With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ...With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.展开更多
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
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
Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been...Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.展开更多
As the penetration rate of renewable energy sources(RES)gradually increases,demand-side resources(DSR)should be fully utilized to provide flexibility and rapidly respond to real-time power supply-demand imbalance.Howe...As the penetration rate of renewable energy sources(RES)gradually increases,demand-side resources(DSR)should be fully utilized to provide flexibility and rapidly respond to real-time power supply-demand imbalance.However,scheduling a large number of DSR clusters will inevitably bring unbearable transmission delay,and computation delay,which in turn lead to lower response speeds.This paper examines flexibility scheduling of DSR clusters within a smart distribution network(SDN)in view of both kinds of delay.Building upon a SDN model,maximum schedulable flexibility of DSR clusters is first quantified.Then,a flexibility response curve is analyzed to reflect the effect of delay on flexibility scheduling.Aiming at reducing flexibility shortage brought by delay,we propose a modified flexibility scheduling strategy based on cloud-edge collaboration.Compared with traditional strategy,centralized optimization is replaced by distributed optimization to consider both economic efficiency and effect of delay.Besides,an offloading strategy is also formulated to decide optimal edge nodes and corresponding wired paths for edge computations.In a case study,we evaluate scheduled flexibility,operational cost,average delay and the chosen edge nodes for edge computations with traditional strategy and our proposed strategy.Evaluation results show the proposed strategy can significantly reduce the effect of delay on flexibility scheduling,and guarantee the optimality of operational cost to some extent.展开更多
In the context of large-scale photovoltaic integration,flexibility scheduling is essential to ensure the secure and efficient operation of distribution networks(DNs).Recently,deep reinforcement learning(DRL)has been w...In the context of large-scale photovoltaic integration,flexibility scheduling is essential to ensure the secure and efficient operation of distribution networks(DNs).Recently,deep reinforcement learning(DRL)has been widely applied to scheduling problems.However,most methods neglect the vulnerability of DRL to state adversarial attacks such as load redistribution attacks,significantly undermining its security and reliability.To this end,a flexibility scheduling method is proposed based on robust graph DRL(RoGDRL).A flexibility gain improvement model considering temperature-dependent resistance is first proposed,which considers weather factors as additional variables to enhance the precision of flexibility analysis.Based on this,a state-adversarial two-player zero-sum Markov game(SA-TZMG)model is proposed,which converts the robust DRL scheduling problem into a Nash equilibrium problem.The proposed SA-TZMG model considers the physical constraints of state attacks that guarantee the maximal flexibility gain for the defender when confronted with the most sophisticated and stealthy attacker.A two-stage RoGDRL algorithm is proposed,which introduces the graph sample and aggregate(GraphSAGE)driven soft actor-critic to capture the complex feature about the neighbors of nodes and their properties via inductive learning,thereby solving the Nash equilibrium policies more efficiently.Simulations based on the modified IEEE 123-bus system demonstrates the efficacy of the proposed method.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
It is urgent to effectively improve the production efficiency in the running process of manufacturing systems through a new generation of information technology.According to the current growing trend of the internet o...It is urgent to effectively improve the production efficiency in the running process of manufacturing systems through a new generation of information technology.According to the current growing trend of the internet of things(IOT)in the manufacturing industry,aiming at the capacitor manufacturing plant,a multi-level architecture oriented to IOT-based manufacturing environment is established for a flexible flow-shop scheduling system.Next,according to multi-source manufacturing information driven in the manufacturing execution process,a scheduling optimization model based on the lot-streaming strategy is proposed under the framework.An improved distribution estimation algorithm is developed to obtain the optimal solution of the problem by balancing local search and global search.Finally,experiments are carried out and the results verify the feasibility and effectiveness of the proposed approach.展开更多
The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied....The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness.展开更多
