CN113298313A - Flexible job shop scheduling method and system based on genetic algorithm - Google Patents
Flexible job shop scheduling method and system based on genetic algorithm Download PDFInfo
- Publication number
- CN113298313A CN113298313A CN202110645518.0A CN202110645518A CN113298313A CN 113298313 A CN113298313 A CN 113298313A CN 202110645518 A CN202110645518 A CN 202110645518A CN 113298313 A CN113298313 A CN 113298313A
- Authority
- CN
- China
- Prior art keywords
- semi
- sequence
- finished product
- product
- 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.)
- Withdrawn
Links
- 238000000034 method Methods 0.000 title claims abstract description 80
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 51
- 230000002068 genetic effect Effects 0.000 title claims abstract description 40
- 239000000047 product Substances 0.000 claims abstract description 127
- 239000011265 semifinished product Substances 0.000 claims abstract description 94
- 238000004519 manufacturing process Methods 0.000 claims abstract description 86
- 238000012545 processing Methods 0.000 claims abstract description 23
- 239000004615 ingredient Substances 0.000 claims description 23
- 238000013468 resource allocation Methods 0.000 claims description 19
- 238000003860 storage Methods 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 12
- 238000012163 sequencing technique Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 3
- 230000006855 networking Effects 0.000 claims 1
- 230000006872 improvement Effects 0.000 abstract description 3
- 238000004364 calculation method Methods 0.000 abstract description 2
- 238000005111 flow chemistry technique Methods 0.000 abstract description 2
- 230000008569 process Effects 0.000 description 41
- 238000010586 diagram Methods 0.000 description 11
- 230000006870 function Effects 0.000 description 5
- 230000001174 ascending effect Effects 0.000 description 4
- 238000010923 batch production Methods 0.000 description 4
- 239000012634 fragment Substances 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 238000009826 distribution Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000012797 qualification Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 239000004065 semiconductor 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- 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
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Evolutionary Biology (AREA)
- Development Economics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Genetics & Genomics (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Physiology (AREA)
- Educational Administration (AREA)
- Manufacturing & Machinery (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a flexible job shop scheduling method and a system based on a genetic algorithm, wherein the method comprises the following steps: acquiring a task sequence for producing one or more products and an optimal scheduling sequence of semi-finished product operation corresponding to each product; constructing a scheduling model taking the shortest production time as a target according to the task sequence and the optimal scheduling sequence of the semi-finished product operation corresponding to each product; and determining the optimal scheduling scheme of the scheduling model by utilizing a genetic algorithm. According to the method, individuals are randomly generated according to the product sequence and form a population, filial generations are generated according to combinations in a parent population to realize iteration or improvement of the product sequence, and the splitting combination calculation of three processing modes, namely semi-finished product batch processing, semi-finished product flow processing and final products, is performed by using a greedy algorithm, so that the quality of the solution of the greedy algorithm is improved, and the production efficiency of flexible job shop scheduling is further improved.
Description
Technical Field
The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a flexible job shop scheduling method and system based on a genetic algorithm.
Background
With the rapid development of artificial intelligence and cloud computing technology, the manufacture or production of traditional products is moving forward to intellectualization and flexibility. The workshop scheduling is taken as a key link of intelligent manufacturing, and the main task is to find out an optimal scheduling scheme under various resources or constraint conditions through an intelligent optimization algorithm, so that the production efficiency is improved, the cost is reduced, and the competitiveness of an enterprise is improved. People mainly aim at the production plan of industrial engineering in the research of workshop scheduling problems, and in the scheduling of different properties, the requirements on processing constraints and processing technologies in the processing process are different.
Therefore, the final goal of workshop scheduling is to reasonably distribute and arrange parts to be processed or products with different forms according to the requirements of industrial engineering production, so as to achieve the expected goal and obtain the optimal performance index. In the research of production scheduling problem, it is necessary to meet the constraint requirements (such as processing route, delivery date, resource usage) in the production process as much as possible. The resources involved in the production schedule in the plant are raw materials, processing equipment, manpower, capital, energy, storage and transportation, etc. The detailed allocation of resources in the production process is limited by the production process, and the factors influencing the workshop scheduling problem are many.
On the other hand, the flexible production line (system) is mainly used for adapting to the situations that the types of orders are various, the batches are small, and the products are continuously and iteratively upgraded. The flexibility of the flexible production line can produce different products in a shortest time in a switching way. Therefore, on the premise of flexible production, higher requirements are undoubtedly put forward on workshop scheduling.
