In the manufacturing industry,reasonable scheduling can greatly improve production efficiency,while excessive resource consumption highlights the growing significance of energy conservation in production.This paper st...In the manufacturing industry,reasonable scheduling can greatly improve production efficiency,while excessive resource consumption highlights the growing significance of energy conservation in production.This paper studies the problem of energy-efficient distributed heterogeneous permutation flowshop problem with variable processing speed(DHPFSP-VPS),considering both the minimum makespan and total energy consumption(TEC)as objectives.A discrete multi-objective squirrel search algorithm(DMSSA)is proposed to solve the DHPFSPVPS.DMSSA makes four improvements based on the squirrel search algorithm.Firstly,in terms of the population initialization strategy,four hybrid initialization methods targeting different objectives are proposed to enhance the quality of initial solutions.Secondly,enhancements are made to the population hierarchy system and position updating methods of the squirrel search algorithm,making it more suitable for discrete scheduling problems.Additionally,regarding the search strategy,six local searches are designed based on problem characteristics to enhance search capability.Moreover,a dynamic predator strategy based on Q-learning is devised to effectively balance DMSSA’s capability for global exploration and local exploitation.Finally,two speed control energy-efficient strategies are designed to reduce TEC.Extensive comparative experiments are conducted in this paper to validate the effectiveness of the proposed strategies.The results of comparing DMSSA with other algorithms demonstrate its superior performance and its potential for efficient solving of the DHPFSP-VPS problem.展开更多
The permutation flowshop scheduling problem （PFSP） is one of the most well-known and well-studied production scheduling problems with strong industrial background. This paper presents a new hybrid optimization algor...The permutation flowshop scheduling problem （PFSP） is one of the most well-known and well-studied production scheduling problems with strong industrial background. This paper presents a new hybrid optimization algorithm which combines the strong global search ability of artificial immune system （AIS） with a strong local search ability of extremal optimization （EO） algorithm. The proposed algorithm is applied to a set of benchmark problems with a makespan criterion. Performance of the algorithm is evaluated. Comparison results indicate that this new method is an effective and competitive approach to the PFSP.展开更多
The distributed permutation flowshop scheduling problem(DPFSP)has received increasing attention in recent years,which always assumes that the machine can process without restrictions.However,in practical production,ma...The distributed permutation flowshop scheduling problem(DPFSP)has received increasing attention in recent years,which always assumes that the machine can process without restrictions.However,in practical production,machine preventive maintenance is required to prevent machine breakdowns.Therefore,this paper studies the DPFSP with preventive maintenance(PM/DPFSP)aiming at minimizing the total flowtime.For solving the problem,a discrete gray wolf optimization algorithm with restart mechanism(DGWO_RM)is proposed.In the initialization phase,a heuristic algorithm that takes into consideration preventive maintenance and idle time is employed to elevate the quality of the initial solution.Next,four local search strategies are proposed for further enhancing the exploitation capability.Furthermore,a restart mechanism is integrated into algorithm to avert the risk of converging prematurely to a suboptimal solution,thereby ensuring a broader exploration of potential solutions.Finally,comprehensive experiments studies are carried out to illustrate the effectiveness of the proposed strategy and to verify the performance of DGWO_RM.The obtained results show that the proposed DGWO_RM significantly outperforms the four state-of-the-art algorithms in solving PM/DPFSP.展开更多
基金supported by the Key Research and Development Project of Hubei Province(Nos.2020BAB114 and 2023BAB094).
文摘In the manufacturing industry,reasonable scheduling can greatly improve production efficiency,while excessive resource consumption highlights the growing significance of energy conservation in production.This paper studies the problem of energy-efficient distributed heterogeneous permutation flowshop problem with variable processing speed(DHPFSP-VPS),considering both the minimum makespan and total energy consumption(TEC)as objectives.A discrete multi-objective squirrel search algorithm(DMSSA)is proposed to solve the DHPFSPVPS.DMSSA makes four improvements based on the squirrel search algorithm.Firstly,in terms of the population initialization strategy,four hybrid initialization methods targeting different objectives are proposed to enhance the quality of initial solutions.Secondly,enhancements are made to the population hierarchy system and position updating methods of the squirrel search algorithm,making it more suitable for discrete scheduling problems.Additionally,regarding the search strategy,six local searches are designed based on problem characteristics to enhance search capability.Moreover,a dynamic predator strategy based on Q-learning is devised to effectively balance DMSSA’s capability for global exploration and local exploitation.Finally,two speed control energy-efficient strategies are designed to reduce TEC.Extensive comparative experiments are conducted in this paper to validate the effectiveness of the proposed strategies.The results of comparing DMSSA with other algorithms demonstrate its superior performance and its potential for efficient solving of the DHPFSP-VPS problem.
基金Project supported by the National Natural Science Foundation of China （Grant No.60574063）
文摘The permutation flowshop scheduling problem （PFSP） is one of the most well-known and well-studied production scheduling problems with strong industrial background. This paper presents a new hybrid optimization algorithm which combines the strong global search ability of artificial immune system （AIS） with a strong local search ability of extremal optimization （EO） algorithm. The proposed algorithm is applied to a set of benchmark problems with a makespan criterion. Performance of the algorithm is evaluated. Comparison results indicate that this new method is an effective and competitive approach to the PFSP.
基金supported by the National Natural Science Foundation of China(Nos.62473186 and 62273221)Natural Science Foundation of Shandong Province(No.ZR2024MF017)Discipline with Strong Characteristics of Liaocheng University Intelligent Science and Technology(No.319462208).
文摘The distributed permutation flowshop scheduling problem(DPFSP)has received increasing attention in recent years,which always assumes that the machine can process without restrictions.However,in practical production,machine preventive maintenance is required to prevent machine breakdowns.Therefore,this paper studies the DPFSP with preventive maintenance(PM/DPFSP)aiming at minimizing the total flowtime.For solving the problem,a discrete gray wolf optimization algorithm with restart mechanism(DGWO_RM)is proposed.In the initialization phase,a heuristic algorithm that takes into consideration preventive maintenance and idle time is employed to elevate the quality of the initial solution.Next,four local search strategies are proposed for further enhancing the exploitation capability.Furthermore,a restart mechanism is integrated into algorithm to avert the risk of converging prematurely to a suboptimal solution,thereby ensuring a broader exploration of potential solutions.Finally,comprehensive experiments studies are carried out to illustrate the effectiveness of the proposed strategy and to verify the performance of DGWO_RM.The obtained results show that the proposed DGWO_RM significantly outperforms the four state-of-the-art algorithms in solving PM/DPFSP.