In a typical discrete manufacturing process,a new type of reconfigurable production line is introduced,which aims to help small-and mid-size enterprises to improve machine utilization and reduce production cost.In ord...In a typical discrete manufacturing process,a new type of reconfigurable production line is introduced,which aims to help small-and mid-size enterprises to improve machine utilization and reduce production cost.In order to effectively handle the production scheduling problem for the manufacturing system,an improved multi-objective particle swarm optimization algorithm based on Brownian motion(MOPSO-BM)is proposed.Since the existing MOPSO algorithms are easily stuck in the local optimum,the global search ability of the proposed method is enhanced based on the random motion mechanism of the BM.To further strengthen the global search capacity,a strategy of fitting the inertia weight with the piecewise Gaussian cumulative distribution function(GCDF)is included,which helps to maintain an excellent convergence rate of the algorithm.Based on the commonly used indicators generational distance(GD)and hypervolume(HV),we compare the MOPSO-BM with several other latest algorithms on the benchmark functions,and it shows a better overall performance.Furthermore,for a real reconfigurable production line of smart home appliances,three algorithms,namely non-dominated sorting genetic algorithm-II(NSGA-II),decomposition-based MOPSO(dMOPSO)and MOPSO-BM,are applied to tackle the scheduling problem.It is demonstrated that MOPSO-BM outperforms the others in terms of convergence rate and quality of solutions.展开更多
Job shop scheduling(JS)is an important technology for modern manufacturing.Flexible job shop scheduling(FJS)is critical in JS,and it has been widely employed in many industries,including aerospace and energy.FJS enabl...Job shop scheduling(JS)is an important technology for modern manufacturing.Flexible job shop scheduling(FJS)is critical in JS,and it has been widely employed in many industries,including aerospace and energy.FJS enables any machine from a certain set to handle an operation,and this is an NP-hard problem.Furthermore,due to the requirements in real-world cases,multi-objective FJS is increasingly widespread,thus increasing the challenge of solving the FJS problems.As a result,it is necessary to develop a novel method to address this challenge.To achieve this goal,a novel collaborative evolutionary algorithmwith two-population based on Pareto optimality is proposed for FJS,which improves the solutions of FJS by interacting in each generation.In addition,several experimental results have demonstrated that the proposed method is promising and effective for multi-objective FJS,which has discovered some new Pareto solutions in the well-known benchmark problems,and some solutions can dominate the solutions of some other methods.展开更多
In the view of staff shortages and the huge inventory of products in the current market, we put forward a personnel scheduling model in the target of closing to the delivery date considering the parallelism. Then we d...In the view of staff shortages and the huge inventory of products in the current market, we put forward a personnel scheduling model in the target of closing to the delivery date considering the parallelism. Then we designed a scheduling algorithm based on genetic algorithm and proposed a flexible parallel decoding method which take full use of the personal capacity. Case study results indicate that the flexible personnel scheduling considering the order-shop scheduling, machine automatic capabilities and personnel flexible in the target of closing to the delivery date optimize the allocation of human resources, then maximize the efficiency.展开更多
Production scheduling involves all activities of building production schedules,including coordinating and assigning activities to each person,group of people,or machine and arranging work orders in each workplace.Prod...Production scheduling involves all activities of building production schedules,including coordinating and assigning activities to each person,group of people,or machine and arranging work orders in each workplace.Production scheduling must solve all problems such as minimizing customer wait time,storage costs,and production time;and effectively using the enterprise’s human resources.This paper studies the application of flexible job shop modelling on scheduling a woven labelling process.The labelling process includes several steps which are handled in different work-stations.Each workstation is also comprised of several identical parallel machines.In this study,job splitting is allowed so that the power of work stations can be utilized better.The final objective is to minimize the total completion time of all jobs.The results show a significant improvement since the new planning may save more than 60%of lead time compared to the current schedule.The contribution of this research is to propose a flexible job shop model for scheduling a woven labelling process.The proposed approach can also be applied to support complex production scheduling processes under fuzzy environments in different industries.A practical case study demonstrates the effectiveness of the proposed model.展开更多
This study developed a user equilibrium traffic assignment model based on trip-chains with flexible activity scheduling order and derived the corresponding optimality conditions. We based on the gradient projection me...This study developed a user equilibrium traffic assignment model based on trip-chains with flexible activity scheduling order and derived the corresponding optimality conditions. We based on the gradient projection method to develop a solution algorithm, the accuracy of which was verified using the test network of UTown. This model could be used to estimate the transportation demands with and without activities scheduling restriction between OD （origin-destination） pairs based on trip-chains, as well as based on trips. Thus, the proposed model is more generalization than conventional trip based or trip-chain based traffic assignment models.展开更多