Disclosure of Invention
In order to optimize the production process of the product, fully schedule the production resources, improve the production efficiency and meet the requirement of flexible production, the invention provides a flexible job shop scheduling method based on a genetic algorithm in a first aspect, which comprises the following steps: acquiring a task sequence for producing one or more products and an optimal scheduling sequence of semi-finished product operation corresponding to each product; constructing a scheduling model taking the shortest production time as a target according to the task sequence and the optimal scheduling sequence of the semi-finished product operation corresponding to each product; and determining the optimal scheduling scheme of the scheduling model by utilizing a genetic algorithm.
In some embodiments of the present invention, the optimal scheduling sequence of the semi-finished job corresponding to each product is obtained by: determining the production quantity of the semi-finished products according to the production batch information of the semi-finished products corresponding to each product; determining the ingredients of the semi-finished products according to the production quantity of the semi-finished products; determining a feasible resource allocation scheme and an operation sequence set according to a semi-finished product ingredient processing mode, wherein the resource allocation comprises personnel allocation and equipment allocation required by semi-finished product ingredient production; and searching the optimal scheduling sequence of the semi-finished product operation corresponding to each product from the resource allocation scheme and the operation sequence set by using a greedy algorithm.
Further, the determining a feasible resource allocation scheme and an operation sequence set according to the processing mode of the semi-finished product ingredients comprises the following steps:
if the semi-finished product ingredients are processed in a streaming way, determining a feasible resource allocation scheme and an operation sequence set in a tree searching way; and if the semi-finished product ingredients are processed in batch, determining a feasible resource allocation scheme and an operation sequence set by taking the maximum parallel batch number and the total production number as constraint conditions.
In some embodiments of the present invention, the determining the optimal scheduling scheme of the scheduling model using a genetic algorithm comprises: clustering and sequencing one or more products belonging to the same task sequence according to the latest finishing time of each product production to obtain the working serial number and the corresponding batch of each product; randomly generating finished product sequences according to a plurality of working serial numbers belonging to the same batch, taking each finished product sequence as an individual, and taking a plurality of individuals of the same task sequence as a population; and calculating the total production time required by the optimal scheduling sequence of each finished product sequence corresponding to the semi-finished product operation, and determining the global optimal solution of the population.
Further, the determining a global optimal solution of the population according to the total production time corresponding to the optimal scheduling sequence of the semi-finished product job corresponding to each finished product sequence comprises the following steps:
if the population does not have the optimal solution, then: calculating the total production time required by the optimal scheduling sequence of the semi-finished product operation corresponding to each finished product sequence, and screening optimal individuals and suboptimal individuals from the finished product sequences; generating a plurality of filial generation individuals according to the optimal individual and the suboptimal individual until the number of the filial generation individuals reaches the upper limit of the population; and repeating the steps until the optimal solution of the population appears.
In the above embodiment, the goal of the scheduling model further includes the shortest computation time.
The invention provides a flexible job shop scheduling system based on a genetic algorithm, which comprises an acquisition module, a construction module and a determination module, wherein the acquisition module is used for acquiring a task sequence for producing one or more products and an optimal scheduling sequence of semi-finished job corresponding to each product; the construction module is used for constructing a scheduling model taking the shortest production time as a target according to the task sequence and the optimal scheduling sequence of the semi-finished product operation corresponding to each product; the determining module is used for determining the optimal scheduling scheme of the scheduling model by utilizing a genetic algorithm.
Further, the determining module comprises a sorting unit, a generating unit and a calculating unit, wherein the sorting unit is used for clustering and sorting one or more products belonging to the same task sequence according to the latest finishing time of each product production to obtain the working serial number and the corresponding batch of each product; the generating unit is used for randomly generating finished product sequences according to a plurality of working serial numbers belonging to the same batch, taking each finished product sequence as an individual, and taking a plurality of individuals of the same task sequence as a population; and the computing unit is used for computing the total production time required by the optimal scheduling sequence of the semi-finished product operation corresponding to each finished product sequence and determining the global optimal solution of the population.
In a third aspect of the present invention, there is provided a flexible production apparatus comprising: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the flexible job shop scheduling method based on genetic algorithm provided by the present invention in the first aspect.
In a fourth aspect of the present invention, a computer readable medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the flexible job shop scheduling method based on genetic algorithm provided in the first aspect of the present invention.
The invention has the beneficial effects that:
1. according to the invention, individuals are generated according to random generation of a product sequence to form a population, and filial generations are generated according to pairwise combination in the population to realize change improvement of the product sequence;
2. the greedy algorithm for obtaining the product sequence adopts the split combination calculation of three processing modes of semi-finished product batch processing, semi-finished product flow processing and final products, and meanwhile tree search is properly used, so that the greedy process can consider the global property of the local optimal solution more, and the quality of the greedy algorithm solution is improved.
