Improved Dynamic Q-Learning Algorithm to Solve the Lot-Streaming Flowshop Scheduling Problem with Equal-Size Sublots  

作　　者：Ping Wang Renato De Leone Hongyan Sang 

机构地区：[1]School of Computer Sciences and Mathematics,University of Camerino,Camerino 62032,Italy [2]School of Computer Science,Liaocheng University,Liaocheng 252000,China

出　　处：《Complex System Modeling and Simulation》2024年第3期223-235,共13页复杂系统建模与仿真（英文）

基　　金：supported by the National Natural Science Foundation of China (Nos.62473186,62273221,and 52205529);Discipline with Strong Characteristics of Liaocheng University-Intelligent Science and Technology (No.319462208);Natural Science Foundation of Shandong Province (Nos.ZR2021QF036 and ZR2021QE195).

摘　　要：The lot-streaming flowshop scheduling problem with equal-size sublots(ELFSP)is a significant extension of the classic flowshop scheduling problem,focusing on optimize makespan.In response,an improved dynamic Q-learning(IDQL)algorithm is proposed,tillizing makespan as feedback.To prevent blind search,a dynamic 8-greedy search strategy is introduced.Additionally,the Nawaz-Enscore-Ham(NEH)algorithm is employed to diversify solution sets,enhancing local optimality.Addressing the limitations of the dynamic 8-greedy strategy,the Glover operator complements local search efforts.Simulation experiments,comparing the IDQL algorithm with other itelligent algorithms,valldate its effectiveness.The performance of the IDQL algorithm surpasses that of its counterparts,as evidenced by the experimental analysis.Overall,the proposed approach offers a promising solution to the complex ELFSP,showcasing its capability to efficiently minimize makespan and optimize scheduling processes in flowshop environments with equal-size sublots.

关 键 词：lot-streaming flowshop sechduling Q-earning dynamic search reward function 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]