a4 ,…,TA Rt a4 ,TB 1t a4 ,TB 2t a4 ,…TB Rt a4 )；a4∈[1,M′]The method comprises the steps of carrying out a first treatment on the surface of the Make TA it a4 The real time window starting point, TB, of the ith external set card task it a4 The real time window end point of the ith external set card task is set;
l1-based in step S35 CS The method for generating the intersection point comprises the following steps: jc=l1 CS+1 * L is; the interleaving operation includes: will T a1 P' th gene of (A) and T a2 P' th gene exchange of (a); p' ∈ [ jc, L]；
The L2-based in step S37 CS Generating a variation point by for T a3 Performing a mutation operation update T a3 Comprising:
s371, based on L2 CS Generating variationsPoint by: by=l2 CS+1 * L is; if by is less than R, the process proceeds to S372; otherwise, enter S373;
s372, chromosome T a3 By gene TA in (2) byt a3 For the start of the time window, TB byt a3 Is in combination with TA byt a3 A corresponding time window endpoint; updating TA byt a3 So that the updated TA byt a3 The constraint is satisfied:
TB byt a3 -TA byt a3 ＝T l ；
TA byt a3 +T k ≥ER it ；
TB byt a3 +6≤ER it ；
T l ≥6；
TA byt a3 、TB byt a3 is a positive integer;
s373, chromosome T a3 By gene TB in (B) by′t a3 For the start of the time window, by' =by-R, TA by′t a3 Is of the type with TB by′t a3 A corresponding time window start point; updating TB by′t a3 So that updated TB by′t a3 The constraint is satisfied:
TB by′t a3 -TA by′t a3 ＝T l ；
TA by′t a3 +T k ≥ER it ；
TB by′t a3 +6≤ER it ；
T l ≥6；
TA by′t a3 、TB by′t a3 is a positive integer.
CN202110420026.1A 2021-04-19 2021-04-19 Container port emergency material optimizing and transferring method considering time window constraint Active CN112884383B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110420026.1A CN112884383B (en) 2021-04-19 2021-04-19 Container port emergency material optimizing and transferring method considering time window constraint

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110420026.1A CN112884383B (en) 2021-04-19 2021-04-19 Container port emergency material optimizing and transferring method considering time window constraint

Publications (2)

Publication Number Publication Date
CN112884383A CN112884383A (en) 2021-06-01
CN112884383B true CN112884383B (en) 2024-04-05

Family

ID=76040173

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110420026.1A Active CN112884383B (en) 2021-04-19 2021-04-19 Container port emergency material optimizing and transferring method considering time window constraint

Country Status (1)

Country Link
CN (1) CN112884383B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113298403B (en) * 2021-06-03 2023-11-07 大连海事大学 Public molten iron intermodal transportation scheduling method based on transportation time window synchronization
CN113570092B (en) * 2021-06-24 2023-09-08 招商局国际科技有限公司 Gate control method, device, equipment and computer readable storage medium
CN115081674B (en) * 2022-05-09 2023-04-18 香港理工大学深圳研究院 Local container transportation typesetting optimization method under novel truck queuing driving mode

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009223552A (en) * 2008-03-14 2009-10-01 Osaka Prefecture Univ Method for optimizing cargo distribution cost
CN106295851A (en) * 2016-07-27 2017-01-04 大连海事大学 A kind of truck information service and dispatching patcher and method of work thereof
CN106651280A (en) * 2017-03-23 2017-05-10 广东工业大学 Container ship logistics transportation scheduling method and system
CN108320059A (en) * 2018-02-22 2018-07-24 石家庄铁道大学 A kind of workflow schedule evolution optimization method and terminal device
CN109711790A (en) * 2018-12-07 2019-05-03 上海大学 Container Shipping method and device for planning
CN110443409A (en) * 2019-07-05 2019-11-12 武汉大学深圳研究院 A kind of harbour Containers For Export prestowage planning method
CN110689155A (en) * 2019-10-15 2020-01-14 上海海事大学 Multi-constraint scheduling method of card collection reservation system considering congestion and emission
CN110826819A (en) * 2019-11-27 2020-02-21 上海海事大学 Automatic container terminal vehicle path planning method
CN111325481A (en) * 2020-03-16 2020-06-23 郑州大学 Intelligent automatic container terminal energy-saving comprehensive scheduling method
CN112001064A (en) * 2020-07-29 2020-11-27 浙江大学 Full-autonomous water transport scheduling method and system between container terminals
CN112053046A (en) * 2020-08-24 2020-12-08 山东科技大学 Automatic container terminal AGV reentry and reentry path planning method with time window
CN112434870A (en) * 2020-12-01 2021-03-02 大连理工大学 Dual-automation field bridge dynamic scheduling method for vertical arrangement of container areas