Drawings
FIG. 1 is a basic flow diagram of a flexible job shop scheduling method based on genetic algorithms in some embodiments of the present invention;
FIG. 2 is a detailed flow diagram of S100 of a flexible job shop scheduling method based on a genetic algorithm in some embodiments of the present invention;
FIG. 3 is a detailed flowchart of step S300 of a flexible job shop scheduling method based on a genetic algorithm in some embodiments of the present invention;
FIG. 4 is a schematic diagram of a population generation process in a flexible job shop scheduling method based on a genetic algorithm according to some embodiments of the present invention;
FIG. 5 is a second schematic diagram illustrating a population generation process in the flexible job shop scheduling method based on genetic algorithm according to some embodiments of the present invention;
FIG. 6 is a schematic diagram illustrating the generation of an optimal scheduling scenario in a flexible job shop scheduling method based on genetic algorithms according to some embodiments of the present invention;
FIG. 7 is a schematic diagram of a flexible job shop scheduling system based on genetic algorithms in some embodiments of the present invention;
FIG. 8 is a schematic diagram of a flexible production facility for flexible job shop scheduling based on genetic algorithms in some embodiments of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Example 1
Referring to fig. 1, in a first aspect of the present invention, there is provided a flexible job shop scheduling method based on a genetic algorithm, including: s100, acquiring a task sequence for producing one or more products and an optimal scheduling sequence of semi-finished product operation corresponding to each product; s200, constructing a scheduling model taking the shortest production time as a target according to the task sequence and the optimal scheduling sequence of the semi-finished product operation corresponding to each product; s300, determining the optimal scheduling scheme of the scheduling model by using a genetic algorithm.
In step S100 of some embodiments of the present invention, obtaining an optimal scheduling sequence of a task sequence including production of one or more products and a semi-finished job corresponding to each product includes: an input data set inst and parameters param are obtained.
In order to improve the relevance of data and reduce the data import time, an input data set inst and a parameter param are divided into five files for describing an order (aps _ order.csv), a commodity (aps _ semi _ product.csv), a semi-finished product process (aps _ process.csv), a resource (aps _ resource.csv) and a resource (aps _ process _ resource.csv) required by the process correspondence. In order to simplify the scheduling model, the following assumptions are made: workers and resources only work for 8 hours a day, and the shift of the workers is not considered; the process execution time is only related to the process and is not related to the used resources; no dependency between resources, e.g. the use of a device must require B person; the utilization rate of the equipment is not set, and the utilization rate of all the equipment is 100 percent. Wherein:
1. the order file describes order details such as a commodity ID (product ID), a commodity quantity (product quantity), an order earliest start time, an order latest end time, and the like contained in an order, and one line of data is a certain commodity information order _ ID required for placing a certain order: numbering the order; description: order description product _ id: a commodity D describing a certain commodity placed in the order; product _ amount: number of items , the amount earliest _ start _ time required for the order to place the item: the earliest start time of the order, latest end _ time: at the latest end of the order, all of the items in the order need to be produced completely before this point in time.
2. The product file (aps _ semi _ product.csv) describes which semi-finished products a product is composed of, wherein the semi-finished products having the same name as the product ID are used to indicate the assembly step, and a row of data is information of a certain semi-finished product required under a certain product. For example: product _ id represents product D number; semi _ product _ ID represents a semi-finished product ID associated with the product.
3. The semi-finished product process (aps _ process.csv) file describes all processes under a semi-finished product, wherein the semi-finished product named as a commodity D is used as an assembly process, the semi-finished product and the processes are in one-to-many relationship, and one line of data is certain process information required by a certain semi-finished product. The specific fields and their meanings are as follows:
semi _ product _ id: the semi-finished product ID is used for distinguishing the semi-finished product and is unique;
semi _ name: name of the semi-finished product;
part _ id: the process ID required by the semi-finished product is unique;
part _ name: the process name required by the semi-finished product is unique;
prev _ part _ id: the front-end procedure which is depended by the procedure can be carried out only after the front-end procedure is finished, and if the front-end procedure is empty, the front-end procedure is not needed;
input _ semi _ finished _ product _ id: the pre-semifinished product ID required for the process is separated by a separator (comma, space, etc.);
input _ semi _ finished _ product _ account: the quantity of the front semi-finished products needed by the process is separated by separators, and the sequence corresponds to the previous field;
output _ semi _ finished _ product _ id: the ID of the semi-finished product output after the working procedure is finished marks that the working procedure is the last working procedure of the semi-finished product, and if the field is empty, the field also has a post working procedure;
production _ mode: the process of the working procedure comprises the following steps: BP (batch process), SP (stream process). Wherein BP: batch processing means that a plurality of semi-finished products from 1 to maximum production quality can be processed at the same time, the process takes time as "production time", and a plurality of semi-finished products can be in parallel;
SP: streaming means that it can be done only once at a time, with a plurality of semi-finished products being executed in series, for example: the production _ mode is BP, maximum _ production _ qualification is 10, and production _ time is 15, which means that the process can be performed in parallel, the process can be performed on 1-10 semi-finished products at the same time, and the consumption time of 1 or 10 semi-finished products is 15 minutes;
the process _ mode is SP, maximum _ Production _ qualification is 1, and the process _ time can only be done serially, with at most 1 semi-finished product being executed at a time, with an execution time of 5 minutes.