In flexible job-shop batch scheduling problem, the optimal lot-size of different process is not always the same because of different processing time and set-up time. Even for the same process of the same workpiece, th...In flexible job-shop batch scheduling problem, the optimal lot-size of different process is not always the same because of different processing time and set-up time. Even for the same process of the same workpiece, the choice of machine also affects the optimal lot-size. In addition, different choices of lot-size between the constrained processes will impact the manufacture efficiency. Considering that each process has its own appropriate lot-size, we put forward the concept of scheduling with lot-splitting based on process and set up the scheduling model of lot-splitting to critical path process as the core. The model could update the set of batch process and machine selection strategy dynamically to determine processing route and arrange proper lot-size for different processes, to achieve the purpose of optimizing the makespan and reducing the processing batches effectively. The experiment results show that, comparing with lot-splitting scheduling scheme based on workpiece, this model optimizes the makespan and improves the utilization efficiency of the machine. It also greatly decreases the machined batches （42%） and reduces the complexity of shop scheduling production management.展开更多
There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling,but most scholars neglect the connection between them,which means the purpose of both models is...There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling,but most scholars neglect the connection between them,which means the purpose of both models is to simulate a more realistic factory environment.From this perspective,the solutions can be more precise and practical if both issues are considered simultaneously.Therefore,the deterioration effect is treated as a part of the fuzzy job shop scheduling problem in this paper,which means the linear increase of a certain processing time is transformed into an internal linear shift of a triangle fuzzy processing time.Apart from that,many other contributions can be stated as follows.A new algorithm called reinforcement learning based biased bi-population evolutionary algorithm(RB2EA)is proposed,which utilizes Q-learning algorithm to adjust the size of the two populations and the interaction frequency according to the quality of population.A local enhancement method which combimes multiple local search stratgies is presented.An interaction mechanism is designed to promote the convergence of the bi-population.Extensive experiments are designed to evaluate the efficacy of RB2EA,and the conclusion can be drew that RB2EA is able to solve energy-efficient fuzzy flexible job shop scheduling problem with deteriorating jobs(EFFJSPD)efficiently.展开更多
The distributed flexible job shop scheduling problem(DFJSP),which is an extension of the flexible job shop scheduling problem,is a famous NP-complete combinatorial optimization problem.This problem is widespread in th...The distributed flexible job shop scheduling problem(DFJSP),which is an extension of the flexible job shop scheduling problem,is a famous NP-complete combinatorial optimization problem.This problem is widespread in the manufacturing industries and comprises the following three subproblems:the assignment of jobs to factories,the scheduling of operations to machines,and the sequence of operations on machines.However,studies on DFJSP are seldom because of its difficulty.This paper proposes an effective improved gray wolf optimizer(IGWO)to solve the aforementioned problem.In this algorithm,new encoding and decoding schemes are designed to represent the three subproblems and transform the encoding into a feasible schedule,respectively.Four crossover operators are developed to expand the search space.A local search strategy with the concept of a critical factory is also proposed to improve the exploitability of IGWO.Effective schedules can be obtained by changing factory assignments and operation sequences in the critical factory.The proposed IGWO algorithm is evaluated on 69 famous benchmark instances and compared with six state-of-the-art algorithms to demonstrate its efficacy considering solution quality and computational efficiency.Experimental results show that the proposed algorithm has achieved good improvement.Particularly,the proposed IGWO updates the new upper bounds of 13 difficult benchmark instances.展开更多
Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of th...Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem(MOFJSP)considering transportation time.Design/methodology/approach–A hybrid genetic algorithm(GA)approach is integrated with simulated annealing to solve the MOFJSP considering transportation time,and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.Findings–The performance of the proposed algorithm is tested on different MOFJSP taken from literature.Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution,especially when the number of jobs and the flexibility of the machine increase.Originality/value–Most of existing studies have not considered the transportation time during scheduling of jobs.The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs.Meanwhile,GA is one of primary algorithms extensively used to address MOFJSP in literature.However,to solve the MOFJSP,the original GA has a possibility to get a premature convergence and it has a slow convergence speed.To overcome these problems,a new hybrid GA is developed in this paper.展开更多
With the popularization of multi-variety and small-batch production patterns,the flexible job shop scheduling problem(FJSSP)has been widely studied.The sharing of processing resources by multiple machines frequently o...With the popularization of multi-variety and small-batch production patterns,the flexible job shop scheduling problem(FJSSP)has been widely studied.The sharing of processing resources by multiple machines frequently occurs due to space constraints in a flexible shop,which results in resource preemption for processing workpieces.Resource preemption complicates the constraints of scheduling problems that are otherwise difficult to solve.In this paper,the flexible job shop scheduling problem under the process resource preemption scenario is modeled,and a two-layer rule scheduling algorithm based on deep reinforcement learning is proposed to achieve the goal of minimum scheduling time.The simulation experiments compare our scheduling algorithm with two traditional metaheuristic optimization algorithms among different processing resource distribution scenarios in static scheduling environment.The results suggest that the two-layer rule scheduling algorithm based on deep reinforcement learning is more effective than the meta-heuristic algorithm in the application of processing resource preemption scenarios.Ablation experiments,generalization,and dynamic experiments are performed to demonstrate the excellent performance of our method for FJSSP under resource preemption.展开更多
The simulation on benchmarks is a very simple and efficient method to evaluate the performance of the algorithm for solving flexible job shop scheduling model.Due to the assignment and scheduling decisions,flexible jo...The simulation on benchmarks is a very simple and efficient method to evaluate the performance of the algorithm for solving flexible job shop scheduling model.Due to the assignment and scheduling decisions,flexible job shop scheduling problem(FJSP)becomes extremely hard to solve for production management.A discrete multi-objective particle swarm optimization(PSO)and simulated annealing(SA)algorithm with variable neighborhood search is developed for FJSP with three criteria:the makespan,the total workload and the critical machine workload.Firstly,a discrete PSO is designed and then SA algorithm performs variable neighborhood search integrating two neighborhoods on public critical block to enhance the search ability.Finally,the selection strategy of the personal-best individual and global-best individual from the external archive is developed in multi-objective optimization.Through the experimental simulation on matlab,the tests on Kacem instances,Brdata instances and BCdata instances show that the modified discrete multi-objective PSO algorithm is a promising and valid method for optimizing FJSP with three criteria.展开更多
基金funded by Jilin Province Science and Technology Development Plan Project,grant number 20220203163SF.
文摘With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.
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.
文摘Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.
文摘As the penetration rate of renewable energy sources(RES)gradually increases,demand-side resources(DSR)should be fully utilized to provide flexibility and rapidly respond to real-time power supply-demand imbalance.However,scheduling a large number of DSR clusters will inevitably bring unbearable transmission delay,and computation delay,which in turn lead to lower response speeds.This paper examines flexibility scheduling of DSR clusters within a smart distribution network(SDN)in view of both kinds of delay.Building upon a SDN model,maximum schedulable flexibility of DSR clusters is first quantified.Then,a flexibility response curve is analyzed to reflect the effect of delay on flexibility scheduling.Aiming at reducing flexibility shortage brought by delay,we propose a modified flexibility scheduling strategy based on cloud-edge collaboration.Compared with traditional strategy,centralized optimization is replaced by distributed optimization to consider both economic efficiency and effect of delay.Besides,an offloading strategy is also formulated to decide optimal edge nodes and corresponding wired paths for edge computations.In a case study,we evaluate scheduled flexibility,operational cost,average delay and the chosen edge nodes for edge computations with traditional strategy and our proposed strategy.Evaluation results show the proposed strategy can significantly reduce the effect of delay on flexibility scheduling,and guarantee the optimality of operational cost to some extent.
基金supported by the National Natural Science Foundation of China(No.52077149)Key Program for National Natural Science Foundation of China Joint Funds(No.U2166202)National Natural Science Foundation of China Young Scientist Fund(No.52407134).
