The Jaya Algorithm And Its Application Research For Energy-saving Shop Scheduling Problem

With the development of economic globalization,distributed manufacturing is becoming a typical scenario in intelligent manufacturing,and the energy-efficient distributed flow-shop scheduling problems(EEDFSP)have attracted great attention from the viewpoint of sustainable development and green manufacturing.In various research literatures,the research on the energy-efficient distributed no-idle flow-shop scheduling problem(EEDNIFSP)is based on the premise of identical factory.However,the features of factories are not completely consistent and the factories belong to heterogeneous factory system.In addition,optimization of factory load balancing in heterogeneous factory system has a crucial impact on rational resource allocation and energy consumption reduction.The study of energy-efficient distributed no-idle flowshop scheduling problem in a heterogeneous factory system(HFS-EEDPFSP)and optimize the factory load balancing among heterogeneous factory system are significance in the field of manufacturing and research.Jaya algorithm,the evolutionary algorithm,is utilized to address HFS-EEDPFSP because of the complexity of HFS-EEDPFSP.First,a surrogate-guided Jaya Algorithm(S-Jaya)and surrogate-assisted Jaya algorithm based on optimal directional guidance and historical learning mechanism are proposed in this paper.In S-Jaya and SDHJaya,the surrogate-guided mechanism and the surrogate-assisted mechanism are proposed respectively to reduce the evaluation times and smooth the local optimal solution.In the SDH-Jaya algorithm,a new search area is provided based on the evolutionary experience of historical populations.The performance of S-Jaya and SDHJaya is tested on the CEC2017 benchmark problem and S-Jaya.The effectiveness of SJaya and SDH-Jaya algorithm outperforms classical Jaya algorithm,the variants of Jaya algorithm,and the state-of-arts algorithms.Second,the Jaya algorithm based on optimal directional guidance and historical learning mechanism(DH-Jaya)and the SDH-Jaya algorithm are utilized to address the gear engineering design problems and no-idle flow-shop scheduling problems to verify the effectiveness of DH-Jaya algorithm and SDH-Jaya algorithm in addressing continuous optimization problems and discrete optimization problems.The effectiveness of DH-Jaya and SDH-Jaya algorithm outperforms other state-of-arts algorithms.Third,a self-learning discrete Jaya algorithm(SD-Jaya)is proposed to address the HFS-EEDPFSP with the optimization goal of minimizing the total tardiness(TTD),total energy consumption(TEC),and factory load balancing(FLB)based on the features of HFS-EEDPFSP and Jaya Algorithm.Firstly,the integer programming model of HFS-EEDPFSP which proposes a calculation criterion of the factory load balancing combining the energy consumption and the completion time is presented.Secondly,a self-learning operation selection strategy,that the success rate of each operation is summarized as knowledge,is designed for guiding the selection of operation.Thirdly,the energy-saving strategy,that the energy-efficient no-idle flow-shop scheduling problem is transformed to be an energy-efficient permutation flow-shop scheduling problem,is introduced to reduce the energy-saving.The SD-Jaya is tested on 60 benchmark instances.On the quality and execution time,the SD-Jaya algorithm outperforms other algorithms for addressing HFS-EEDPFSP.