Production _ time: the execution time of the procedure is in minutes;
minimum _ production _ quality: a minimum executable number; maximum production quality: a maximum executable number; workspace: to which production plant the process belongs, the process can only use resources under that production plant.
4. The resource file (aps _ resource.csv) describes the relevant information of the resource. Wherein the meaning of the field is:
resource _ id: the resource id is unique;
name: a resource name;
reaource type: the resource belongs to the largest category;
and (4) amount: the number of homogeneous resources, which represents the total number of resources that are identical
5. The process-corresponding required resource (aps _ process _ resource.csv) describes the matching relationship between the process and the required resource, and the process and the resource are in one-to-many relationship:
part _ id: the process ID corresponds to part _ ID in aps _ process;
name: the process name;
resource attributes: the resource attribute required by the process is related to resource _ attributes in ape _ resource;
and (4) amount: the amount of such resources required for the process.
It is understood that the data related to the order, the goods, the semi-finished product process, the resource, and the resource required by the process can be stored in a data format or a database format such as XML, json, jason, DB, SQL, etc. By semi-finished product is understood a product in one or more intermediate forms that are not finished.
Referring to fig. 2, in step S100 of some embodiments of the present invention, the optimal scheduling sequence of the semi-finished job corresponding to each product is obtained by: determining the production quantity of the semi-finished products according to the production batch information of the semi-finished products corresponding to each product; determining the ingredients of the semi-finished products according to the production quantity of the semi-finished products; determining a feasible resource allocation scheme and an operation sequence set according to a semi-finished product ingredient processing mode, wherein the resource allocation comprises personnel allocation and equipment allocation required by semi-finished product ingredient production; and searching the optimal scheduling sequence of the semi-finished product operation corresponding to each product from the resource allocation scheme and the operation sequence set by using a greedy algorithm.
Further, the determining a feasible resource allocation scheme and an operation sequence set according to the processing mode of the semi-finished product ingredients comprises the following steps:
if the semi-finished product ingredients are processed in a streaming way, determining a feasible resource allocation scheme and an operation sequence set in a tree searching way; and if the semi-finished product ingredients are processed in batch, determining a feasible resource allocation scheme and an operation sequence set by taking the maximum parallel batch number and the total production number as constraint conditions.
Specifically, step 1001: inputting data;
step 1002: b is 0;
step 1003: selecting batches b in sequence from the semi-finished product batch table s _ batch;
step 1004: batch b produces parts total _ num ═ f _ num + v _ num; f num is the demand and v num is the additional production; generating initial operation allocation resources of each batch, storing the operation to be linked in waited, wherein an ingredient table of the batch is recipes, and p is 0;
step 1005: sequentially selecting semi-finished product ingredients in an ingredient list; the ordinal number is p;
step 1006: judging whether the ingredient is processed in batch or in stream: if the stream processing is performed, go to step 1007; if the batch processing is performed, go to step 1008;
step 1007: the time of the last in-place resource is the starting time max _ CT, and resource distribution and operation connection are carried out according to tree search selection operation under the condition that the total number of production is not more than total _ num; go to step 1009;
step 1008: the time of the last in-place resource is the starting time max _ CT, and the operation is selected to carry out resource distribution and operation connection under the condition that the requirement that the maximum parallel batch Q and the total number of production is not more than the total number of num is met;
step 1009: updating a to-be-linked operation table waited, wherein p is p + 1;
step 10010: judging whether the processing of the ingredient table is finished or not, namely judging whether p is equal to sides. If yes, go to step 10011; if not, go back to step 1005;
step 10011: b is completed, b is b + 1;
step 10012: judging whether the semi-finished product batch table is distributed completely or not, namely judging whether b is equal to s _ batch. If yes, go to step 10013; if not, go back to step 1003;
step 10013: finishing a semi-finished product batch table;
step 10014: sorting the semi-finished products in ascending order according to the completion time max _ CT; b is 0;
step 10015: selecting batches b in sequence from a product batch table product _ batch; n is 0;
step 10016: searching and allocating resources by a greedy algorithm tree to complete resource allocation and operation selection of one product; n is n + 1;
step 10017: judging whether the required product quantity of the batch b is finished: that is, judge n > - _ b.f _ num: if not, go back to step 10015, if yes, go to step 10018;
step 10018: completing batch b, b ═ b + 1;
step 10019: judging whether the required quantity of the batch list is finished or not; namely, judging b ═ product _ batch: if not, go back to step 10015; if yes, go to step 10020;
step 10020: and finishing the greedy algorithm scheduling and returning the solution after scheduling.
In some embodiments of the invention, step S200 comprises: s201, generating individuals with the number of popsize as an ith generation of population; wherein, the product batch product _ batch and the semi-finished product batch semi _ batch are generated randomly by each individual; s202, solving all individuals in the ith generation of population by a Greedy algorithm, and sorting in an ascending order according to objective function values with the manufacturing time makespan of the individuals as a solution.
Referring to fig. 3, in step S300 of some embodiments of the present invention, the determining an optimal scheduling scheme of the scheduling model using a genetic algorithm includes: s301, clustering and sequencing one or more products belonging to the same task sequence according to the latest finishing time of each product production to obtain the working serial number and the corresponding batch of each product; s302, randomly generating finished product sequences according to a plurality of working sequence numbers belonging to the same batch, taking each finished product sequence as an individual, and taking a plurality of individuals of the same task sequence as a population; and S303, calculating the total production time required by the optimal scheduling sequence of the semi-finished product operation corresponding to each finished product sequence, and determining the global optimal solution of the population.
Further, in step S303, the determining a global optimal solution of the population according to the total production time corresponding to the optimal scheduling sequence of the semi-finished product job corresponding to each finished product sequence includes the following steps:
if the population does not have the optimal solution, then: calculating the total production time required by the optimal scheduling sequence of the semi-finished product operation corresponding to each finished product sequence, and screening optimal individuals and suboptimal individuals from the finished product sequences; generating a plurality of filial generation individuals according to the optimal individual and the suboptimal individual until the number of the filial generation individuals reaches the upper limit of the population; and repeating the steps until the optimal solution of the population appears.
Specifically, step 3036: the value of the optimal solution is recorded into a variable best _ cost, and the structure of the optimal solution is recorded into a variable best _ sol;
step 3037: crossing the product batches of the best two individuals in the population to generate two groups of new product _ batch and semi _ batch as two filial generations, and adding the two groups of new product _ batch and semi _ batch into the filial generation population offset;
step 3038: randomly selecting two individuals from the population, comparing the two individuals to obtain a better individual as a parent 1, randomly selecting two individuals from the rest population to compare, and using the better individual as a parent 2; crossing the product batches of the parent 1 and the parent 2 to generate two groups of new product _ batch and semi _ batch as two children, and adding the two groups of new product _ batch and semi _ batch into the child population offset;
step 3039: whether the size of the offspring population offset is less than popsize; if yes, continuing to step 8, otherwise, returning to step 30310 for continuing;
step 30310: i is i + 1; making the offspring population as a new ith generation;
step 30311: judging whether i is less than the set maximum number GA _ ITER, if so, returning to the step 3 for continuous operation, otherwise, performing the step 30312;
step 30312: and outputting the value best _ cost of the optimal solution and the structure best _ sol of the optimal solution.
Schematically, the output results of the above steps are shown in fig. 4 to 6, and three lots of jobs are represented by a first individual as [ [5, 4], [2, 0], [4, 1, 3 ]; and performing set division on the working batches in ascending order of the starting time. 5. Job No. 4 started earliest, job No. 2, 0 second, and job No. 4, 1, 3 again. The order in the same batch is randomly generated, thereby creating the diversity of individuals in the population. [ [6, 9], [9, 17, 11, 12, 15, 22], [9, 15, 21, 24, 12], [20, 23, 9, 11, 15, 6], [15, 9, 12, 22, 17, 11], [20, 19, 6, 10, 24, 11], [8, 25, 11, 19, 24, 10] ] is a set division of work serial numbers of semi-finished products corresponding to the product sequence, for example, [6, 9] represents the work steps of semi-finished products of the work of product number 5 and is arranged in ascending order of start time sequence. Then, offspring are generated by using the best two individuals in the parent, such as the individual No. 8, mak espan is 958, and the individual No. 2, makespan is 974 in the figure; the two are the optimal and suboptimal individuals, so the segments are selected randomly by crossing the optimal and suboptimal individuals to obtain two filial generations. Progeny 1 the first fragment [5, 4] is from individual No. 8, and the third fragment [4, 1, 3] is from individual No. 2; progeny 1 the first fragment [4, 5] is from individual No. 2 and the third fragment [4, 1, 3] is from individual No. 8. It is understood that the numbers in [ ] represent the work serial numbers, which are related to the product category numbers and the order latest end time (latest end time of each product production) in the task sequence, for example [ [5, 4], [2, 0], [4, 1, 3] ] represent different categories of products belonging to the same batch of order tasks, where [5, 4] represents the production plan (task sequence) of product 4 and product 3 that must be completed on the first day, and [2, 0], [4, 1, 3] represents the production plan on the second and third days, respectively. Alternatively, [ ] the numbers within are used as individual codes for the genetic algorithm, which are encoded using one or more of the relevant data for the input data set inst and parameter param as described above.
It should be noted that, in the above embodiment, the optimal scheduling scheme of the scheduling model is determined to obtain the task sequence and the optimal scheduling sequence of the semi-finished job corresponding to each product, and the optimal scheduling scheme is converted into the actual scheduling plan of the workshop and the resource scheduling plan by querying the corresponding data set and parameters.
Example 2
Referring to fig. 7, in a second aspect of the present invention, a flexible job shop scheduling system 1 based on a genetic algorithm is provided, including an obtaining module 11, a constructing module 12, and a determining module 13, where the obtaining module 11 is configured to obtain a task sequence including production of one or more products and an optimal scheduling sequence of semi-finished jobs corresponding to each product; the building module 12 is configured to build a scheduling model with the shortest production time as a target according to the task sequence and the optimal scheduling sequence of the semi-finished job corresponding to each product; the determining module 13 is configured to determine an optimal scheduling scheme of the scheduling model by using a genetic algorithm.
Further, the determining module 13 includes a sorting unit, a generating unit, and a calculating unit, where the sorting unit is configured to perform clustering and sorting on one or more products belonging to the same task sequence according to the latest ending time of production of each product, so as to obtain a work serial number and a corresponding batch of each product; the generating unit is used for randomly generating finished product sequences according to a plurality of working serial numbers belonging to the same batch, taking each finished product sequence as an individual, and taking a plurality of individuals of the same task sequence as a population; and the computing unit is used for computing the total production time required by the optimal scheduling sequence of the semi-finished product operation corresponding to each finished product sequence and determining the global optimal solution of the population.
Example 3
In a third aspect of the present invention, there is provided a flexible production apparatus comprising: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the flexible job shop scheduling method based on genetic algorithm provided by the first aspect of the present invention.
Referring to fig. 8, the flexible production apparatus 500 may include a processing device (e.g., a central processing unit, a graphic processor, etc.) 501 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage device 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following devices may be connected to the I/O interface 505 in general: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; a storage device 508 including, for example, a hard disk; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 8 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 8 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium described in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more computer programs which, when executed by the electronic device, cause the electronic device to:
computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, Python, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A flexible job shop scheduling method based on a genetic algorithm is characterized by comprising the following steps:
acquiring a task sequence for producing one or more products and an optimal scheduling sequence of semi-finished product operation corresponding to each product;
constructing a scheduling model taking the shortest production time as a target according to the task sequence and the optimal scheduling sequence of the semi-finished product operation corresponding to each product;
and determining the optimal scheduling scheme of the scheduling model by utilizing a genetic algorithm.
2. The flexible job shop scheduling method based on genetic algorithm according to claim 1, wherein the optimal scheduling sequence of the semi-finished job corresponding to each product is obtained by the following steps:
determining the production quantity of the semi-finished products according to the production batch information of the semi-finished products corresponding to each product;
determining the ingredients of the semi-finished products according to the production quantity of the semi-finished products;
determining a feasible resource allocation scheme and an operation sequence set according to a semi-finished product ingredient processing mode, wherein the resource allocation comprises personnel allocation and equipment allocation required by semi-finished product ingredient production;
and searching the optimal scheduling sequence of the semi-finished product operation corresponding to each product from the resource allocation scheme and the operation sequence set by using a greedy algorithm.
3. The flexible job shop scheduling method based on genetic algorithm according to claim 2, wherein the determining of the feasible resource allocation scheme and operation sequence set according to the processing mode of the semi-finished product ingredients comprises the following steps:
if the semi-finished product ingredients are processed in a streaming way, determining a feasible resource allocation scheme and an operation sequence set in a tree searching way;
and if the semi-finished product ingredients are processed in batch, determining a feasible resource allocation scheme and an operation sequence set by taking the maximum parallel batch number and the total production number as constraint conditions.
4. The genetic algorithm-based flexible job shop scheduling method according to claim 1, wherein said determining the optimal scheduling scheme of the scheduling model using a genetic algorithm comprises:
clustering and sequencing one or more products belonging to the same task sequence according to the latest finishing time of each product production to obtain the working serial number and the corresponding batch of each product;
randomly generating finished product sequences according to a plurality of working serial numbers belonging to the same batch, taking each finished product sequence as an individual, and taking a plurality of individuals of the same task sequence as a population;
and calculating the total production time required by the optimal scheduling sequence of each finished product sequence corresponding to the semi-finished product operation, and determining the global optimal solution of the population.
5. The flexible job shop scheduling method based on genetic algorithm according to claim 4, wherein the determining the global optimal solution of the population according to the total production time corresponding to the optimal scheduling sequence of the semi-finished job corresponding to each finished product sequence comprises the following steps:
if the population does not have the optimal solution, then:
calculating the total production time required by the optimal scheduling sequence of the semi-finished product operation corresponding to each finished product sequence, and screening optimal individuals and suboptimal individuals from the finished product sequences;
generating a plurality of filial generation individuals according to the optimal individual and the suboptimal individual until the number of the filial generation individuals reaches the upper limit of the population;
and repeating the steps until the optimal solution of the population appears.
6. The genetic algorithm-based flexible job shop scheduling method according to any one of claims 1-5, wherein the goal of the scheduling model further comprises a minimum computation time.
7. A flexible job shop scheduling system based on genetic algorithm is characterized by comprising an acquisition module, a construction module and a determination module,
the acquisition module is used for acquiring a task sequence for producing one or more products and an optimal scheduling sequence of semi-finished product operation corresponding to each product;
the construction module is used for constructing a scheduling model taking the shortest production time as a target according to the task sequence and the optimal scheduling sequence of the semi-finished product operation corresponding to each product;
the determining module is used for determining the optimal scheduling scheme of the scheduling model by utilizing a genetic algorithm.
8. The genetic algorithm-based flexible job shop scheduling system according to claim 7, wherein said determining module comprises a sorting unit, a generating unit, a calculating unit,
the sequencing unit is used for clustering and sequencing one or more products belonging to the same task sequence according to the latest finishing time of each product production to obtain the working serial number and the corresponding batch of each product;
the generating unit is used for randomly generating finished product sequences according to a plurality of working serial numbers belonging to the same batch, taking each finished product sequence as an individual, and taking a plurality of individuals of the same task sequence as a population;
and the computing unit is used for computing the total production time required by the optimal scheduling sequence of the semi-finished product operation corresponding to each finished product sequence and determining the global optimal solution of the population.
9. A flexible production facility of car networking communication terminal product includes: one or more processors; a storage device for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the flexible genetic algorithm-based job shop scheduling method according to any one of claims 1 to 6.
10. A computer-readable medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the flexible job shop scheduling method based on genetic algorithms according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110645518.0A CN113298313A (en) | 2021-06-10 | 2021-06-10 | Flexible job shop scheduling method and system based on genetic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110645518.0A CN113298313A (en) | 2021-06-10 | 2021-06-10 | Flexible job shop scheduling method and system based on genetic algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113298313A true CN113298313A (en) | 2021-08-24 |
Family
ID=77327795
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110645518.0A Withdrawn CN113298313A (en) | 2021-06-10 | 2021-06-10 | Flexible job shop scheduling method and system based on genetic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113298313A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113743788A (en) * | 2021-09-06 | 2021-12-03 | 爱动超越人工智能科技（北京）有限责任公司 | Industrial vehicle scheduling method based on improved genetic algorithm |
| CN114563994A (en) * | 2022-03-17 | 2022-05-31 | 昆明理工大学 | Optimized scheduling method for distributed production and assembly process of automobile parts |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103717007A (en) * | 2014-01-22 | 2014-04-09 | 哈尔滨工业大学 | Multiple-suction-nozzle chip mounter mounting process optimization method based on clustering analysis and genetic algorithm |
| CN111047081A (en) * | 2019-11-28 | 2020-04-21 | 上海电机学院 | Manufacturing resource allocation optimization decision method for green production |
-
2021
- 2021-06-10 CN CN202110645518.0A patent/CN113298313A/en not_active Withdrawn
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103717007A (en) * | 2014-01-22 | 2014-04-09 | 哈尔滨工业大学 | Multiple-suction-nozzle chip mounter mounting process optimization method based on clustering analysis and genetic algorithm |
| CN111047081A (en) * | 2019-11-28 | 2020-04-21 | 上海电机学院 | Manufacturing resource allocation optimization decision method for green production |
Non-Patent Citations (4)
| Title |
|---|
| 徐智等: "基于遗传算法的一类带缓冲区的混合生产调度", 《控制理论与应用》 * |
| 李康顺等: "一种基于启发式演化算法的最优-最差蚂蚁系统", 《中南大学学报(自然科学版)》 * |
| 王凌等: "绿色车间调度优化研究进展", 《控制与决策》 * |
| 赵等: "冷轧薄板生产线组批调度模型与算法", 《计算机集成制造系统》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113743788A (en) * | 2021-09-06 | 2021-12-03 | 爱动超越人工智能科技（北京）有限责任公司 | Industrial vehicle scheduling method based on improved genetic algorithm |
| CN114563994A (en) * | 2022-03-17 | 2022-05-31 | 昆明理工大学 | Optimized scheduling method for distributed production and assembly process of automobile parts |
| CN114563994B (en) * | 2022-03-17 | 2024-03-26 | 昆明理工大学 | optimized scheduling method for distributed production and assembly processes of automobile parts |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yang et al. | Optimized flowshop scheduling of multiple production lines for precast production | |
| Ma et al. | Optimized rescheduling of multiple production lines for flowshop production of reinforced precast concrete components | |
| CN113705866B (en) | Scheduling optimization method and system based on resource-constrained project scheduling problem model | |
| CN110046777B (en) | Continuous reconfiguration scheduling method and device for flexible job shop | |
| CN109636011B (en) | Multi-shift planning and scheduling method based on improved variable neighborhood genetic algorithm | |
| CN110058573B (en) | Huff and puff flexible intelligent assembly logistics path planning platform | |
| CN110084405B (en) | Throughput flexible intelligent assembly logistics path planning method | |
| CN111062535A (en) | Method and system for realizing dynamic scheduling of energetic material production process | |
| CN113298313A (en) | Flexible job shop scheduling method and system based on genetic algorithm | |
| CN111915410A (en) | Intelligent management and control system for high-dynamic production logistics process | |
| CN109409763A (en) | A kind of dynamic test assignment dispatching method and dispatching platform based on Greedy grouping strategy | |
| CN115600774A (en) | Multi-target production scheduling optimization method for assembly type building component production line | |
| CN112990515A (en) | Workshop resource scheduling method based on heuristic optimization algorithm | |
| CN115578023A (en) | Assembly workshop scheduling method, device, equipment and storage medium | |
| CN115271130B (en) | Dynamic scheduling method and system for maintenance order of ship main power equipment | |
| CN114862209A (en) | Transport capacity scheduling method and device, electronic equipment and storage medium | |
| CN113128839A (en) | High-end equipment distributed manufacturing and multi-mode transportation oriented cooperative scheduling method | |
| CN112116189A (en) | Production plan drafting support system | |
| CN112884367B (en) | Multi-project collaborative scheduling method and system for equipment research and development with multi-skill employee constraint | |
| CN117763943A (en) | Multi-stage production resource scheduling simulation optimization oriented algorithm configurable system | |
| Schneider et al. | Benefits of digitalization for business processes in semiconductor manufacturing | |
| CN115511292B (en) | Production scheduling method, system, intelligent terminal and storage medium | |
| CN116451936A (en) | Scheduling method, system and storage medium based on genetic algorithm | |
| CN113592288B (en) | Discharging plan scheduling method based on non-equivalent parallel machine model | |
| Nearchou et al. | Multisite and multishift personnel planning with set-up costs |
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 | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Chen Qi Inventor after: Chen Feng Inventor after: Yan Jin Inventor after: Yao Li Inventor before: Wang Kai Inventor before: Cai Lei Inventor before: Liu Xudong Inventor before: Yang Cong Inventor before: Chen Qi Inventor before: Chen Feng Inventor before: Qin Hu Inventor before: Luo Zhixing Inventor before: Wang Yilun Inventor before: Yan Jin Inventor before: Chen Bishao Inventor before: Yao Li |
|
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20210824 |