CN112348323A - Multi-target energy supply and operation flexible scheduling method - Google Patents
Multi-target energy supply and operation flexible scheduling method Download PDFInfo
- Publication number
- CN112348323A CN112348323A CN202011146645.8A CN202011146645A CN112348323A CN 112348323 A CN112348323 A CN 112348323A CN 202011146645 A CN202011146645 A CN 202011146645A CN 112348323 A CN112348323 A CN 112348323A
- Authority
- CN
- China
- Prior art keywords
- energy supply
- energy
- objective
- array
- scheduling
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 184
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 41
- 238000005265 energy consumption Methods 0.000 claims abstract description 38
- 238000005457 optimization Methods 0.000 claims abstract description 37
- 230000002068 genetic effect Effects 0.000 claims abstract description 30
- 230000035772 mutation Effects 0.000 claims abstract description 22
- 230000008569 process Effects 0.000 claims description 105
- 210000000349 chromosome Anatomy 0.000 claims description 34
- 108090000623 proteins and genes Proteins 0.000 claims description 31
- 238000004519 manufacturing process Methods 0.000 claims description 21
- 238000012545 processing Methods 0.000 claims description 18
- 238000002360 preparation method Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 claims description 4
- 230000007246 mechanism Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 description 3
- 238000004134 energy conservation Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 230000036961 partial effect Effects 0.000 description 3
- 230000000717 retained effect Effects 0.000 description 3
- 238000010845 search algorithm Methods 0.000 description 3
- 238000012163 sequencing technique Methods 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- 230000002759 chromosomal effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000002829 reductive effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000010187 selection method Methods 0.000 description 2
- 238000000638 solvent extraction Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 238000004378 air conditioning Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000003780 insertion Methods 0.000 description 1
- 230000037431 insertion Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
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
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Manufacturing & Machinery (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- General Factory Administration (AREA)
Abstract
The invention discloses a multi-target energy supply and operation flexible scheduling method, which comprises the following steps: (1) determining an energy supply optimization index and energy supply constraint of flexible scheduling, and constructing a multi-objective energy supply optimization model; the objective functions of the multi-objective energy supply optimization model comprise an objective function with the minimum maximum completion time, an objective function with the minimum energy consumption maximum load and an objective function with the minimum total energy consumption load; (2) solving the multi-target energy supply optimization model by adopting an improved genetic algorithm to ensure that each target function reaches the minimum value and obtain the optimal energy supply and operation flexible scheduling; the improved genetic algorithm adopts self-adaptive cross and mutation probability and adopts selection operation based on NSGA-II. The energy-saving and efficiency-increasing method takes energy-saving and efficiency-increasing as an optimization target, constructs an energy optimization index, applies an improved genetic algorithm, assists a scheduling staff to quickly generate a scheduling plan, and further generates an energy supply plan.
Description
Technical Field
The invention relates to the technical field of workshop production scheduling, in particular to a multi-target energy supply and operation flexible scheduling method.
Background
In the aspect of workshop production scheduling, more than sixty years of research history exists, and some analytical optimization, dynamic programming, heuristic algorithms and the like are proposed in the early stage. With the development of computer science and software and hardware technology, some multidisciplinary crossing complex algorithms such as genetic algorithm, artificial neural network, ant colony algorithm and other intelligent methods are gradually applied, and abundant research results are also accumulated in the aspect of operation scheduling under complex conditions.
The genetic algorithm can carry out global optimization on complex engineering problems by simulating the living competition process of organisms in the nature and utilizing operators such as selection, intersection, mutation and the like. For example, chinese patent publication No. CN111208796A discloses a workshop production operation scheduling method based on clustering niche genetic algorithm, and publication No. CN110069880A discloses a multi-target device layout and production schedule coordination optimization method based on simulation, which all apply genetic algorithm to operation scheduling in complex situations. However, the local search capability of the traditional genetic algorithm is limited, and the traditional genetic algorithm is easy to fall into a local optimal solution in the search process.
With the continuous deepening of the economic globalization and the aggravation of market competition, enterprises usually implement flexible production modes of multiple varieties and small batches to adapt to complex and variable competitive environments, so that the enterprises are required to adopt flexible and stable organizational modes in the production level. And a large amount of production activities have higher requirements on energy supply, such as power distribution, air-conditioning temperature and humidity, air pressure, steam and the like, the minute-level real-time dynamic balance needs to be realized, and the energy waste is minimum while the energy supply is ensured to be stable. To meet such production requirements, not only the scheduling optimization is required in the workshop and the process, but also the scheduling optimization is required in the energy supply, and the capability of handling some emergencies (such as equipment damage, emergency order insertion, etc.) is required.
Partial job scheduling means that a job can be performed on only a part of the devices, which is more consistent with the production practice of a general enterprise, but has more constraints and is more complicated in solving. In energy supply, it is often required to achieve multiple objectives, to be able to adapt to emergency situations, similar to partial job scheduling, and the multi-objective, dynamic, partial flexible job scheduling problem is still difficult to find a good solution.