文摘In the context of large-scale photovoltaic integration,flexibility scheduling is essential to ensure the secure and efficient operation of distribution networks(DNs).Recently,deep reinforcement learning(DRL)has been widely applied to scheduling problems.However,most methods neglect the vulnerability of DRL to state adversarial attacks such as load redistribution attacks,significantly undermining its security and reliability.To this end,a flexibility scheduling method is proposed based on robust graph DRL(RoGDRL).A flexibility gain improvement model considering temperature-dependent resistance is first proposed,which considers weather factors as additional variables to enhance the precision of flexibility analysis.Based on this,a state-adversarial two-player zero-sum Markov game(SA-TZMG)model is proposed,which converts the robust DRL scheduling problem into a Nash equilibrium problem.The proposed SA-TZMG model considers the physical constraints of state attacks that guarantee the maximal flexibility gain for the defender when confronted with the most sophisticated and stealthy attacker.A two-stage RoGDRL algorithm is proposed,which introduces the graph sample and aggregate(GraphSAGE)driven soft actor-critic to capture the complex feature about the neighbors of nodes and their properties via inductive learning,thereby solving the Nash equilibrium policies more efficiently.Simulations based on the modified IEEE 123-bus system demonstrates the efficacy of the proposed method.
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.
基金supported by the National Natural Science Foundations of China(No. 51875171)
文摘It is urgent to effectively improve the production efficiency in the running process of manufacturing systems through a new generation of information technology.According to the current growing trend of the internet of things(IOT)in the manufacturing industry,aiming at the capacitor manufacturing plant,a multi-level architecture oriented to IOT-based manufacturing environment is established for a flexible flow-shop scheduling system.Next,according to multi-source manufacturing information driven in the manufacturing execution process,a scheduling optimization model based on the lot-streaming strategy is proposed under the framework.An improved distribution estimation algorithm is developed to obtain the optimal solution of the problem by balancing local search and global search.Finally,experiments are carried out and the results verify the feasibility and effectiveness of the proposed approach.
基金supported by the National Key Research and Development Program of China (No.2020YFB1710500)the National Natural Science Foundation of China(No.51805253)the Fundamental Research Funds for the Central Universities(No. NP2020304)
文摘The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness.
基金supported by the National Natural Science Foundation of China(71871203,52005447,L1924063)Zhejiang Provincial Natural Science Foundation of China(LY18G010017,LQ21E050014).
文摘In a typical discrete manufacturing process,a new type of reconfigurable production line is introduced,which aims to help small-and mid-size enterprises to improve machine utilization and reduce production cost.In order to effectively handle the production scheduling problem for the manufacturing system,an improved multi-objective particle swarm optimization algorithm based on Brownian motion(MOPSO-BM)is proposed.Since the existing MOPSO algorithms are easily stuck in the local optimum,the global search ability of the proposed method is enhanced based on the random motion mechanism of the BM.To further strengthen the global search capacity,a strategy of fitting the inertia weight with the piecewise Gaussian cumulative distribution function(GCDF)is included,which helps to maintain an excellent convergence rate of the algorithm.Based on the commonly used indicators generational distance(GD)and hypervolume(HV),we compare the MOPSO-BM with several other latest algorithms on the benchmark functions,and it shows a better overall performance.Furthermore,for a real reconfigurable production line of smart home appliances,three algorithms,namely non-dominated sorting genetic algorithm-II(NSGA-II),decomposition-based MOPSO(dMOPSO)and MOPSO-BM,are applied to tackle the scheduling problem.It is demonstrated that MOPSO-BM outperforms the others in terms of convergence rate and quality of solutions.
基金This research work is the Key R&D Program of Hubei Province under Grant No.2021AAB001National Natural Science Foundation of China under Grant No.U21B2029。
文摘Job shop scheduling(JS)is an important technology for modern manufacturing.Flexible job shop scheduling(FJS)is critical in JS,and it has been widely employed in many industries,including aerospace and energy.FJS enables any machine from a certain set to handle an operation,and this is an NP-hard problem.Furthermore,due to the requirements in real-world cases,multi-objective FJS is increasingly widespread,thus increasing the challenge of solving the FJS problems.As a result,it is necessary to develop a novel method to address this challenge.To achieve this goal,a novel collaborative evolutionary algorithmwith two-population based on Pareto optimality is proposed for FJS,which improves the solutions of FJS by interacting in each generation.In addition,several experimental results have demonstrated that the proposed method is promising and effective for multi-objective FJS,which has discovered some new Pareto solutions in the well-known benchmark problems,and some solutions can dominate the solutions of some other methods.
基金Supported by Anhui Provincial Natural Science Foundation (1308085MF102)Fundamental Research Funds for the Central Universities(2012HGBZ0195)
文摘In the view of staff shortages and the huge inventory of products in the current market, we put forward a personnel scheduling model in the target of closing to the delivery date considering the parallelism. Then we designed a scheduling algorithm based on genetic algorithm and proposed a flexible parallel decoding method which take full use of the personal capacity. Case study results indicate that the flexible personnel scheduling considering the order-shop scheduling, machine automatic capabilities and personnel flexible in the target of closing to the delivery date optimize the allocation of human resources, then maximize the efficiency.
基金This research was partly supported by the National Kaohsiung University of Science and Technology,and MOST 109-2622-E-992-026 from the Ministry of Sciences and Technology in Taiwan。
文摘Production scheduling involves all activities of building production schedules,including coordinating and assigning activities to each person,group of people,or machine and arranging work orders in each workplace.Production scheduling must solve all problems such as minimizing customer wait time,storage costs,and production time;and effectively using the enterprise’s human resources.This paper studies the application of flexible job shop modelling on scheduling a woven labelling process.The labelling process includes several steps which are handled in different work-stations.Each workstation is also comprised of several identical parallel machines.In this study,job splitting is allowed so that the power of work stations can be utilized better.The final objective is to minimize the total completion time of all jobs.The results show a significant improvement since the new planning may save more than 60%of lead time compared to the current schedule.The contribution of this research is to propose a flexible job shop model for scheduling a woven labelling process.The proposed approach can also be applied to support complex production scheduling processes under fuzzy environments in different industries.A practical case study demonstrates the effectiveness of the proposed model.
文摘This study developed a user equilibrium traffic assignment model based on trip-chains with flexible activity scheduling order and derived the corresponding optimality conditions. We based on the gradient projection method to develop a solution algorithm, the accuracy of which was verified using the test network of UTown. This model could be used to estimate the transportation demands with and without activities scheduling restriction between OD （origin-destination） pairs based on trip-chains, as well as based on trips. Thus, the proposed model is more generalization than conventional trip based or trip-chain based traffic assignment models.
基金Supported by National Key Technology R&D Program(No.2013BAJ06B)
文摘In flexible job-shop batch scheduling problem, the optimal lot-size of different process is not always the same because of different processing time and set-up time. Even for the same process of the same workpiece, the choice of machine also affects the optimal lot-size. In addition, different choices of lot-size between the constrained processes will impact the manufacture efficiency. Considering that each process has its own appropriate lot-size, we put forward the concept of scheduling with lot-splitting based on process and set up the scheduling model of lot-splitting to critical path process as the core. The model could update the set of batch process and machine selection strategy dynamically to determine processing route and arrange proper lot-size for different processes, to achieve the purpose of optimizing the makespan and reducing the processing batches effectively. The experiment results show that, comparing with lot-splitting scheduling scheme based on workpiece, this model optimizes the makespan and improves the utilization efficiency of the machine. It also greatly decreases the machined batches （42%） and reduces the complexity of shop scheduling production management.
文摘There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling,but most scholars neglect the connection between them,which means the purpose of both models is to simulate a more realistic factory environment.From this perspective,the solutions can be more precise and practical if both issues are considered simultaneously.Therefore,the deterioration effect is treated as a part of the fuzzy job shop scheduling problem in this paper,which means the linear increase of a certain processing time is transformed into an internal linear shift of a triangle fuzzy processing time.Apart from that,many other contributions can be stated as follows.A new algorithm called reinforcement learning based biased bi-population evolutionary algorithm(RB2EA)is proposed,which utilizes Q-learning algorithm to adjust the size of the two populations and the interaction frequency according to the quality of population.A local enhancement method which combimes multiple local search stratgies is presented.An interaction mechanism is designed to promote the convergence of the bi-population.Extensive experiments are designed to evaluate the efficacy of RB2EA,and the conclusion can be drew that RB2EA is able to solve energy-efficient fuzzy flexible job shop scheduling problem with deteriorating jobs(EFFJSPD)efficiently.
基金supported by the National Natural Science Foundation of China(Grant Nos.51825502 and U21B2029)。
文摘The distributed flexible job shop scheduling problem(DFJSP),which is an extension of the flexible job shop scheduling problem,is a famous NP-complete combinatorial optimization problem.This problem is widespread in the manufacturing industries and comprises the following three subproblems:the assignment of jobs to factories,the scheduling of operations to machines,and the sequence of operations on machines.However,studies on DFJSP are seldom because of its difficulty.This paper proposes an effective improved gray wolf optimizer(IGWO)to solve the aforementioned problem.In this algorithm,new encoding and decoding schemes are designed to represent the three subproblems and transform the encoding into a feasible schedule,respectively.Four crossover operators are developed to expand the search space.A local search strategy with the concept of a critical factory is also proposed to improve the exploitability of IGWO.Effective schedules can be obtained by changing factory assignments and operation sequences in the critical factory.The proposed IGWO algorithm is evaluated on 69 famous benchmark instances and compared with six state-of-the-art algorithms to demonstrate its efficacy considering solution quality and computational efficiency.Experimental results show that the proposed algorithm has achieved good improvement.Particularly,the proposed IGWO updates the new upper bounds of 13 difficult benchmark instances.
基金supported by National Social Science Foundation of China under the project of 18BGL003.
文摘Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem(MOFJSP)considering transportation time.Design/methodology/approach–A hybrid genetic algorithm(GA)approach is integrated with simulated annealing to solve the MOFJSP considering transportation time,and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.Findings–The performance of the proposed algorithm is tested on different MOFJSP taken from literature.Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution,especially when the number of jobs and the flexibility of the machine increase.Originality/value–Most of existing studies have not considered the transportation time during scheduling of jobs.The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs.Meanwhile,GA is one of primary algorithms extensively used to address MOFJSP in literature.However,to solve the MOFJSP,the original GA has a possibility to get a premature convergence and it has a slow convergence speed.To overcome these problems,a new hybrid GA is developed in this paper.
文摘With the popularization of multi-variety and small-batch production patterns,the flexible job shop scheduling problem(FJSSP)has been widely studied.The sharing of processing resources by multiple machines frequently occurs due to space constraints in a flexible shop,which results in resource preemption for processing workpieces.Resource preemption complicates the constraints of scheduling problems that are otherwise difficult to solve.In this paper,the flexible job shop scheduling problem under the process resource preemption scenario is modeled,and a two-layer rule scheduling algorithm based on deep reinforcement learning is proposed to achieve the goal of minimum scheduling time.The simulation experiments compare our scheduling algorithm with two traditional metaheuristic optimization algorithms among different processing resource distribution scenarios in static scheduling environment.The results suggest that the two-layer rule scheduling algorithm based on deep reinforcement learning is more effective than the meta-heuristic algorithm in the application of processing resource preemption scenarios.Ablation experiments,generalization,and dynamic experiments are performed to demonstrate the excellent performance of our method for FJSSP under resource preemption.
基金supported in part by the National Natural Science Foundation of China(No.61174032)the Public Scientific Research Project of State Administration of Grain(No.201313012)+1 种基金the National Natural Science Foundation of China(Project No:61572238)the National High-tech Research and Development Projects of China(Project No:2014AA041505).
文摘The simulation on benchmarks is a very simple and efficient method to evaluate the performance of the algorithm for solving flexible job shop scheduling model.Due to the assignment and scheduling decisions,flexible job shop scheduling problem(FJSP)becomes extremely hard to solve for production management.A discrete multi-objective particle swarm optimization(PSO)and simulated annealing(SA)algorithm with variable neighborhood search is developed for FJSP with three criteria:the makespan,the total workload and the critical machine workload.Firstly,a discrete PSO is designed and then SA algorithm performs variable neighborhood search integrating two neighborhoods on public critical block to enhance the search ability.Finally,the selection strategy of the personal-best individual and global-best individual from the external archive is developed in multi-objective optimization.Through the experimental simulation on matlab,the tests on Kacem instances,Brdata instances and BCdata instances show that the modified discrete multi-objective PSO algorithm is a promising and valid method for optimizing FJSP with three criteria.
