CN113240176A - Intelligent scheduling method for unit type assembly workshop based on limited personnel instant station - Google Patents

Intelligent scheduling method for unit type assembly workshop based on limited personnel instant station Download PDF

Info

Publication number
CN113240176A
CN113240176A CN202110516722.2A CN202110516722A CN113240176A CN 113240176 A CN113240176 A CN 113240176A CN 202110516722 A CN202110516722 A CN 202110516722A CN 113240176 A CN113240176 A CN 113240176A
Authority
CN
China
Prior art keywords
scheduling
station
task
workshop
personnel
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.)
Granted
Application number
CN202110516722.2A
Other languages
Chinese (zh)
Other versions
CN113240176B (en
Inventor
李龙
和延立
赵鑫
张政
李子昂
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northwestern Polytechnical University
Original Assignee
Northwestern Polytechnical University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Northwestern Polytechnical University filed Critical Northwestern Polytechnical University
Priority to CN202110516722.2A priority Critical patent/CN113240176B/en
Publication of CN113240176A publication Critical patent/CN113240176A/en
Application granted granted Critical
Publication of CN113240176B publication Critical patent/CN113240176B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • 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
    • 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
    • G06Q10/06316—Sequencing of tasks or work
    • 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)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Health & Medical Sciences (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Biophysics (AREA)
  • Development Economics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Evolutionary Biology (AREA)
  • Educational Administration (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (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)
  • Genetics & Genomics (AREA)
  • Physiology (AREA)
  • Manufacturing & Machinery (AREA)
  • Primary Health Care (AREA)
  • General Factory Administration (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses an intelligent scheduling method of a unit assembly workshop based on limited personnel instant station positions, which is used for constructing a mathematical model of the intelligent scheduling problem of the unit assembly workshop aiming at elements such as workshop scheduling tasks, workshop personnel production and the like; combining with rules, solving a scheduling mathematical model by using an improved genetic algorithm, wherein the scheduling mathematical model comprises the steps of constructing a task matrix, constructing initial population, gene codes of tasks and personnel stations, cross variation of population genes, calculating population fitness, selecting excellent genes and the like, and obtaining an optimal instant station scheduling scheme; the invention combines the instant station problem and the scheduling problem, integrates rules and an improved genetic algorithm, limits the constraint of the problem through mathematical abstraction, ensures the corresponding relation between a responsible person and a task by means of coding, obtains the optimal scheduling scheme of the instant station of the unit assembly workshop on the whole through the scheduling algorithm, improves the production efficiency of the assembly workshop, balances the workload of workers, improves the utilization rate of equipment, reduces the production cost and improves the comprehensive competitiveness of enterprises.

Description

Intelligent scheduling method for unit type assembly workshop based on limited personnel instant station
Technical Field
The invention belongs to the technical field of operational research, and particularly relates to an intelligent scheduling method for a unit type assembly workshop.
Background
At present, many enterprises still use a static scheduling method, although a production workshop can normally run, once a scheduling plan is determined, the production is generally carried out according to the plan, resources cannot be effectively configured, events such as machine faults, personnel lack of posts and the like are difficult to deal with, the situation of untimely scheduling can occur, and the production efficiency of the workshop is low. How to allocate limited resources within a certain time is a key for influencing the comprehensive strength of enterprises, comprehensively arranging production tasks, shortening production period and maximally utilizing resources.
Dynamic scheduling refers to the whole manufacturing system as a dynamic process, and requires the whole system to respond to dynamic events, such as uncertainty of component arrival, machine failure, etc., in real time, so that the whole system has good flexibility, but also increases the complexity of the whole system and difficulty of scheduling production.
In the current research of intelligent production scheduling technology, the majority is based on an approximation method. For the problem of intelligent scheduling of an assembly workshop, the scheduling process is complex, factors of field operators are not taken into consideration in the current research, and a model for the problem of self-adaptive scheduling in a unit assembly mode is lacked, so that human resources are wasted, the equipment utilization rate is low, and the production efficiency of the assembly workshop is low. Meanwhile, the automatic production scheduling method provided in the prior art has low efficiency and poor application effect in enterprises. Therefore, it is necessary to further determine the corresponding scheduling strategy and design the structure of the system, and establish a mathematical model and an efficient solving algorithm, so as to obtain an optimal intelligent scheduling scheme of the instant station of the unit assembly workshop, guide the production of the workshop, and improve the enterprise benefit.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an intelligent scheduling method of a unit assembly workshop based on limited personnel instant station, aiming at elements such as workshop scheduling tasks, workshop personnel production and the like, a mathematical model of the intelligent scheduling problem of the unit assembly workshop is constructed; combining with rules, solving a scheduling mathematical model by using an improved genetic algorithm, wherein the scheduling mathematical model comprises the steps of constructing a task matrix, constructing initial population, gene codes of tasks and personnel stations, cross variation of population genes, calculating population fitness, selecting excellent genes and the like, and obtaining an optimal instant station scheduling scheme; the invention combines the instant station problem and the scheduling problem, integrates rules and an improved genetic algorithm, limits the constraint of the problem through mathematical abstraction, ensures the corresponding relation between a responsible person and a task by means of coding, obtains the optimal scheduling scheme of the instant station of the unit assembly workshop on the whole through the scheduling algorithm, improves the production efficiency of the assembly workshop, balances the workload of workers, improves the utilization rate of equipment, reduces the production cost and improves the comprehensive competitiveness of enterprises.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
step 1: acquiring a production element data set of a unit type assembly workshop;
step 2: aiming at scheduling tasks and production element data of the unit type assembly workshop, a scheduling problem mathematical model of the instant station of the unit type assembly workshop is constructed;
and step 3: determining a task scheduling optimization strategy according to the task scheduling rule, the personnel station position rule and the scheduling optimization target;
and 4, step 4: solving a scheduling problem mathematical model by using an improved genetic algorithm to obtain an optimal unit type assembly workshop instant station intelligent scheduling scheme;
and 5: and (4) judging whether the scheduling task of the unit type assembly workshop has updating change, if so, returning to the step 1 to perform scheduling again.
Further, the unit assembly plant production element data set includes: on duty worker data, scheduling task data and scheduling date data;
on Shift worker data, i.e., the number of workers on Shift within a unit that can engage in assembly production; scheduling task data, namely the number of tasks for scheduling, the number of working procedures, the installation and adjustment time of each working procedure, the task issuing time, the task handing-over time and the total task processing period; the production schedule is the time for the assembly operation to start the first process of the first job of the batch.
Further, the scheduling problem mathematical model of the instant station of the unit type assembly workshop is as follows:
Figure BDA0003061779620000021
wherein f represents the minimized maximum completion time, i.e., the optimization objective of scheduling; t isijThe completion time of the jth process of the ith part is shown, n is the number of tasks of the scheduling, and m is the total number of assembly processes of each task, namely the number of stations of the unit type assembly workshop.
Further, the constraint conditions of the scheduling problem mathematical model of the instant station of the unit assembly plant comprise:
constraint 1: the components are assembled in sequence according to a preset process route:
Sij+xijkyikltij≤Si(j+1)，i∈{0,1,2,...n}j∈{0,1,2,...m}k∈{0,1,2,...h}
in the formula, SijIndicates the assembly start time, S, of the jth process of the ith componenti(j+1)Represents the assembly start time, t, of the (i) th part in the (j + 1) th processijThe machining time of the jth procedure of the ith part is shown, and h is the number of workers;
Figure BDA0003061779620000031
Figure BDA0003061779620000032
constraint 2: the same process for different parts can only be assembled one at a time:
Sij≠Si′j，i、i’∈{0,1,2,...n}，j∈{0,1,2,...m}
constraint 3: only one process can be assembled by one assembler at the same time:
∑xijk＝1，i∈{0,1,2,...n}，j∈{0,1,2,...m}，k∈{0,1,2,...l}
in the formula xijkThe number of the working marks representing the working personnel is 0 or 1;
constraint 4: only one working procedure can be assembled at one station and the same time:
∑yikl＝1，i∈{0,1,2,...n}，k∈{0,1,2,...m}，l∈{0,1,2,...m}
in the formula yiklThe number of the working marks representing the stations is 0 or 1;
constraint 5: the process cannot be terminated once assembly is initiated until completion:
Tij＝Sij+tij。
further, the task scheduling rule comprises a task arrival time, a task delivery time, a task assembly period, a task duration and a task urgency degree rule; the personnel station position rules comprise personnel-fixed station-fixed, task-fixed, idle station-fixed and random station position rules; the optimization strategies include rule-based genetic algorithm optimization and globally optimal genetic algorithm optimization.
Further, the method for solving the scheduling problem mathematical model by using the improved genetic algorithm to obtain the optimal unit type assembly workshop instant station intelligent scheduling scheme comprises the following specific steps:
step 4-1: constructing an initial condition matrix;
an n multiplied by m matrix t is adopted to represent the assembly time length of each procedure of each part, the arrangement of each row element in the matrix obeys the task scheduling rule, namely the priority of the task is increased or decreased with the increase of the number of the rows;
Figure BDA0003061779620000033
in the formula tijShowing the assembly time of the jth procedure of the ith part;
an n x m matrix p is adopted to represent the station position condition of each station worker, and the arrangement of each element in the matrix obeys the personnel station position rule;
Figure BDA0003061779620000041
in the formula pijAn assembler code indicating the jth process of the ith component;
step 4-2: population individual gene coding and decoding:
step 4-3: creating an initial population;
generating a plurality of initial population sequences in a random generation mode; forming an initial population G ═ G1,g2,…,gq) Wherein q is the number of individuals in the set initial population;
step 4-4: carrying out individual gene sequence cross variation in the population by adopting a two-point corresponding method;
and 4-5: constructing a fitness function, and solving the individual fitness value f in the populationfitness：
Figure BDA0003061779620000042
And 4-6: selecting individuals in the population by adopting a method combining optimal individual retention and tournament selection;
and 4-7: judging whether a stopping criterion of a genetic algorithm is met, if so, outputting an optimal unit type assembly workshop instant station intelligent scheduling scheme, and if not, continuing to execute;
and 4-8: and generating a scheduling Gantt chart according to the output optimal scheduling scheme, and issuing the production scheme to a workshop.
Further, the population individual gene encoding and decoding are described as follows:
the population individual gene comprises working procedures and worker station positions, the total length of the gene sequence is set to be 2 x m x n, the front m x n elements are used for carrying out gene arrangement on each working procedure of each component, meanwhile, the elements in the matrix p are arranged to form the rear m x n gene sequence of the gene according to the index value of the working procedure arrangement, and the decoding process is the reverse process of the encoding process.
Further, the method for performing individual gene sequence cross variation in the population by adopting the two-point correspondence method comprises the following specific steps:
step 4-4-1: rule-based genetic algorithm optimization:
A. the cross operation adopts station-to-station corresponding POX cross, comprising the following steps:
1) randomly selecting a numbered part R and a worker station corresponding to each process of the part R from all product types as fixed position genes;
2) respectively copying the part R process sequences of two parents P1 and P2 and the worker site genes corresponding to each process into descendants C1 and C2 with the maintenance positions unchanged;
3) copying the remaining products in the product sequences in P1 and P2 to C2 and C1 in sequence;
B. variation of gene sequence:
the mutation operation adopts the mutual change mutation corresponding to the I-bit: randomly selecting two positions on a product sequence, then exchanging genes on the two positions, and simultaneously exchanging worker station position genes corresponding to the two position procedures;
step 4-4-2: optimizing a global optimal genetic algorithm;
step 4-4-2-1: gene sequence crossing;
A. the cross operation adopts subsection cross, the process section gene sequence cross and POX cross, and comprises the following steps:
1) randomly selecting a numbered part R and a worker station corresponding to each process of the R part from all product types as fixed position genes;
2) respectively copying the part R process sequences of two parents P1 and P2 and the worker site genes corresponding to each process into descendants C1 and C2 with the maintenance positions unchanged;
3) copying the remaining products in the product sequences in P1 and P2 to C2 and C1 in sequence;
B. worker station segment genes are uniformly crossed, and the method comprises the following steps:
1) randomly selecting a plurality of integers in a robot selection sequence length interval [1, M ];
2) generating integers according to step 1, copying gene retention positions and sequences at corresponding positions on the parent P1 and P2 robot selection sequences into offspring C1 and C2 respectively;
3) copying the remaining genes in the robot selection sequences of P1 and P2 to C2, C1 in sequence, keeping the positions and order unchanged;
step 4-4-2-2: variation of gene sequence;
1) the variation operation adopts segmented variation, and the sequence segment genes of the part procedures adopt interchange variation: randomly selecting two positions on a product sequence, then exchanging genes on the two positions, and simultaneously exchanging the worker station position genes corresponding to the two position procedures;
2) the site gene of the worker adopts variation: randomly selecting a plurality of positions on the personnel station segment gene, and changing the personnel station at the positions into personnel codes different from the current position.
An intelligent scheduling system of a unit type assembly workshop based on limited personnel instant station positions comprises:
the model construction module is used for constructing a scheduling problem mathematical model of the instant station of the unit type assembly workshop aiming at the workshop scheduling task;
the solving module is used for solving a scheduling problem mathematical model by using an improved genetic algorithm to obtain an optimal unit type assembly workshop instant station intelligent scheduling scheme;
and the judging module is used for judging whether the scheduling task is updated or not, and if so, executing the model building module.
Further, the judging module judges as follows:
step 8-1: judging whether a new component enters a waiting component queue or not according to the production element data of the unit assembly workshop acquired in the step 1, if no new component enters, executing a step 4-8, otherwise, executing a step 4-1;
step 8-2: judging whether the number of workers in the workshop is changed or not according to the production element data of the unit assembly workshop acquired in the step 1, if so, executing the step 4-1, otherwise, executing the step 4-8;
step 8-3: judging whether the number of waiting components in the waiting component queue exceeds the preset queue length or not according to the production element data of the unit assembly workshop acquired in the step 1, if so, executing the step 4-1, otherwise, returning to execute the step 4-8;
step 8-4: and (5) reconstructing the fitness function and executing the step 2.
The invention has the following beneficial effects:
1. on the basis of describing the real-time station intelligent scheduling problem of the unit assembly workshop, the invention fully considers all production factors and constraints, simultaneously considers the situation of workers on duty on site neglected by the former people, establishes a mathematical model of the problem, avoids the waste of human resources and improves the utilization rate of equipment;
2. the method combines the genetic algorithm optimization of the rules, and combines the rules and the heuristic algorithm, so that the production scheduling problem under various rules can be efficiently and quickly solved and optimized;
3. the improved genetic algorithm is adopted to solve the model, the encoding of the genetic algorithm is improved, the encoding efficiency is improved, a real-time station intelligent scheduling optimization scheme with good performance for a unit type assembly workshop can be effectively obtained, the production efficiency of the assembly workshop is improved, the production cost is reduced, and the like;
4. the method has simple operation process, obtains better optimization effect, reduces unnecessary resource waste in the process, reduces production cost and improves the comprehensive competitiveness of manufacturing enterprises.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a flow chart of the improved genetic algorithm of the present invention.
FIG. 3 is a schematic diagram of the rule-based genetic algorithm gene cross variation in the method of the present invention
FIG. 4 is a schematic diagram of gene cross variation of the global optimization genetic algorithm of the method of the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
As shown in fig. 1, an intelligent scheduling method for a unit assembly shop based on limited personnel immediate standing includes the following steps:
step 1: acquiring a production element data set of a unit type assembly workshop;
step 2: aiming at scheduling tasks and production element data of the unit type assembly workshop, a scheduling problem mathematical model of the instant station of the unit type assembly workshop is constructed;
and step 3: determining a task scheduling optimization strategy according to the task scheduling rule, the personnel station position rule and the scheduling optimization target;
and 4, step 4: solving a scheduling problem mathematical model by using an improved genetic algorithm to obtain an optimal unit type assembly workshop instant station intelligent scheduling scheme;
and 5: and (4) judging whether the scheduling task of the unit type assembly workshop has updating change, if so, returning to the step 1 to perform scheduling again.
Further, the unit assembly plant production element data set includes: on duty worker data, scheduling task data and scheduling date data;
on Shift worker data, i.e., the number of workers on Shift within a unit that can engage in assembly production; scheduling task data, namely the number of tasks for scheduling, the number of working procedures, the installation and adjustment time of each working procedure, the task issuing time, the task handing-over time and the total task processing period; the production schedule is the time for the assembly operation to start the first process of the first job of the batch.
Further, the scheduling problem mathematical model of the instant station of the unit type assembly workshop is as follows:
Figure BDA0003061779620000071
wherein f represents the minimized maximum completion timeI.e. optimization objectives for scheduling production; t isijThe completion time of the jth process of the ith part is shown, n is the number of tasks of the scheduling, and m is the total number of assembly processes of each task, namely the number of stations of the unit type assembly workshop.
Further, the constraint conditions of the scheduling problem mathematical model of the instant station of the unit assembly plant comprise:
constraint 1: the components are assembled in sequence according to a preset process route:
Sij+xijkyikltij≤Si(j+1)，i∈{0,1,2,...n}j∈{0,1,2,...m}k∈{0,1,2,...h}
in the formula, SijIndicates the assembly start time, S, of the jth process of the ith componenti(j+1)Represents the assembly start time, t, of the (i) th part in the (j + 1) th processijThe machining time of the jth procedure of the ith part is shown, and h is the number of workers;
Figure BDA0003061779620000072
Figure BDA0003061779620000073
constraint 2: the same process for different parts can only be assembled one at a time:
Sij≠Si′j，i、i’∈{0,1,2,...n}，j∈{0,1,2,...m}
constraint 3: only one process can be assembled by one assembler at the same time:
∑xijk＝1，i∈{0,1,2,...n}，j∈{0,1,2,...m}，k∈{0,1,2,...l}
in the formula xijkThe number of the working marks representing the working personnel is 0 or 1;
constraint 4: only one working procedure can be assembled at one station and the same time:
∑yikl＝1，i∈{0,1,2,...n}，k∈{0,1,2,...m}，l∈{0,1,2,...m}
in the formula yiklThe number of the working marks representing the stations is 0 or 1;
constraint 5: the process cannot be terminated once assembly is initiated until completion:
Tij＝Sij+tij。
further, the task scheduling rule comprises a task arrival time, a task delivery time, a task assembly period, a task duration and a task urgency degree rule; the personnel station position rules comprise personnel-fixed station-fixed, task-fixed, idle station-fixed and random station position rules; setting an assembly priority and a personnel station position scheme of the task according to the rules; the optimization objectives include: optimizing a rule-based genetic algorithm, namely, optimizing a scheme formulated according to a certain obedience rule by using an improved genetic algorithm to obtain an optimal production scheduling scheme obeying the rule; secondly, optimizing a global optimal genetic algorithm, namely optimizing by using an improved genetic algorithm under the condition that the constraint of a rule is not considered and the condition that limited personnel and timely stations are met in a global range to obtain a global optimal scheduling scheme;
further, the method for solving the scheduling problem mathematical model by using the improved genetic algorithm to obtain the optimal unit type assembly workshop instant station intelligent scheduling scheme comprises the following specific steps:
step 4-1: constructing an initial condition matrix;
an n multiplied by m matrix t is adopted to represent the assembly time length of each procedure of each part, the arrangement of each row element in the matrix obeys the task scheduling rule, namely the priority of the task is increased or decreased with the increase of the number of the rows;
Figure BDA0003061779620000081
in the formula tijShowing the assembly time of the jth procedure of the ith part;
an n x m matrix p is adopted to represent the station position condition of each station worker, and the arrangement of each element in the matrix obeys the personnel station position rule;
Figure BDA0003061779620000091
in the formula pijAn assembler code indicating the jth process of the ith component;
step 4-2: population individual gene coding and decoding:
according to the goal of optimization, the coding of genes is divided into two cases: firstly, optimizing a rule-based genetic algorithm, and scheduling tasks based on the rule not only considering order sequence and process sequence, but also considering worker station positions corresponding to each process, so that the coding of genes follows scheduling rules and personnel station position rules of the tasks, the coding of individual gene sequences is coded according to the form of former work and later work, and the tasks with high priority are scheduled to the front gene sequences according to a certain probability q; secondly, optimizing a global optimal genetic algorithm, and obtaining a global optimal production scheduling scheme by adopting a form of a front-end and a rear-end gene coding without considering rule constraint; the decoding process is the inverse process of the encoding process;
step 4-3: creating an initial population;
generating a plurality of initial population sequences in a random generation mode; forming an initial population G ═ G1,g2,…,gp) Wherein p is the number of individuals in the set initial population;
step 4-4: carrying out individual gene sequence cross variation in the population by adopting a two-point corresponding method;
and 4-5: constructing a fitness function, and solving the individual fitness value f in the populationfitness：
Figure BDA0003061779620000092
And 4-6: selecting individuals in the population by adopting a method combining optimal individual retention and tournament selection;
and 4-7: judging whether a stopping criterion of a genetic algorithm is met, if so, outputting an optimal unit type assembly workshop instant station intelligent scheduling scheme, and if not, continuing to execute;
and 4-8: and generating a scheduling Gantt chart according to the output optimal scheduling scheme, and issuing the production scheme to a workshop.
Further, the population individual gene encoding and decoding are described as follows:
the population individual gene comprises working procedures and worker station positions, the total length of the gene sequence is set to be 2 x m x n, the front m x n elements are used for carrying out gene arrangement on each working procedure of each component, meanwhile, the elements in the matrix p are arranged to form the rear m x n gene sequence of the gene according to the index value of the working procedure arrangement, and the decoding process is the reverse process of the encoding process.
Further, the method for performing individual gene sequence cross variation in the population by adopting the two-point correspondence method comprises the following specific steps:
step 4-4-1: rule-based genetic algorithm optimization:
A. the cross operation adopts station-to-station corresponding POX cross, comprising the following steps:
1) randomly selecting a numbered part R and a worker station corresponding to each process of the part R from all product types as fixed position genes;
2) respectively copying the part R process sequences of two parents P1 and P2 and the worker site genes corresponding to each process into descendants C1 and C2 with the maintenance positions unchanged;
3) copying the remaining products in the product sequences in P1 and P2 to C2 and C1 in sequence;
B. variation of gene sequence:
the mutation operation adopts the mutual change mutation corresponding to the I-bit: randomly selecting two positions on a product sequence, then exchanging genes on the two positions, and simultaneously exchanging worker station position genes corresponding to the two position procedures;
step 4-4-2: optimizing a global optimal genetic algorithm;
step 4-4-2-1: gene sequence crossing;
A. the cross operation adopts subsection cross, the process section gene sequence cross and POX cross, and comprises the following steps:
1) randomly selecting a numbered part R and a worker station corresponding to each process of the R part from all product types as fixed position genes;
2) respectively copying the part R process sequences of two parents P1 and P2 and the worker site genes corresponding to each process into descendants C1 and C2 with the maintenance positions unchanged;
3) copying the remaining products in the product sequences in P1 and P2 to C2 and C1 in sequence;
B. worker station segment genes are uniformly crossed, and the method comprises the following steps:
1) randomly selecting a plurality of integers in a robot selection sequence length interval [1, M ];
2) generating integers according to step 1, copying gene retention positions and sequences at corresponding positions on the parent P1 and P2 robot selection sequences into offspring C1 and C2 respectively;
3) copying the remaining genes in the robot selection sequences of P1 and P2 to C2, C1 in sequence, keeping the positions and order unchanged;
step 4-4-2-2: variation of gene sequence;
1) the variation operation adopts segmented variation, and the sequence segment genes of the part procedures adopt interchange variation: randomly selecting two positions on a product sequence, then exchanging genes on the two positions, and simultaneously exchanging the worker station position genes corresponding to the two position procedures;
2) the site gene of the worker adopts variation: randomly selecting a plurality of positions on the personnel station segment gene, and changing the personnel station at the positions into personnel codes different from the current position.
An intelligent scheduling system of a unit type assembly workshop based on limited personnel instant station positions comprises:
the model construction module is used for constructing a scheduling problem mathematical model of the instant station of the unit type assembly workshop aiming at the workshop scheduling task;
the solving module is used for solving a scheduling problem mathematical model by using an improved genetic algorithm to obtain an optimal unit type assembly workshop instant station intelligent scheduling scheme;
and the judging module is used for judging whether the scheduling task is updated or not, and if so, executing the model building module.
Further, the judging module judges as follows:
step 8-1: judging whether a new component enters a waiting component queue or not according to the production element data of the unit assembly workshop acquired in the step 1, if no new component enters, executing a step 4-8, otherwise, executing a step 4-1;
step 8-2: judging whether the number of workers in the workshop is changed or not according to the production element data of the unit assembly workshop acquired in the step 1, if so, executing the step 4-1, otherwise, executing the step 4-8;
step 8-3: judging whether the number of waiting components in the waiting component queue exceeds the preset queue length or not according to the production element data of the unit assembly workshop acquired in the step 1, if so, executing the step 4-1, otherwise, returning to execute the step 4-8;
step 8-4: and (5) reconstructing the fitness function and executing the step 2.
The specific embodiment is as follows:
1. acquiring a production element data set of a unit type assembly workshop;
the unit assembly plant production element data set comprises: on duty worker data, scheduling task data, scheduling date data, and the like; the on duty worker data is the number of workers on duty in the workshop capable of engaging in assembly production; scheduling task data is data information such as the number of tasks for scheduling, the number of working procedures, the installation and adjustment time of each working procedure, the task issuing time, the task handing-over time, the total processing period of the tasks and the like; the production schedule is the time for the assembly operation to start the first process of the first job of the batch. For example, two existing on duty personnel [ a, b ] are responsible for [0,1,2] three 3 procedures to-be-assembled parts, and the production schedule is 20210203%; the process durations and other information data sets of 0 can be represented as [0,4,6,2,20210103,20220103,365,12], 0 represents the order of 0,4,6,2 represents the process durations, 20210103,20220103 represents the task issuing time and the task handing time, 365 represents the task duration, and 12 represents the total task assembling time.
2. Aiming at scheduling tasks and production element data of the unit type assembly workshop, a scheduling problem mathematical model and constraint conditions of the instant station of the unit type assembly workshop are constructed;
3. determining a task scheduling optimization strategy according to the task scheduling rule and the personnel station position rule; the optimization objectives include: optimizing a rule-based genetic algorithm, namely, optimizing a scheme formulated according to a certain obedience rule by using an improved genetic algorithm to obtain an optimal production scheduling scheme obeying the rule; secondly, optimizing a global optimal genetic algorithm, namely optimizing by using an improved genetic algorithm under the condition that the constraint of a rule is not considered and the condition that limited personnel and timely stations are met in a global range to obtain a global optimal scheduling scheme; and according to the optimization target, selecting rule-based scheduling optimization or global scheduling optimization, and determining a scheduling optimization scheme.
4. Solving the mathematical model by using an improved genetic algorithm, wherein the flow is shown in figure 2, and obtaining and outputting an optimal intelligent scheduling scheme of the instant station of the unit assembly plant;
4.1 constructing an initial condition matrix;
4.2 population individual gene coding and decoding:
taking 3 orders, 3 processes, 2 workers as an example, a task set {0,1,2} and a worker set { a, b };
based on the optimization of a regular genetic algorithm, the task priority is from high to low 0,1 and 2, the personnel position rule is used for positioning a person to be responsible for 1 and 3 positions, b is responsible for 2 positions, the task with the high priority is ranked before the task with the second priority by 70 percent, then one of the gene sequences of the task is '001012122 abaabaabaabaabaabaabaaba', and the total gene length is 3 x 2-18; from left to right in the gene sequence, the first "0" represents the 1 st procedure of the order form of the "0" number, the second "0" represents the 2 nd procedure of the order form of the "0" number, and so on; the first a in the second half part is numbered with '0' for drinking the first order in the first half part, which indicates that the worker a is in charge of the '1' station and also indicates the 1 st process of the '0' order, the third a indicates that the worker a is in charge of the '3' station and also indicates the 3 rd process of the '0' order, the first b indicates that the worker b is in charge of the '1' station and also indicates the 1 st process of the '1' order, and the like; the decoding process is the inverse process of the encoding process;
and (3) optimizing a global optimal genetic algorithm, wherein one gene sequence of the batch of tasks is as follows: "100120122 abbabaabaababa" is explained above.
4.3 creating an initial population;
4.4, carrying out individual gene sequence cross variation in the population by adopting a two-point corresponding method;
4.4.1 rules-based genetic algorithm optimization (taking human orientation as an example), as shown in FIG. 3:
A. the crossover operation uses a work-bit corresponding POX crossover (as shown in FIG. 3 (a));
B. a gene sequence variation (as shown in FIG. 3 (b));
4.4.2 Global optimal genetic Algorithm optimization, as shown in FIG. 4:
(1) gene sequence crossover (as shown in fig. 4 (a));
(2) gene sequence variation (as shown in fig. 4 (b));
4.5 constructing a fitness function, and solving the individual fitness value in the population:
Figure BDA0003061779620000131
4.6 selecting individuals in the population by adopting a method combining the optimal individual reservation and the championship selection; the specific process comprises the following steps:
(1) calculating the adaptive value of all individuals, and recording the adaptive value as Fi，0<i≤p；
(2) Adopting the best individual reservation, and selecting the front (p/2) individuals with the maximum adaptive value to enter the next generation of population;
(3) and determining the number N of the individuals selected each time by adopting an competitive bidding competition method. (binary tournament selection i.e. 2 individuals selected);
(4) and randomly selecting N individuals from the rest population (each individual has the same selection probability), and selecting the individual with the best fitness value from the N individuals into the next generation population according to the fitness value of each individual.
(5) And (4) repeating the step (4) for a plurality of times (the number of times is the size of the population) until the new population size reaches the original population size.
4.7 judging whether the stopping criterion of the genetic algorithm is met or not by the judging module, if so, outputting an optimal scheduling scheme, and if not, continuing to execute;
and 4.8, generating a scheduling Gantt chart according to the output optimal scheduling scheme, and issuing the production scheme to a workshop.
5. And (4) judging whether the task data is updated and changed, if so, triggering a re-scheduling algorithm, and returning to the step 1.

Claims (10)

1. An intelligent scheduling method for a unit type assembly workshop based on limited personnel instant station is characterized by comprising the following steps:
step 1: acquiring a production element data set of a unit type assembly workshop;
step 2: aiming at scheduling tasks and production element data of the unit type assembly workshop, a scheduling problem mathematical model of the instant station of the unit type assembly workshop is constructed;
and step 3: determining a task scheduling optimization strategy according to the task scheduling rule, the personnel station position rule and the scheduling optimization target;
and 4, step 4: solving a scheduling problem mathematical model by using an improved genetic algorithm to obtain an optimal unit type assembly workshop instant station intelligent scheduling scheme;
and 5: and (4) judging whether the scheduling task of the unit type assembly workshop has updating change, if so, returning to the step 1 to perform scheduling again.
2. The intelligent scheduling method for the unit assembly shop based on the limited personnel immediate station as claimed in claim 1, wherein the unit assembly shop production element data set comprises: on duty worker data, scheduling task data and scheduling date data;
on Shift worker data, i.e., the number of workers on Shift within a unit that can engage in assembly production; scheduling task data, namely the number of tasks for scheduling, the number of working procedures, the installation and adjustment time of each working procedure, the task issuing time, the task handing-over time and the total task processing period; the production schedule is the time for the assembly operation to start the first process of the first job of the batch.
3. The intelligent scheduling method for the unit type assembly shop based on the limited personnel instant station as claimed in claim 2, wherein the scheduling problem mathematical model of the unit type assembly shop instant station is as follows:
Figure FDA0003061779610000011
wherein f represents the minimized maximum completion time, i.e., the optimization objective of scheduling; t isijThe completion time of the jth process of the ith part is shown, n is the number of tasks of the scheduling, and m is the total number of assembly processes of each task, namely the number of stations of the unit type assembly workshop.
4. The intelligent scheduling method for the unit assembly shop based on the limited personnel instant station as claimed in claim 3, wherein the constraint conditions of the scheduling problem mathematical model of the unit assembly shop instant station include:
constraint 1: the components are assembled in sequence according to a preset process route:
Sij+xijkyikltij≤Si(j+1)，i∈{0,1,2,...n}j∈{0,1,2,...m}k∈{0,1,2,...h}
in the formula, SijIndicates the assembly start time, S, of the jth process of the ith componenti(j+1)Represents the assembly start time, t, of the (i) th part in the (j + 1) th processijThe machining time of the jth procedure of the ith part is shown, and h is the number of workers;
Figure FDA0003061779610000021
Figure FDA0003061779610000022
constraint 2: the same process for different parts can only be assembled one at a time:
Sij≠Si′j，i、i’∈{0,1,2,...n}，j∈{0,1,2,...m}
constraint 3: only one process can be assembled by one assembler at the same time:
∑xijk＝1，i∈{0,1,2,...n}，j∈{0,1,2,...m}，k∈{0,1,2,...l}
in the formula xijkThe number of the working marks representing the working personnel is 0 or 1;
constraint 4: only one working procedure can be assembled at one station and the same time:
∑yikl＝1，i∈{0,1,2,...n}，k∈{0,1,2,...m}，l∈{0,1,2,...m}
in the formula yiklThe number of the working marks representing the stations is 0 or 1;
constraint 5: the process cannot be terminated once assembly is initiated until completion:
Tij＝Sij+tij。
5. the intelligent scheduling method for the unit assembly shop based on the limited personnel immediate station as claimed in claim 4, wherein the task scheduling rules comprise task arrival time, task delivery time, task assembly period, task duration and task urgency rules; the personnel station position rules comprise personnel-fixed station-fixed, task-fixed, idle station-fixed and random station position rules; the optimization strategies include rule-based genetic algorithm optimization and globally optimal genetic algorithm optimization.
6. The intelligent scheduling method for the limited personnel immediate station based unit assembly workshop according to claim 5, wherein the optimal intelligent scheduling scheme for the unit assembly workshop immediate station is obtained by solving a scheduling problem mathematical model by using an improved genetic algorithm, and the specific steps comprise:
step 4-1: constructing an initial condition matrix;
an n multiplied by m matrix t is adopted to represent the assembly time length of each procedure of each part, the arrangement of each row element in the matrix obeys the task scheduling rule, namely the priority of the task is increased or decreased with the increase of the number of the rows;
Figure FDA0003061779610000031
in the formula tijShowing the assembly time of the jth procedure of the ith part;
an n x m matrix p is adopted to represent the station position condition of each station worker, and the arrangement of each element in the matrix obeys the personnel station position rule;
Figure FDA0003061779610000032
in the formula pijAn assembler code indicating the jth process of the ith component;
step 4-2: population individual gene coding and decoding:
step 4-3: creating an initial population;
generating a plurality of initial population sequences in a random generation mode; forming an initial population G ═ G1,g2,…,gq) Wherein q is the number of individuals in the set initial population;
step 4-4: carrying out individual gene sequence cross variation in the population by adopting a two-point corresponding method;
and 4-5: constructing a fitness function, and solving the individual fitness value f in the populationfitness：
Figure FDA0003061779610000033
And 4-6: selecting individuals in the population by adopting a method combining optimal individual retention and tournament selection;
and 4-7: judging whether a stopping criterion of a genetic algorithm is met, if so, outputting an optimal unit type assembly workshop instant station intelligent scheduling scheme, and if not, continuing to execute;
and 4-8: and generating a scheduling Gantt chart according to the output optimal scheduling scheme, and issuing the production scheme to a workshop.
7. The intelligent scheduling method for the unit assembly shop based on the limited personnel immediate station as claimed in claim 6, wherein the population individual gene coding and decoding are described as follows:
the population individual gene comprises working procedures and worker station positions, the total length of the gene sequence is set to be 2 x m x n, the front m x n elements are used for carrying out gene arrangement on each working procedure of each component, meanwhile, the elements in the matrix p are arranged to form the rear m x n gene sequence of the gene according to the index value of the working procedure arrangement, and the decoding process is the reverse process of the encoding process.
8. The intelligent scheduling method for the unit assembly shop based on the limited personnel immediate station as claimed in claim 7, wherein the method of two-point correspondence is adopted to perform individual gene sequence cross variation in the population, and the specific steps are as follows:
step 4-4-1: rule-based genetic algorithm optimization:
A. the cross operation adopts station-to-station corresponding POX cross, comprising the following steps:
1) randomly selecting a numbered part R and a worker station corresponding to each process of the part R from all product types as fixed position genes;
2) respectively copying the part R process sequences of two parents P1 and P2 and the worker site genes corresponding to each process into descendants C1 and C2 with the maintenance positions unchanged;
3) copying the remaining products in the product sequences in P1 and P2 to C2 and C1 in sequence;
B. variation of gene sequence:
the mutation operation adopts the mutual change mutation corresponding to the I-bit: randomly selecting two positions on a product sequence, then exchanging genes on the two positions, and simultaneously exchanging worker station position genes corresponding to the two position procedures;
step 4-4-2: optimizing a global optimal genetic algorithm;
step 4-4-2-1: gene sequence crossing;
A. the cross operation adopts subsection cross, the process section gene sequence cross and POX cross, and comprises the following steps:
1) randomly selecting a numbered part R and a worker station corresponding to each process of the R part from all product types as fixed position genes;
2) respectively copying the part R process sequences of two parents P1 and P2 and the worker site genes corresponding to each process into descendants C1 and C2 with the maintenance positions unchanged;
3) copying the remaining products in the product sequences in P1 and P2 to C2 and C1 in sequence;
B. worker station segment genes are uniformly crossed, and the method comprises the following steps:
1) randomly selecting a plurality of integers in a robot selection sequence length interval [1, M ];
2) generating integers according to step 1, copying gene retention positions and sequences at corresponding positions on the parent P1 and P2 robot selection sequences into offspring C1 and C2 respectively;
3) copying the remaining genes in the robot selection sequences of P1 and P2 to C2, C1 in sequence, keeping the positions and order unchanged;
step 4-4-2-2: variation of gene sequence;
1) the variation operation adopts segmented variation, and the sequence segment genes of the part procedures adopt interchange variation: randomly selecting two positions on a product sequence, then exchanging genes on the two positions, and simultaneously exchanging the worker station position genes corresponding to the two position procedures;
2) the site gene of the worker adopts variation: randomly selecting a plurality of positions on the personnel station segment gene, and changing the personnel station at the positions into personnel codes different from the current position.
9. The utility model provides a unit formula assembly shop intelligence scheduling system based on instant station of limited personnel which characterized in that includes:
the model construction module is used for constructing a scheduling problem mathematical model of the instant station of the unit type assembly workshop aiming at the workshop scheduling task;
the solving module is used for solving a scheduling problem mathematical model by using an improved genetic algorithm to obtain an optimal unit type assembly workshop instant station intelligent scheduling scheme;
and the judging module is used for judging whether the scheduling task is updated or not, and if so, executing the model building module.
10. The intelligent scheduling system of the unit assembly shop based on the limited personnel immediate station as claimed in claim 8, wherein the judgment process of the judgment module is as follows:
step 8-1: judging whether a new component enters a waiting component queue or not according to the production element data of the unit assembly workshop acquired in the step 1, if no new component enters, executing a step 4-8, otherwise, executing a step 4-1;
step 8-2: judging whether the number of workers in the workshop is changed or not according to the production element data of the unit assembly workshop acquired in the step 1, if so, executing the step 4-1, otherwise, executing the step 4-8;
step 8-3: judging whether the number of waiting components in the waiting component queue exceeds the preset queue length or not according to the production element data of the unit assembly workshop acquired in the step 1, if so, executing the step 4-1, otherwise, returning to execute the step 4-8;
step 8-4: and (5) reconstructing the fitness function and executing the step 2.
CN202110516722.2A 2021-05-12 2021-05-12 Unit type assembly workshop intelligent scheduling method based on limited personnel instant station Active CN113240176B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110516722.2A CN113240176B (en) 2021-05-12 2021-05-12 Unit type assembly workshop intelligent scheduling method based on limited personnel instant station

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110516722.2A CN113240176B (en) 2021-05-12 2021-05-12 Unit type assembly workshop intelligent scheduling method based on limited personnel instant station

Publications (2)

Publication Number Publication Date
CN113240176A true CN113240176A (en) 2021-08-10
CN113240176B CN113240176B (en) 2023-06-20

Family

ID=77133606

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110516722.2A Active CN113240176B (en) 2021-05-12 2021-05-12 Unit type assembly workshop intelligent scheduling method based on limited personnel instant station

Country Status (1)

Country Link
CN (1) CN113240176B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105629927A (en) * 2015-12-18 2016-06-01 武汉开目信息技术有限责任公司 Hybrid genetic algorithm-based MES (Manufacturing Execution System) production planning and scheduling method
US20160179081A1 (en) * 2014-12-22 2016-06-23 Siemens Aktiengesellschaft Optimized Production Scheduling Using Buffer Control and Genetic Algorithm
CN107392497A (en) * 2017-08-08 2017-11-24 大连交通大学 A kind of Job-Shop system and dispatching method based on improved adaptive GA-IAGA
CN111210062A (en) * 2019-12-31 2020-05-29 深圳金赋科技有限公司 Intelligent workpiece scheduling method, equipment and medium based on genetic algorithm
CN112232548A (en) * 2020-09-14 2021-01-15 中国船舶重工集团公司第七一六研究所 Machining workshop intelligent scheduling optimization method and system based on genetic algorithm

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160179081A1 (en) * 2014-12-22 2016-06-23 Siemens Aktiengesellschaft Optimized Production Scheduling Using Buffer Control and Genetic Algorithm
CN105629927A (en) * 2015-12-18 2016-06-01 武汉开目信息技术有限责任公司 Hybrid genetic algorithm-based MES (Manufacturing Execution System) production planning and scheduling method
CN107392497A (en) * 2017-08-08 2017-11-24 大连交通大学 A kind of Job-Shop system and dispatching method based on improved adaptive GA-IAGA
CN111210062A (en) * 2019-12-31 2020-05-29 深圳金赋科技有限公司 Intelligent workpiece scheduling method, equipment and medium based on genetic algorithm
CN112232548A (en) * 2020-09-14 2021-01-15 中国船舶重工集团公司第七一六研究所 Machining workshop intelligent scheduling optimization method and system based on genetic algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
刘爱军;杨育;程文明;邢青松;陆惠;赵小华;张煜东;曾强;姚豪;: "复杂制造环境下的改进非支配排序遗传算法", 计算机集成制造系统, no. 11 *
郝琪;张绪勇;邢洁芳;: "基于遗传算法的印刷企业生产调度模型的构建", 制造业自动化, no. 01 *

Also Published As

Publication number Publication date
CN113240176B (en) 2023-06-20

Similar Documents

Publication Publication Date Title
CN109359884B (en) Aviation complex equipment structural member workshop operation planning and rescheduling method
CN111738578A (en) Discrete type workshop scheduling method under dynamic environment
CN110414863A (en) A kind of intelligence manufacture workshop resource regulating method
CN102393634A (en) Single-target multi-production line scheduling control method based on PSO-GA mixing optimization algorithm
CN111259314B (en) Production scheduling method for distributed concrete prefabricated part flow shop
CN113034026A (en) Q-learning and GA based multi-target flexible job shop scheduling self-learning method
Bokhorst et al. Lean production control at a high-variety, low-volume parts manufacturer
CN113159383A (en) Manufacturing resource reconfiguration scheduling method and system for multi-machine cooperation processing workshop
CN113159506B (en) Integrated decision-making method for task allocation and job scheduling of man-machine cooperation production unit
CN113592319A (en) INSGA-II-based flexible job shop scheduling method and device under complex constraint
CN112232548A (en) Machining workshop intelligent scheduling optimization method and system based on genetic algorithm
CN116540659B (en) Large complex product workshop scheduling method, system, equipment and medium
CN110648050B (en) Reconstruction method for converting traditional assembly line assembly into unit assembly mode
CN115952896A (en) Flexible job shop scheduling method based on material process alignment
CN117311299B (en) Factory management system and method based on multi-source heterogeneous data integration
CN107703897B (en) Collaborative navigation optimization method for fixed assembly process
CN113240176A (en) Intelligent scheduling method for unit type assembly workshop based on limited personnel instant station
CN112699544A (en) Multi-target flexible job shop scheduling method
CN111814359A (en) Discrete manufacturing-oriented integrated workshop scheduling and assembly sequence planning method
CN116774658A (en) Flexible job shop dynamic scheduling method under material limitation
CN114676987A (en) Intelligent flexible job shop active scheduling method based on hyper-heuristic algorithm
CN116027741A (en) Edge cloud collaborative artificial intelligence framework for complex manufacturing scene
CN114066312A (en) Production scheduling method, system, equipment and storage medium based on discrete manufacturing
CN112488543A (en) Intelligent work site shift arrangement method and system based on machine learning
CN116934058B (en) Product service decision method based on multi-agent reinforcement learning

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

Inventor after: He Yanli

Inventor after: Li Long

Inventor after: Zhao Xin

Inventor after: Zhang Zheng

Inventor after: Li Ziang

Inventor before: Li Long

Inventor before: He Yanli

Inventor before: Zhao Xin

Inventor before: Zhang Zheng

Inventor before: Li Ziang

CB03 Change of inventor or designer information
GR01 Patent grant
GR01 Patent grant