Disclosure of Invention
Aiming at the problems that power energy consumption and the like are not considered in production scheduling of an enterprise at present, the invention provides a multi-target energy supply and operation flexible scheduling method, which takes energy conservation and efficiency improvement as optimization targets, constructs flexible scheduling optimization indexes, particularly energy optimization indexes, applies an improved genetic algorithm, assists scheduling personnel to quickly generate a scheduling plan, and further generates an energy supply plan.
The specific technical scheme is as follows:
a multi-objective energy supply and operation flexible scheduling method comprises the following steps:
(1) determining an energy supply optimization index and energy supply constraint of flexible scheduling, and constructing a multi-objective energy supply optimization model; the objective functions of the multi-objective energy supply optimization model comprise an objective function with the minimum maximum completion time, an objective function with the minimum energy consumption maximum load and an objective function with the minimum total energy consumption load;
(2) solving the multi-target energy supply optimization model by adopting an improved genetic algorithm, so that an objective function with the minimum maximum completion time, an objective function with the minimum energy consumption maximum load and an objective function with the minimum total energy consumption load all reach the minimum value, and the optimal energy supply and operation flexible scheduling is obtained; the improved genetic algorithm adopts self-adaptive cross and mutation probability and adopts selection operation based on NSGA-II.
The flexible scheduling method considers the power energy consumption, takes the minimum maximum completion time, the minimum maximum energy consumption load and the minimum total energy consumption load as energy optimization indexes, adopts an improved genetic algorithm to assist the scheduling personnel to quickly generate a scheduling plan, further generates an energy supply plan, and realizes the energy conservation and the efficiency improvement of production.
The maximum completion time refers to the maximum time for a product to complete all the processing procedures, which represents the efficiency of production operation, and the energy supply scheduling also serves as the basic index for optimizing the production, and the objective function with the minimum maximum completion time is as follows:
wherein i represents a process code; n represents the number of processes; t is tiIndicating the maximum completion time required for process i.
An important aspect of energy supply is the reduction of waste, while the energy consumption load required for different processing steps is different. Without loss of generality, the energy sources of equipment in a certain process are considered to have processes of preparation, supply and withdrawal, the energy consumption load is gradually increased from 0 in the preparation process, the load is gradually reduced until the load is 0 in the withdrawal process, and the energy consumption load is kept unchanged in the supply process. The energy consumption load is as small and uniform as possible, the use efficiency of energy can be effectively improved, and the objective function with the minimum energy consumption maximum load is as follows:
wherein j represents a device code; m represents the number of devices; h represents the total number of process executions, hiRepresents the total number of execution of the step i; e.g. of the typeijhRepresents the energy required for step i; x is more than or equal to 0ijhLess than or equal to 1, energy equipment preparation stage, xijhGradual change from 0 to 1, energy device exit phase, xijhGradually changing from 1 to 0, and x when the energy equipment operates normallyijh＝1；xijhExpressed as:
since the demand of different energy sources is not necessarily the same for different facilities, the total energy consumption load varies according to the scheduling scheme. On the premise of meeting the requirements of minimum maximum completion time and minimum maximum energy consumption of equipment, if the total energy consumption load is allowed, the aim of saving energy can be achieved, and the objective function with the minimum total energy consumption load is as follows:
preferably, step (2) comprises:
(2-1) establishing a flexible scheduling chromosome code, wherein the chromosome comprises an equipment selection part and a process scheduling part; the length of the equipment selection part is the same as that of the procedure scheduling part, and the length is equal to the sum S of all procedures;
(2-2) generating the equipment selection part and the process scheduling part in a random generation mode to form an individual; generating N individuals to form an initial population P0(ii) a N is a preset constant;
(2-3) obtaining offspring Q through crossing and mutation operations0；
(2-4) adding P0And Q0Combining to obtain a population with the individual number of 2N;
(2-5) obtaining a fitness function of each individual in the population through a rapid non-dominated front-end sorting algorithm;
(2-6) executing natural selection process by using congestion degree comparison operator to obtain next generation population P with individual number N1(ii) a An elite mechanism is introduced in the selection process;
(2-7) iteratively executing the steps (2-3) - (2-6) until a preset termination condition is reached.
The preset termination condition may be set to reach a maximum number of iterations, and the search is stopped when the maximum number of iterations is reached.
In the aspect of global search, the invention uses an improved genetic algorithm to carry out global optimization; in the aspect of local search, a variable-field search algorithm is used for quickly changing a search area, so that local optimal solutions are prevented from being obtained by searching the local area; in the aspect of multi-objective optimization, a plurality of optimization objectives are comprehensively evaluated in a non-dominated sorting mode.
The method integrates the methods of multi-objective optimization, variable neighborhood search, tournament selection based on individual crowding distance, self-adaptive intersection, mutation probability and the like, can better improve the traditional operation scheduling capability of enterprises, improves the production efficiency and reduces the energy consumption.
Two main problems are studied due to flexible scheduling of energy supply: equipment selection and process scheduling. Equipment selection refers to determining an optional equipment set of each production task; the process scheduling refers to the sequence of the processes after the equipment is selected and the processing starting time. The supply of energy is mainly determined by equipment, process scheduling and process requirements, and a certain strategy is used. Therefore, the invention selects a device and process combined coding mode during chromosome coding, wherein the chromosome is divided into two sections, one section records device selection, and the other section records process scheduling.
Preferably, the step (2-1) includes:
(2-1a) numbering the selectable devices by using integers, wherein the integers refer to the device serial numbers to fill in the chromosome loci of the selected parts of the devices; arranging the chromosome genes in sequence according to the production task;
(2-1b) numbering the processes by using integers, wherein the occurrence times of the same process number are the total execution number of the process, and the integers are used for indicating the process number to fill in the chromosome locus of the process scheduling part; on the selected equipment, the processing is performed sequentially from left to right.
Preferably, in the step (2-2), the equipment selection part and the process scheduling part are both generated by a random generation method, and the individual equipment selection part and the process scheduling part comprise:
(2-2a) assume that there are n jobs w in total1,w2,…wnIs an operation w1The process for providing power energy comprises the following steps
For an operation w2The process for providing power energy comprises the following steps
The process for providing power energy for the operation wn comprises the following steps
The sum of all the steps of n operations is S, S ═ S1+S2+…Sn；
Setting an array A and an array B, wherein the length of each array is the sum S of all the procedures, and the array A stores the serial numbers of optional equipment of all the procedures from 1 to S
The array B randomly stores the numbers of all the working procedures, and the sequence of the working procedure numbers represents the execution sequence of the working procedures; initializing the array A and the array B to 0;
(2-2b) randomly extracting a device from the optional device set of the 1 st procedure of the operation 1 in the array A, and storing the serial number of the device into the 1 st position of the array A; randomly extracting a device from the optional device set of the 2 nd procedure of the operation 1, storing the serial number of the device in the 2 nd position of the array A, and sequentially operating until randomly extracting a device from the optional device set of the last 1 procedure of the operation n, and storing the serial number of the device in the S-th position of the array A;
(2-2c) randomly storing the working procedure numbers of the operation into each position of the array B on the basis of meeting the requirement of the product processing technology in the array B;
(2-2d) connecting the obtained array B to the tail part of the array A to form a chromosome code, namely an individual.
In order to generate a new individual, on the basis of meeting basic requirements (inheritance, completeness, non-redundancy and the like) of an intersection operator, uniform intersection is performed on an equipment selection part, and POX intersection of operation optimization sequences is performed on a procedure scheduling part.
The equipment selection part is uniformly crossed, so that the sequence among all the gene sites can be ensured not to be changed. The uniform crossing method comprises the following steps: a mask is randomly generated to determine how the offspring individuals obtain the gene from the parent individuals. The length of the mask is the same as the length of the individual gene string and is generated from 0, 1. For example, if the first digit of the mask word is 0, the first gene of the gene string of the first filial individual inherits the parent individual a, and the first gene of the gene string of the second filial individual inherits the parent individual B; if the first digit of the mask is 1, the first gene of the gene string of the first filial individual inherits the parent individual B, and the first gene of the gene string of the second filial individual inherits the parent individual A. And so on.
The process scheduling part carries out POX crossing, and the chromosomes p1 and p2 cross to generate two offspring c1 and c2, and the crossing process is as follows: 1) randomly partitioning the job set into two non-empty subsets o1 and o 2; 2) copying the processes belonging to the jobs in job set o1 in p1 to c1, copying the processes belonging to the jobs in job set o1 in p2 to c2, and keeping their positions; 3) the processes belonging to the jobs in job set o2 in p1 are copied to c2, and the processes belonging to the jobs in job set o2 in p2 are copied to c1, and their order is retained.
In order to enhance the local search capability of the genetic algorithm and combine the business characteristics of the flexible scheduling, preferably, in step (2-3), the mutation operation includes:
an apparatus selection section: randomly generating a plurality of variation points; aiming at the working procedure on each mutation point, selecting equipment with the shortest processing time in the corresponding optional equipment set, and if the processing time of the current equipment is shortest, selecting the next shortest equipment;
the process scheduling part: the method for searching variation based on the neighborhood comprises the following steps:
(a) randomly selecting m gene sites on chromosome c of the process scheduling part, and generating possible variation neighborhood N based on the gene sitesiI is 1,2, …, m is a preset variation neighborhood number;
(b-1) making i ═ 1;
(b-2) repeating the following process until i ═ m;
(b-2-1) resolving chromosome c in mutation neighborhood NiCarrying out sufficient variable neighborhood local search to obtain an optimal solution c';
(b-2-2) if c 'is better than c, then c ═ c', i ═ 1, otherwise, i ═ i + 1.
In crossover or mutation operations, crossover summaryRate p1Probability of variation p2The calculation formula of (2) is as follows:
wherein f ismaxIs the maximum fitness value in the population; f. ofavgMean fitness value in the population; f is the greater fitness value of the two individuals to be crossed; f' is the fitness value of the individual to be mutated; k is a radical of1、k2、k3、k4Are all constants.