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11829935B2 (en) * 2018-04-03 2023-11-28 Florida A&M University Application of a multi-objective optimization model for automatic vessel scheduling in liner shipping

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009223552A (en) * 2008-03-14 2009-10-01 Osaka Prefecture Univ Method for optimizing cargo distribution cost
CN106295851A (en) * 2016-07-27 2017-01-04 大连海事大学 A kind of truck information service and dispatching patcher and method of work thereof
CN106651280A (en) * 2017-03-23 2017-05-10 广东工业大学 Container ship logistics transportation scheduling method and system
CN108320059A (en) * 2018-02-22 2018-07-24 石家庄铁道大学 A kind of workflow schedule evolution optimization method and terminal device
CN109711790A (en) * 2018-12-07 2019-05-03 上海大学 Container Shipping method and device for planning
CN110443409A (en) * 2019-07-05 2019-11-12 武汉大学深圳研究院 A kind of harbour Containers For Export prestowage planning method
CN110689155A (en) * 2019-10-15 2020-01-14 上海海事大学 Multi-constraint scheduling method of card collection reservation system considering congestion and emission
CN110826819A (en) * 2019-11-27 2020-02-21 上海海事大学 Automatic container terminal vehicle path planning method
CN111325481A (en) * 2020-03-16 2020-06-23 郑州大学 Intelligent automatic container terminal energy-saving comprehensive scheduling method
CN112001064A (en) * 2020-07-29 2020-11-27 浙江大学 Full-autonomous water transport scheduling method and system between container terminals
CN112053046A (en) * 2020-08-24 2020-12-08 山东科技大学 Automatic container terminal AGV reentry and reentry path planning method with time window
CN112434870A (en) * 2020-12-01 2021-03-02 大连理工大学 Dual-automation field bridge dynamic scheduling method for vertical arrangement of container areas

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Scheduling trucks in container terminals using a genetic algorithm;W. C.Ng et al.;《Engineering Optmization》;第39卷(第1期);第33-47页 *
考虑早晚高峰期影响的集卡预约系统多约束调度模型与算法;许波桅等;《计算机集成制造系统》;第26卷(第10期);第2851-2863页 *

Also Published As

Publication number Publication date
CN112884383A (en) 2021-06-01

Similar Documents

Publication Publication Date Title
CN112884383B (en) Container port emergency material optimizing and transferring method considering time window constraint
Rashidi et al. Novel constraints satisfaction models for optimization problems in container terminals
Di Francesco et al. The effect of multi-scenario policies on empty container repositioning
Lin et al. The hierarchical network design problem for time-definite express common carriers
Li et al. Berth and quay crane coordinated scheduling using multi-objective chaos cloud particle swarm optimization algorithm
Song et al. Study on berth planning problem in a container seaport: Using an integrated programming approach
US20130159208A1 (en) Shipper-oriented logistics base optimization system
US20120226624A1 (en) Optimization system of smart logistics network
Ichoua1 et al. Planned route optimization for real-time vehicle routing
Yan et al. Transshipment operations optimization of sea-rail intermodal container in seaport rail terminals
CN111768149A (en) Express logistics network planning optimization method and system
CN107833002A (en) Multistage low-carbon logistics distribution network planing method based on collaboration multi-objective Algorithm
Won et al. Workload-based yard-planning system in container terminals
Leachman et al. Congestion analysis of waterborne, containerized imports from Asia to the United States
Feng et al. Space planning considering congestion in container terminal yards
Guo et al. Global synchromodal transport with dynamic and stochastic shipment matching
CN113240234A (en) Coordination optimization method for coal port shipment equipment allocation and ship traffic organization
Butko et al. Organization of railway freight short-haul transportation on the basis of logistic approaches
Yu et al. Ship scheduling problems in tramp shipping considering static and spot cargoes
CN117495001A (en) Mine accident area emergency material scheduling-path model construction method for improving PSO-BA algorithm
Lai Models and algorithms for the empty container repositioning and its integration with routing problems
Lai et al. A rolling horizon model to optimize aerodynamic efficiency of intermodal freight trains with uncertainty
Wu et al. Emergency resource scheduling during COVID-19
Gkolias The discrete and continuous berth allocation problem: Models and algorithms
Laik et al. Container assignment and yard crane deployment in a container terminal: A case study

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant