With the development of economic globalization,distributedmanufacturing is becomingmore andmore prevalent.Recently,integrated scheduling of distributed production and assembly has captured much concern.This research s...With the development of economic globalization,distributedmanufacturing is becomingmore andmore prevalent.Recently,integrated scheduling of distributed production and assembly has captured much concern.This research studies a distributed flexible job shop scheduling problem with assembly operations.Firstly,a mixed integer programming model is formulated to minimize the maximum completion time.Secondly,a Q-learning-assisted coevolutionary algorithmis presented to solve themodel:(1)Multiple populations are developed to seek required decisions simultaneously;(2)An encoding and decoding method based on problem features is applied to represent individuals;(3)A hybrid approach of heuristic rules and random methods is employed to acquire a high-quality population;(4)Three evolutionary strategies having crossover and mutation methods are adopted to enhance exploration capabilities;(5)Three neighborhood structures based on problem features are constructed,and a Q-learning-based iterative local search method is devised to improve exploitation abilities.The Q-learning approach is applied to intelligently select better neighborhood structures.Finally,a group of instances is constructed to perform comparison experiments.The effectiveness of the Q-learning approach is verified by comparing the developed algorithm with its variant without the Q-learning method.Three renowned meta-heuristic algorithms are used in comparison with the developed algorithm.The comparison results demonstrate that the designed method exhibits better performance in coping with the formulated problem.展开更多
A modified bottleneck-based （MB） heuristic for large-scale job-shop scheduling problems with a welldefined bottleneck is suggested, which is simpler but more tailored than the shifting bottleneck （SB） procedure. I...A modified bottleneck-based （MB） heuristic for large-scale job-shop scheduling problems with a welldefined bottleneck is suggested, which is simpler but more tailored than the shifting bottleneck （SB） procedure. In this algorithm, the bottleneck is first scheduled optimally while the non-bottleneck machines are subordinated around the solutions of the bottleneck schedule by some effective dispatching rules. Computational results indicate that the MB heuristic can achieve a better tradeoff between solution quality and computational time compared to SB procedure for medium-size problems. Furthermore, it can obtain a good solution in a short time for large-scale jobshop scheduling problems.展开更多
The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborativ...The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.展开更多
A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exp...A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.展开更多
To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transfo...To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transformed into Markov decision process,and six state features are designed to improve the state feature representation by using two-way scheduling method,including four state features that distinguish the optimal action and two state features that are related to the learning goal.An extended variant of graph isomorphic network GIN++is used to encode disjunction graphs to improve the performance and generalization ability of the model.Through iterative greedy algorithm,random strategy is generated as the initial strategy,and the action with the maximum information gain is selected to expand it to optimize the exploration ability of Actor-Critic algorithm.Through validation of the trained policy model on multiple public test data sets and comparison with other advanced DRL methods and scheduling rules,the proposed method reduces the minimum average gap by 3.49%,5.31%and 4.16%,respectively,compared with the priority rule-based method,and 5.34%compared with the learning-based method.11.97%and 5.02%,effectively improving the accuracy of DRL to solve the approximate solution of JSSP minimum completion time.展开更多
To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem. The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, o...To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem. The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, optimum solutions are obtained by sequencing A small job shop scheduling problem is solved in DNA computing, and the ＂operations＂ of the computation were performed with standard protocols, as ligation, synthesis, electrophoresis etc. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.展开更多
Existing methods of local search mostly focus on how to reach optimal solution.However,in some emergency situations,search time is the hard constraint for job shop scheduling problem while optimal solution is not nece...Existing methods of local search mostly focus on how to reach optimal solution.However,in some emergency situations,search time is the hard constraint for job shop scheduling problem while optimal solution is not necessary.In this situation,the existing method of local search is not fast enough.This paper presents an emergency local search(ELS) approach which can reach feasible and nearly optimal solution in limited search time.The ELS approach is desirable for the aforementioned emergency situations where search time is limited and a nearly optimal solution is sufficient,which consists of three phases.Firstly,in order to reach a feasible and nearly optimal solution,infeasible solutions are repaired and a repair technique named group repair is proposed.Secondly,in order to save time,the amount of local search moves need to be reduced and this is achieved by a quickly search method named critical path search(CPS).Finally,CPS sometimes stops at a solution far from the optimal one.In order to jump out the search dilemma of CPS,a jump technique based on critical part is used to improve CPS.Furthermore,the schedule system based on ELS has been developed and experiments based on this system completed on the computer of Intel Pentium(R) 2.93 GHz.The experimental result shows that the optimal solutions of small scale instances are reached in 2 s,and the nearly optimal solutions of large scale instances are reached in 4 s.The proposed ELS approach can stably reach nearly optimal solutions with manageable search time,and can be applied on some emergency situations.展开更多
The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worke...The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.展开更多
A small and medium enterprises(SMEs)manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities.The optimal job shop scheduling is generated ...A small and medium enterprises(SMEs)manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities.The optimal job shop scheduling is generated by utilizing the scheduling system of the platform,and a minimum production time,i.e.,makespan decides whether the scheduling is optimal or not.This scheduling result allows manufacturers to achieve high productivity,energy savings,and customer satisfaction.Manufacturing in Industry 4.0 requires dynamic,uncertain,complex production environments,and customer-centered services.This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform.The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors.The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors:early delivery date and fulfillment of processing as many orders as possible.The genetic algorithm(GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem(JSSP)by comparing with the real-world data from a textile weaving factory in South Korea.The proposed platform will provide producers with an optimal production schedule,introduce new producers to buyers,and eventually foster relationships and mutual economic interests.展开更多
In real production,machines are operated by workers,and the constraints of worker flexibility should be considered.The flexible job shop scheduling problem with both machine and worker resources(DRCFJSP)has become a r...In real production,machines are operated by workers,and the constraints of worker flexibility should be considered.The flexible job shop scheduling problem with both machine and worker resources(DRCFJSP)has become a research hotspot in recent years.In this paper,DRCFJSP with the objective of minimizing the makespan is studied,and it should solve three sub-problems:machine allocation,worker allocation,and operations sequencing.To solve DRCFJSP,a novel hybrid algorithm(CEAM-CP)of cooperative evolutionary algorithm with multiple populations(CEAM)and constraint programming(CP)is proposed.Specifically,the CEAM-CP algorithm is comprised of two main stages.In the first stage,CEAM is used based on three-layer encoding and full active decoding.Moreover,CEAM has three populations,each of which corresponds to one layer encoding and determines one sub-problem.Moreover,each population evolves cooperatively by multiple cross operations.To further improve the solution quality obtained by CEAM,CP is adopted in the second stage.Experiments are conducted on 13 benchmark instances to assess the effectiveness of multiple crossover operations,CP,and CEAM-CP.Most importantly,the proposed CEAM-CP improves 9 best-known solutions out of 13 benchmark instances.展开更多
Despite advancements in optimisation techniques,existing flexible job shop problem(FJSP)models are reactive and struggle with dynamic scheduling.Digital twin(DT)technology offers a solution.This study integrates DT wi...Despite advancements in optimisation techniques,existing flexible job shop problem(FJSP)models are reactive and struggle with dynamic scheduling.Digital twin(DT)technology offers a solution.This study integrates DT with deep reinforcement learning(DRL)for proactive dynamic scheduling.A digital twin-based framework with multi-agent proximal policy optimisation(PPO)was used to adapt scheduling strategies in real-time.The virtual environment simulates production,predicts disruptions,and enables proactive adjustment.The dynamic flexible job shop problem(DFJSP)is modelled as a Markov decision process(MDP)with agents introduced to optimise decisions using DRL.The state and action spaces for the machine and job agents were designed to capture the real-time states.The reward function combines global(makespan)and local(machine utilisation)rewards.Multi-agent PPO trains agents in a virtual environment based on DT interactions.Experiments show that the method outperforms traditional rules and genetic algorithms,particularly in large-scale problems.Additionally,a real-world case study proved its effectiveness in managing machine failures and ensuring on-time completion with minimal deviation in dynamic and uncertain environments.展开更多
With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing importance.The Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds...With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing importance.The Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerable theoretical research value.However,finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP.A co-operative-guided ant colony optimization algorithm with knowledge learning(namely KLCACO)is proposed to address this difficulty.This algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge.A solution construction scheme based on scheduling knowledge learning is proposed for KLCACO.The problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions.A pheromone guidance mechanism,which is based on a collaborative machine strategy,is used to simplify information learning and the problem space by collaborating with different machine processing orders.Additionally,the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution,expanding the search space of the algorithm and accelerating its convergence.The KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets,comprising 48 benchmark test cases in total.The effectiveness of the proposed algorithm in addressing JSPs is validated,demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.展开更多
Firstly an overview of the potential impact on work-in-process(WIP)and lead time is provided when transfer lot sizes are undifferentiated from processing lot sizes.Simple performance examples are compared to those fro...Firstly an overview of the potential impact on work-in-process(WIP)and lead time is provided when transfer lot sizes are undifferentiated from processing lot sizes.Simple performance examples are compared to those from a shop with one-piece transfer lots.Next,a mathematical programming model for minimizing lead time in the mixed-model job shop is presented,in which one-piece transfer lots are used.Key factors affecting lead time are found by analyzing the sum of the longest setup time of individual items among the shared processes(SLST)and the longest processing time of individual items among processes(LPT).And lead time can be minimized by cutting down the SLST and LPT.Reduction of the SLST is described as a traveling salesman problem(TSP),and the minimum of the SLST is solved through job shop scheduling.Removing the bottleneck and leveling the production line optimize the LPT.If the number of items produced is small,the routings are relatively short,and items and facilities are changed infrequently,the optimal schedule will remain valid.Finally a brief example serves to illustrate the method.展开更多
文摘With the development of economic globalization,distributedmanufacturing is becomingmore andmore prevalent.Recently,integrated scheduling of distributed production and assembly has captured much concern.This research studies a distributed flexible job shop scheduling problem with assembly operations.Firstly,a mixed integer programming model is formulated to minimize the maximum completion time.Secondly,a Q-learning-assisted coevolutionary algorithmis presented to solve themodel:(1)Multiple populations are developed to seek required decisions simultaneously;(2)An encoding and decoding method based on problem features is applied to represent individuals;(3)A hybrid approach of heuristic rules and random methods is employed to acquire a high-quality population;(4)Three evolutionary strategies having crossover and mutation methods are adopted to enhance exploration capabilities;(5)Three neighborhood structures based on problem features are constructed,and a Q-learning-based iterative local search method is devised to improve exploitation abilities.The Q-learning approach is applied to intelligently select better neighborhood structures.Finally,a group of instances is constructed to perform comparison experiments.The effectiveness of the Q-learning approach is verified by comparing the developed algorithm with its variant without the Q-learning method.Three renowned meta-heuristic algorithms are used in comparison with the developed algorithm.The comparison results demonstrate that the designed method exhibits better performance in coping with the formulated problem.
基金the National Natural Science Foundation of China (6027401360474002)Shanghai Development Found for Science and Technology (04DZ11008).
文摘A modified bottleneck-based （MB） heuristic for large-scale job-shop scheduling problems with a welldefined bottleneck is suggested, which is simpler but more tailored than the shifting bottleneck （SB） procedure. In this algorithm, the bottleneck is first scheduled optimally while the non-bottleneck machines are subordinated around the solutions of the bottleneck schedule by some effective dispatching rules. Computational results indicate that the MB heuristic can achieve a better tradeoff between solution quality and computational time compared to SB procedure for medium-size problems. Furthermore, it can obtain a good solution in a short time for large-scale jobshop scheduling problems.
基金supported by the National Key R&D Program of China(2018AAA0101700)the Program for HUST Academic Frontier Youth Team(2017QYTD04).
文摘The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.
文摘A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.
基金Shaanxi Provincial Key Research and Development Project(2023YBGY095)and Shaanxi Provincial Qin Chuangyuan"Scientist+Engineer"project(2023KXJ247)Fund support.
文摘To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transformed into Markov decision process,and six state features are designed to improve the state feature representation by using two-way scheduling method,including four state features that distinguish the optimal action and two state features that are related to the learning goal.An extended variant of graph isomorphic network GIN++is used to encode disjunction graphs to improve the performance and generalization ability of the model.Through iterative greedy algorithm,random strategy is generated as the initial strategy,and the action with the maximum information gain is selected to expand it to optimize the exploration ability of Actor-Critic algorithm.Through validation of the trained policy model on multiple public test data sets and comparison with other advanced DRL methods and scheduling rules,the proposed method reduces the minimum average gap by 3.49%,5.31%and 4.16%,respectively,compared with the priority rule-based method,and 5.34%compared with the learning-based method.11.97%and 5.02%,effectively improving the accuracy of DRL to solve the approximate solution of JSSP minimum completion time.
基金This Project was supported by the National Nature Science Foundation (60274026 ,30570431) China Postdoctoral Sci-ence Foundation Natural +1 种基金Science Foundation of Educational Government of Anhui Province of China Excellent Youth Scienceand Technology Foundation of Anhui Province of China (06042088) and Doctoral Foundation of Anhui University of Scienceand Technology
文摘To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem. The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, optimum solutions are obtained by sequencing A small job shop scheduling problem is solved in DNA computing, and the ＂operations＂ of the computation were performed with standard protocols, as ligation, synthesis, electrophoresis etc. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.
基金supported by National Natural Science Foundation of China(Grant No.61004109)Fundamental Research Funds for the Central Universities of China(Grant No.FRF-TP-12-071A)
文摘Existing methods of local search mostly focus on how to reach optimal solution.However,in some emergency situations,search time is the hard constraint for job shop scheduling problem while optimal solution is not necessary.In this situation,the existing method of local search is not fast enough.This paper presents an emergency local search(ELS) approach which can reach feasible and nearly optimal solution in limited search time.The ELS approach is desirable for the aforementioned emergency situations where search time is limited and a nearly optimal solution is sufficient,which consists of three phases.Firstly,in order to reach a feasible and nearly optimal solution,infeasible solutions are repaired and a repair technique named group repair is proposed.Secondly,in order to save time,the amount of local search moves need to be reduced and this is achieved by a quickly search method named critical path search(CPS).Finally,CPS sometimes stops at a solution far from the optimal one.In order to jump out the search dilemma of CPS,a jump technique based on critical part is used to improve CPS.Furthermore,the schedule system based on ELS has been developed and experiments based on this system completed on the computer of Intel Pentium(R) 2.93 GHz.The experimental result shows that the optimal solutions of small scale instances are reached in 2 s,and the nearly optimal solutions of large scale instances are reached in 4 s.The proposed ELS approach can stably reach nearly optimal solutions with manageable search time,and can be applied on some emergency situations.
基金supported by the Natural Science Foundation of Anhui Province(Grant Number 2208085MG181)the Science Research Project of Higher Education Institutions in Anhui Province,Philosophy and Social Sciences(Grant Number 2023AH051063)the Open Fund of Key Laboratory of Anhui Higher Education Institutes(Grant Number CS2021-ZD01).
文摘The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.
基金This work was supported by the Technology Innovation Program 20004205(the development of smart collaboration manufacturing innovation service platform in the textile industry by producer-buyer)funded by MOTIE,Korea.
文摘A small and medium enterprises(SMEs)manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities.The optimal job shop scheduling is generated by utilizing the scheduling system of the platform,and a minimum production time,i.e.,makespan decides whether the scheduling is optimal or not.This scheduling result allows manufacturers to achieve high productivity,energy savings,and customer satisfaction.Manufacturing in Industry 4.0 requires dynamic,uncertain,complex production environments,and customer-centered services.This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform.The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors.The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors:early delivery date and fulfillment of processing as many orders as possible.The genetic algorithm(GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem(JSSP)by comparing with the real-world data from a textile weaving factory in South Korea.The proposed platform will provide producers with an optimal production schedule,introduce new producers to buyers,and eventually foster relationships and mutual economic interests.
基金supported by the Funds for the National Natural Science Foundation of China(Nos.52205529 and 62303204)Natural Science Foundation of Shandong Province(Nos.ZR2021QE195 and ZR2021QF036)+2 种基金Youth Innovation Team Program of Shandong Higher Education Institution(No.2023KJ206)Guangyue。Youth Scholar Innovation Talent Program support received from Liaocheng University(No.LCUGYTD2022-03)Foundation of Young Talent of Lifting engineering for Science and Technology in Shandong,China(No.SDAST2024QTA074).
文摘In real production,machines are operated by workers,and the constraints of worker flexibility should be considered.The flexible job shop scheduling problem with both machine and worker resources(DRCFJSP)has become a research hotspot in recent years.In this paper,DRCFJSP with the objective of minimizing the makespan is studied,and it should solve three sub-problems:machine allocation,worker allocation,and operations sequencing.To solve DRCFJSP,a novel hybrid algorithm(CEAM-CP)of cooperative evolutionary algorithm with multiple populations(CEAM)and constraint programming(CP)is proposed.Specifically,the CEAM-CP algorithm is comprised of two main stages.In the first stage,CEAM is used based on three-layer encoding and full active decoding.Moreover,CEAM has three populations,each of which corresponds to one layer encoding and determines one sub-problem.Moreover,each population evolves cooperatively by multiple cross operations.To further improve the solution quality obtained by CEAM,CP is adopted in the second stage.Experiments are conducted on 13 benchmark instances to assess the effectiveness of multiple crossover operations,CP,and CEAM-CP.Most importantly,the proposed CEAM-CP improves 9 best-known solutions out of 13 benchmark instances.
基金supported in part by the Natural Science Foundation of Jiangsu Province of China(BK20241780)Changzhou Science and Technology Program Project(CM20223014 and CJ20220207)Changzhou Science and Technology Support Plan(Social Development)Project(CE20205045).
文摘Despite advancements in optimisation techniques,existing flexible job shop problem(FJSP)models are reactive and struggle with dynamic scheduling.Digital twin(DT)technology offers a solution.This study integrates DT with deep reinforcement learning(DRL)for proactive dynamic scheduling.A digital twin-based framework with multi-agent proximal policy optimisation(PPO)was used to adapt scheduling strategies in real-time.The virtual environment simulates production,predicts disruptions,and enables proactive adjustment.The dynamic flexible job shop problem(DFJSP)is modelled as a Markov decision process(MDP)with agents introduced to optimise decisions using DRL.The state and action spaces for the machine and job agents were designed to capture the real-time states.The reward function combines global(makespan)and local(machine utilisation)rewards.Multi-agent PPO trains agents in a virtual environment based on DT interactions.Experiments show that the method outperforms traditional rules and genetic algorithms,particularly in large-scale problems.Additionally,a real-world case study proved its effectiveness in managing machine failures and ensuring on-time completion with minimal deviation in dynamic and uncertain environments.
基金supported by the National Natural Science Foundation of China(Nos.62366003 and 62066019)the Natural Science Foundation of Jiangxi Province(No.20232BAB202046)the Graduate Innovation Foundation of Jiangxi University of Science and Technology(No.XY2022-S040).
文摘With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing importance.The Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerable theoretical research value.However,finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP.A co-operative-guided ant colony optimization algorithm with knowledge learning(namely KLCACO)is proposed to address this difficulty.This algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge.A solution construction scheme based on scheduling knowledge learning is proposed for KLCACO.The problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions.A pheromone guidance mechanism,which is based on a collaborative machine strategy,is used to simplify information learning and the problem space by collaborating with different machine processing orders.Additionally,the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution,expanding the search space of the algorithm and accelerating its convergence.The KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets,comprising 48 benchmark test cases in total.The effectiveness of the proposed algorithm in addressing JSPs is validated,demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.
基金This project is supported by National Natural Science Foundation of China(No.70372062,No.70572044)Program for New Century Excellent Talents in University of China(No.NCET-04-0240).
文摘Firstly an overview of the potential impact on work-in-process(WIP)and lead time is provided when transfer lot sizes are undifferentiated from processing lot sizes.Simple performance examples are compared to those from a shop with one-piece transfer lots.Next,a mathematical programming model for minimizing lead time in the mixed-model job shop is presented,in which one-piece transfer lots are used.Key factors affecting lead time are found by analyzing the sum of the longest setup time of individual items among the shared processes(SLST)and the longest processing time of individual items among processes(LPT).And lead time can be minimized by cutting down the SLST and LPT.Reduction of the SLST is described as a traveling salesman problem(TSP),and the minimum of the SLST is solved through job shop scheduling.Removing the bottleneck and leveling the production line optimize the LPT.If the number of items produced is small,the routings are relatively short,and items and facilities are changed infrequently,the optimal schedule will remain valid.Finally a brief example serves to illustrate the method.