The step (2-5) comprises the following steps: dividing the population P into front terminal population domination relations P having domination relations and not intersecting with each other according to the domination relations among individuals1＜p2＜…＜prAnd distributing selection probability to all individuals according to the crowding distance according to the sequencing result. Crowding distance d of each front-end internal unit qqThe calculation formula is as follows:
wherein n isoThe number of the objective functions is expressed, and the method uses three objective functions, namely, the maximum completion time is minimum, the maximum energy consumption of equipment is minimum, and the total energy consumption load is kept minimum. dqIs the crowding distance of the front end individual q, dq+1、dq-1Respectively, the crowding distance of the nearest neighbor of individual q.
In the step (2-6), firstly, all solutions of the optimal non-dominated curved surface are reserved, if the number of the solutions is smaller than N, the solution of the second non-dominated curved surface is continuously reserved, and the like is performed until all the solutions of the non-dominated curved surface cannot be reserved, namely the total number of the reserved solutions is larger than N, a congestion comparison operator is used for selecting the solution with the better fitness function value until the total number reaches N, and the selection process is completed.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention considers the problem of power energy consumption, and adopts an improved genetic algorithm to search the optimal flexible scheduling according to the energy optimization index so as to realize the purposes of energy conservation and efficiency improvement;
(2) the genetic algorithm can carry out global optimization on complex engineering problems by simulating the living competition process of organisms in the nature and utilizing operators such as selection, intersection, mutation and the like. However, the local search capability of the traditional genetic algorithm is limited, and the traditional genetic algorithm is easy to fall into a local optimal solution in the search process. The method improves the traditional genetic algorithm, on one hand, the method is suitable for multi-target optimization solution in a target problem, and the individuals in the genetic population are subjected to non-dominated sorting according to the crowding distance, so that the production time requirement and the energy-saving requirement are met; on the other hand, the local search capability is improved by combining the variable neighborhood search algorithm, and the algorithm performance is improved.
Drawings
FIG. 1 is a schematic flow diagram of an improved genetic algorithm;
FIG. 2 is a schematic representation of the coding structure of chromosomes;
fig. 3 is a schematic diagram of a process for generating a new population by a selection operation.
Detailed Description
The invention will be described in further detail below with reference to the drawings and examples, which are intended to facilitate the understanding of the invention without limiting it in any way.
1. On the basis of constructing flexible scheduling optimization indexes and energy supply constraints and establishing an energy supply flexible scheduling mathematical model, an improved genetic algorithm is designed, the flow of the improved genetic algorithm is shown in figure 1, and the steps are as follows:
(1) flexible scheduled chromosome coding and decoding is established.
(2) And initializing the flexible scheduling population.
(3) A tournament selection procedure based on the NSGA-ii method was performed.
(4) A chromosome crossing operation is performed.
(5) Carrying out chromosome mutation operation.
(6) And judging whether a termination condition is reached.
2. Establishing an energy supply flexible scheduling optimization index, comprising the following steps:
1) the maximum completion time is minimal. The maximum completion time refers to the maximum time for a product to complete all its processing procedures, and represents the efficiency of production operation, and the energy supply scheduling also serves as the basic index of this production optimization, which can be expressed as:
wherein i represents a process code, tiIndicating the maximum completion time required for process i.
2) The maximum load of energy consumption is minimum. An important aspect of energy supply is the reduction of waste, while the energy consumption load required for different processing steps is different. Without loss of generality, the energy sources of equipment in a certain process are considered to have processes of preparation, supply and withdrawal, the energy consumption load is gradually increased from 0 in the preparation process, the load is gradually reduced until the load is 0 in the withdrawal process, and the energy consumption load is kept unchanged in the supply process. Through making energy consumption load as little as possible and even, can effectively improve the availability factor of the energy, can show as:
wherein j represents the device code, m represents the number of devices, h represents the number of processesiIndicates the number of steps required for step i, eijhRepresents the energy required for step i; x is more than or equal to 0ijhLess than or equal to 1, energy equipment preparation stage, xijhGradual change from 0 to 1, energy device exit phase, xijhSlowly from 1 to 0; when the energy equipment is in normal operation, xijh＝1；
Wherein, a0、b0、a1、b1The method is an inherent property of the energy equipment and represents a linear constant of the numerical change of the simulated load in the starting or exiting stage of the energy equipment.
3) The total energy consumption load is minimal. Since the demand of different energy sources is not necessarily the same for different facilities, the total energy consumption load varies according to the scheduling scheme. On the premise of meeting the requirements of minimum maximum completion time and minimum maximum energy consumption of equipment, if the total energy consumption load is minimized, the purpose of energy saving can be achieved, and the method can be expressed as follows:
3. the method is a composite method, and in the aspect of global search, an improved genetic algorithm is used for global optimization; in the aspect of local search, a variable-field search algorithm is used for quickly changing a search area, so that local optimal solutions are prevented from being obtained by searching the local area; in the aspect of multi-objective optimization, a plurality of optimization objectives are comprehensively evaluated in a non-dominated sorting mode.
4. Establishing a flexible scheduling chromosome coding scheme
Two main problems are studied due to energy supply scheduling: equipment selection and process scheduling. Equipment selection refers to determining an optional equipment set of each production task; the process scheduling refers to the sequence of the processes after the equipment is selected and the processing starting time. The supply of energy is mainly determined by equipment, process scheduling and process requirements, and a certain strategy is used. For this purpose, a device and process joint coding mode is selected during chromosome coding, and for this purpose, a chromosome coding scheme is divided into two segments as shown in fig. 2, one segment records device selection, one segment records process scheduling:
1) the equipment selection part has a length S (sum of process). Since the number of devices that can be selected per process is limited, the set of selectable devices is set to MoTherefore, the equipment in the equipment set can be coded by integers, the serial number of the equipment is referred to by the integers, and the chromosomal gene position is filled in. DeviceEach locus of a part of the chromosome corresponds to a process, the order of which follows a fixed order of processes.
2) The process schedule portion is also S long. The process number is expressed by an integer, and the occurrence number of the same process number is the total execution number h of the processiAnd processing on the selected equipment from left to right in sequence.
5. Population initialization
In order to avoid the influence of the initial population with low quality on the algorithm execution speed and quality, aiming at the characteristics of chromosome coding adopted by the method, the initial population is generated by adopting a mode of randomly generating equipment selection and process scheduling. The method specifically comprises the following steps:
1) suppose there are n jobs w in total1,w2,…wnIs an operation w1The process for providing power energy comprises the following steps
For an operation w2The process for providing power energy comprises the following steps
The sum of all the steps of n jobs is S, i.e. S ═ S1+S2+…Sn. Setting an array A and an array B, wherein the length of each array is the sum S of all procedures, and the array A stores sequence numbers of optional equipment of all procedures from 1 to S
The array B randomly stores all the working procedures, and the sequence of the working procedures represents the execution sequence of the working procedures; initializing the array A and the array B to 0;
2) in array A, optional equipment set M from the 1 st process of job 1oAnd randomly extracting an equipment serial number and storing the equipment serial number in the 1 st position.
3) And repeating the step 2) until the equipment of the last 1 procedure of the operation n is randomly selected and the equipment serial number is stored in the S-th position of the array A.
4) And in the array B, the working procedure numbers of the operation are randomly stored in each position on the basis of meeting the requirement of the product processing technology.
5) And (5) repeating the step 4) until all the operations and procedures are stored, and then connecting the data B to the tail part of the data A to form a chromosome code.
6. A chromosome crossing operation is performed.
In order to generate a new individual, uniform crossing is performed on the equipment selection part and crossing based on the operation priority is performed on the process scheduling part on the basis of meeting basic requirements (inheritance, completeness, non-redundancy and the like) of a crossing operator.
1) The equipment selection part is uniformly crossed, so that the sequence among all the gene sites can be ensured not to be changed. The uniform crossing method comprises the following steps: a mask is randomly generated to determine how the offspring individuals obtain the gene from the parent individuals. The length of the mask is the same as the length of the individual gene string and is generated from 0, 1. For example, if the first digit of the mask word is 0, the first gene of the gene string of the first filial individual inherits the parent individual a, and the first gene of the gene string of the second filial individual inherits the parent individual B; if the first digit of the mask is 1, the first gene of the gene string of the first filial individual inherits the parent individual B, and the first gene of the gene string of the second filial individual inherits the parent individual A. And so on.
2) The process scheduling part carries out POX crossing, and the chromosomes p1 and p2 cross to generate two offspring c1 and c2, and the crossing process is as follows: 1) randomly partitioning the job set into two non-empty subsets o1 and o 2; 2) copying the processes belonging to the jobs in job set o1 in p1 to c1, copying the processes belonging to the jobs in job set o1 in p2 to c2, and keeping their positions; 3) the processes belonging to the jobs in job set o2 in p1 are copied to c2, and the processes belonging to the jobs in job set o2 in p2 are copied to c1, and their order is retained.
7. Carrying out chromosome mutation operation.
In order to enhance the local search capability of the genetic algorithm, the following variation modes are adopted by combining the flexible scheduling business characteristics:
1) an apparatus selection section: randomly generating n variation points; aiming at the working procedure on each mutation point, corresponding to the optional equipment set MoAnd selecting the equipment number with the shortest processing time, and selecting the next shortest equipment if the current equipment has the shortest processing time.
2) The process scheduling part: the method adopts a neighborhood search based mutation mode, and specifically comprises the following steps:
a. randomly selecting m gene sites on the chromosome c of the process scheduling part, and generating a possible variation neighborhood N based on the gene sitesi，i＝1,2,…,m；
b. The following process is repeated until the algorithm terminates:
b-1 making i ═ 1;
b-2 repeating the following process until i ═ m;
b-2-1 solution c in neighborhood NiCarrying out sufficient variable neighborhood local search to obtain an optimal solution c';
b-2-2 if c 'is better than c, then c ═ c', i ═ 1, otherwise i ═ i + 1.
8. Adaptive crossover and mutation probabilities
Crossover probability p in the crossover and mutation operations described above1Probability of variation p2Adopting a self-adaptive method, namely when the fitness of individuals in the population tends to be consistent or close to local optimum, increasing p1And p2To avoid local optimality; conversely, when the individual fitness value difference is larger, p is reduced1And p2So that better individuals are retained. Differential crossover probabilities p are also used for different individuals1And the probability of variation p2Namely, the individuals with fitness lower than the average value select higher probability value, and the individuals with fitness higher than the average value select lower probability value, so as to accelerate the generation speed of excellent individuals. p is a radical of1And p2The specific calculation expression is as follows:
wherein the content of the first and second substances,fmaxis the maximum fitness value in the population, favgIs the average fitness value in the population, f is the greater fitness value of the two individuals to be crossed, f' is the fitness value of the individual to be mutated, k1、k2、k3、k4Are all constants. Cross probability p1Generally, the value is between 0.4 and 0.99, and the specific value is the operation effect of the algorithm. When f is<favgWhen is p1＝k2Thus k is2Also between 0.4 and 0.99; f is not less than favgWhen is p1Is k1More than 1 times of, k1Should be less than or equal to p1And the effect of reducing the cross probability is achieved. The variation probability is generally between 0.0001 and 0.1, and it can be known that k4Should also be between 0.0001-0.1, k3And is smaller. The setting needs trial and error.
9. And (4) carrying out chromosome selection operation.
This step employs an NSGA-II based tournament selection procedure that preserves chromosomal diversity. First, define PiIs the i-th generation population, and P0The initial population is the number of individuals in the population N, and QiFor the ith progeny produced by the genetic algorithm, the number is also N. The algorithm process is shown in fig. 3 and is described in detail as follows:
1) randomly generating an initial generation population P0The number of individuals is N;
2) obtaining filial generation Q through cross operator and genetic operator0；
3) Merging population P0And Q0And obtaining the population with the number of 2N.
4) And (4) solving the fitness function of each individual in the population through a rapid non-dominant front-end sequencing algorithm. Dividing the population P into front terminal population domination relations P having domination relations and not intersecting with each other according to the domination relations among individuals1＜p2＜…＜prAnd distributing selection probability to all individuals according to the crowding distance according to the sequencing result.
5) The natural selection process is performed by a congestion comparison operator. Meanwhile, an elite mechanism is introduced in the selection process, and the non-dominant curved surface arranged in the front is reserved. Specifically, all solutions of the optimal non-dominated curved surface are reserved firstly, if the number of the solutions is smaller than N, the solution of the second non-dominated curved surface is reserved continuously, and the like is performed until all the solutions of the non-dominated curved surface cannot be reserved, namely the total number of the reserved solutions is larger than N, a congestion comparison operator is used for selecting the solution with the better fitness function value until the total number reaches N, and the selection process is completed. The calculation formula of the crowding distance d between the internal individuals of each front end is as follows:
wherein n isoRepresenting the number of objective functions.
6) Obtaining next generation population P with the number of individuals of N1；
7) And (5) repeatedly executing the steps of 2-6 according to the specified evolution times of the model until the algorithm is completed.
10. Termination criteria
The method adopts the maximum iteration number as a termination criterion, namely, the searching is stopped when the algorithm evolves to a certain iteration number.
The above-mentioned embodiments are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only specific embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions, equivalents, etc. made within the scope of the principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A multi-objective energy supply and operation flexible scheduling method is characterized by comprising the following steps:
(1) determining an energy supply optimization index and energy supply constraint of flexible scheduling, and constructing a multi-objective energy supply optimization model; the objective functions of the multi-objective energy supply optimization model comprise an objective function with the minimum maximum completion time, an objective function with the minimum energy consumption maximum load and an objective function with the minimum total energy consumption load;
(2) solving the multi-target energy supply optimization model by adopting an improved genetic algorithm, so that an objective function with the minimum maximum completion time, an objective function with the minimum energy consumption maximum load and an objective function with the minimum total energy consumption load all reach the minimum value, and the optimal energy supply and operation flexible scheduling is obtained; the improved genetic algorithm adopts self-adaptive cross and mutation probability and adopts selection operation based on NSGA-II.
2. The multi-objective energy supply and job flexible scheduling method of claim 1, wherein the objective function with the minimum maximum completion time is:
wherein i represents a process code; s represents the number of processes; t is tiIndicating the maximum completion time required for process i.
3. The multi-objective flexible scheduling method for energy supply and operation of claim 2, wherein the objective function with the least energy consumption and the least load is:
wherein j represents a device code; m represents the number of devices; h represents the total number of process executions, hiRepresents the total number of execution of the step i; e.g. of the typeijhRepresents the energy required for step i; x is more than or equal to 0ijhLess than or equal to 1, energy equipment preparation stage, xijhGradual change from 0 to 1, energy device exit phase, xijhGradually changing from 1 to 0, and x when the energy equipment operates normallyijh＝1。
4. The multi-objective energy supply and job flexible scheduling method of claim 3, wherein the objective function with the minimum total energy consumption load is:
5. the multi-objective energy supply and job flexible scheduling method of claim 1, wherein step (2) comprises:
(2-1) establishing a flexible scheduling chromosome code, wherein the chromosome comprises an equipment selection part and a process scheduling part; the length of the equipment selection part is the same as that of the procedure scheduling part, and the length is equal to the sum S of all procedures;
(2-2) generating the equipment selection part and the process scheduling part in a random generation mode to form an individual; generating N individuals to form an initial population P0(ii) a N is a preset constant;
(2-3) obtaining offspring Q through crossing and mutation operations0；
(2-4) adding P0And Q0Combining to obtain a population with the individual number of 2N;
(2-5) obtaining a fitness function of each individual in the population through a rapid non-dominated front-end sorting algorithm;
(2-6) executing natural selection process by using congestion degree comparison operator to obtain next generation population P with individual number N1(ii) a An elite mechanism is introduced in the selection process;
(2-7) iteratively executing the steps (2-3) - (2-6) until a preset termination condition is reached.
6. The multi-objective energy supply and job flexible scheduling method according to claim 5, wherein the step (2-1) comprises:
(2-1a) numbering the selectable devices by using integers, wherein the integers refer to the device serial numbers to fill in the chromosome loci of the selected parts of the devices; arranging the chromosome genes in sequence according to the production task;
(2-1b) numbering the processes by using integers, wherein the occurrence times of the same process number are the total execution number of the process, and the integers are used for indicating the process number to fill in the chromosome locus of the process scheduling part; on the selected equipment, the processing is performed sequentially from left to right.
7. The multi-objective energy supply and job flexible scheduling method according to claim 5, wherein in the step (2-2), the equipment selection part and the process scheduling part are generated by random generation, and constitute an individual, including:
(2-2a) assume that there are n jobs w in total1，w2，...wnIs an operation w1The process for providing power energy comprises the following steps
For an operation w2The process for providing power energy comprises the following steps
For an operation wnThe process for providing power energy comprises the following steps
The sum of all the steps of n operations is S, S ═ S1+S2+…Sn；
Setting an array A and an array B, wherein the length of each array is the sum S of all the procedures, and the array A stores the serial numbers of optional equipment of all the procedures from 1 to S
The array B randomly stores the sequence numbers of all the working procedures, and the sequence of the working procedure sequence numbers represents the execution sequence of the working procedures; initializing the array A and the array B to 0;
(2-2b) randomly extracting a device from the optional device set of the 1 st procedure of the operation 1 in the array A, and storing the serial number of the device into the 1 st position of the array A; randomly extracting a device from the optional device set of the 2 nd procedure of the operation 1, storing the serial number of the device in the 2 nd position of the array A, and sequentially operating until randomly extracting a device from the optional device set of the last 1 procedure of the operation n, and storing the serial number of the device in the S-th position of the array A;
(2-2c) randomly storing the working procedure numbers of the operation into each position of the array B on the basis of meeting the requirement of the product processing technology in the array B;
(2-2d) connecting the obtained array B to the tail part of the array A to form a chromosome code, namely an individual.
8. The multi-objective energy supply and job flexible scheduling method according to claim 5, wherein in the step (2-3), the uniform crossing is performed on the equipment selection part, and the POX crossing of the job preference order is performed on the process scheduling part.
9. The method for multi-objective flexible scheduling of energy supply and operations of claim 5, wherein the mutation operations in steps (2-3) comprise:
an apparatus selection section: randomly generating a plurality of variation points; aiming at the working procedure on each mutation point, selecting equipment with the shortest processing time in the corresponding optional equipment set, and if the processing time of the current equipment is shortest, selecting the next shortest equipment;
the process scheduling part: the method for searching variation based on the neighborhood comprises the following steps:
(a) randomly selecting m gene sites on chromosome c of the process scheduling part, and generating possible variation neighborhood N based on the gene sitesiI is 1,2, and m is a preset variation neighborhood number;
(b-1) making i ═ 1;
(b-2) repeating the following process until i ═ m;
(b-2-1) resolving chromosome c in mutation neighborhood NiCarrying out sufficient variable neighborhood local search to obtain an optimal solution c';
(b-2-2) if c 'is better than c, then c ═ c', i ═ 1, otherwise, i ═ i + 1.
10. The method for flexible scheduling of energy supply and operation of multiple targets of claim 8 or 9, wherein the crossover probability p is determined by crossover operation or mutation operation1Probability of variation p2The calculation formula of (2) is as follows:
wherein f ismaxIs the maximum fitness value in the population; f. ofavgMean fitness value in the population; f is the greater fitness value of the two individuals to be crossed; f' is the fitness value of the individual to be mutated; k is a radical of1、k2、k3、k4Are all constants.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011146645.8A CN112348323A (en) | 2020-10-23 | 2020-10-23 | Multi-target energy supply and operation flexible scheduling method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011146645.8A CN112348323A (en) | 2020-10-23 | 2020-10-23 | Multi-target energy supply and operation flexible scheduling method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112348323A true CN112348323A (en) | 2021-02-09 |
Family
ID=74360008
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011146645.8A Pending CN112348323A (en) | 2020-10-23 | 2020-10-23 | Multi-target energy supply and operation flexible scheduling method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112348323A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115222243A (en) * | 2022-07-18 | 2022-10-21 | 中建八局建筑科技(山东)有限公司 | Self-adaptive rescheduling method and system for door and window automatic production line |
| CN116629578A (en) * | 2023-07-11 | 2023-08-22 | 工业富联(佛山)创新中心有限公司 | Method and system for calculating scheduling of manufacturing factory |
| WO2024104003A1 (en) * | 2022-11-16 | 2024-05-23 | 上海宝信软件股份有限公司 | Cpu parallel acceleration method and system suitable for intelligent scheduling system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107451747A (en) * | 2017-08-08 | 2017-12-08 | 大连交通大学 | Job-Shop system and its method of work based on adaptive non-dominant genetic algorithm |
| CN110543151A (en) * | 2019-08-12 | 2019-12-06 | 陕西科技大学 | Method for solving workshop energy-saving scheduling problem based on improved NSGA-II |
| CN110619437A (en) * | 2019-09-20 | 2019-12-27 | 国网江苏省电力有限公司电力科学研究院 | Low-energy-consumption flexible job shop scheduling method |
-
2020
- 2020-10-23 CN CN202011146645.8A patent/CN112348323A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107451747A (en) * | 2017-08-08 | 2017-12-08 | 大连交通大学 | Job-Shop system and its method of work based on adaptive non-dominant genetic algorithm |
| CN110543151A (en) * | 2019-08-12 | 2019-12-06 | 陕西科技大学 | Method for solving workshop energy-saving scheduling problem based on improved NSGA-II |
| CN110619437A (en) * | 2019-09-20 | 2019-12-27 | 国网江苏省电力有限公司电力科学研究院 | Low-energy-consumption flexible job shop scheduling method |
Non-Patent Citations (3)
| Title |
|---|
| 吕琳: "基于改进的自适应多目标遗传算法的研究及其应用", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 廖芹 等: "《数据挖掘与数学建模》", 28 February 2010, 北京：国防工业出版社 * |
| 张国辉: "柔性作业车间调度方法研究", 《中国博士学位论文全文数据库 基础科学辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115222243A (en) * | 2022-07-18 | 2022-10-21 | 中建八局建筑科技(山东)有限公司 | Self-adaptive rescheduling method and system for door and window automatic production line |
| WO2024104003A1 (en) * | 2022-11-16 | 2024-05-23 | 上海宝信软件股份有限公司 | Cpu parallel acceleration method and system suitable for intelligent scheduling system |
| CN116629578A (en) * | 2023-07-11 | 2023-08-22 | 工业富联(佛山)创新中心有限公司 | Method and system for calculating scheduling of manufacturing factory |
| CN116629578B (en) * | 2023-07-11 | 2024-03-22 | 工业富联(佛山)创新中心有限公司 | Method and system for calculating scheduling of manufacturing factory |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112348323A (en) | Multi-target energy supply and operation flexible scheduling method | |
| Zolfaghari et al. | Comparative study of simulated annealing, genetic algorithms and tabu search for solving binary and comprehensive machine-grouping problems | |
| CN114186749B (en) | Flexible workshop scheduling method and model based on reinforcement learning and genetic algorithm | |
| CN110598920A (en) | Multi-objective optimization method and system for main production plan of casting parallel workshop | |
| CN101118611A (en) | Business process model resource configuring optimizing method based on inheritance algorithm | |
| CN110956371B (en) | Green scheduling optimization method for intelligent manufacturing workshop facing complex man-machine coupling | |
| CN114565239B (en) | Comprehensive low-carbon energy scheduling method and system for industrial park | |
| CN114266509A (en) | Flexible job shop scheduling method for solving by random greedy initial population genetic algorithm | |
| CN112163808B (en) | Method for solving logistics center addressing problem by self-adaptive whale algorithm based on opponent learning | |
| CN110531716A (en) | The method for solving low-carbon Job-Shop problem based on discrete whale algorithm | |
| CN113569483A (en) | Method for solving multi-target flexible job shop scheduling based on artificial bee colony algorithm | |
| CN117075545A (en) | Multi-target flexible job shop scheduling method, electronic equipment and medium | |
| CN116560313A (en) | Genetic algorithm optimization scheduling method for multi-objective flexible job shop problem | |
| CN114021934A (en) | Method for solving workshop energy-saving scheduling problem based on improved SPEA2 | |
| CN114282370A (en) | Disassembly line setting method considering physical and mental loads of operator | |
| CN109102203A (en) | A kind of Target Assignment optimization method based on more string chromosomal inheritance algorithms | |
| CN117495052A (en) | Multi-agricultural machine multi-task scheduling method driven by reinforcement learning and genetic algorithm fusion | |
| CN116245346B (en) | Multi-unmanned aerial vehicle task allocation method based on multiple swarm genetic algorithms and local search | |
| CN110889534A (en) | Product principle arrangement method based on improved NSGA-II | |
| CN105894034A (en) | Commercial load characteristic clustering analysis method based on quadratic clustering | |
| CN116307214A (en) | Automatic cotton distribution method based on NSGA-II algorithm and related device | |
| CN114089699B (en) | Method for solving mixed flow shop scheduling based on cell type membrane calculation model | |
| CN114879618A (en) | Knowledge-driven workshop intelligent scheduling method | |
| CN115496322A (en) | Distributed flow shop scheduling method and device | |
| CN113901728A (en) | Computer second-class assembly line balance optimization method based on migration genetic algorithm |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210209 |
|
| RJ01 | Rejection of invention patent application after publication |