Intelligent Optimization Under Multiple Factories: Hybrid Flow Shop Scheduling Problem with Blocking Constraints Using an Advanced Iterated Greedy Algorithm  

作　　者：Yong Wang Yuting Wang Yuyan Han Junqing Li Kaizhou Gao Yusuke Nojima 

机构地区：[1]School of Computer Science,Liaocheng University,Liaocheng 252000,China [2]School of Computer Science,Shandong Normal University,Jinan 252000,China [3]Macao Institute of Systems Engineering,Macao University of Science and Technology,Macao 999078,China [4]Department of Computer Science and Intelligent Systems,Osaka Prefecture University,Osaka 599-8531,Japan

出　　处：《Complex System Modeling and Simulation》2023年第4期282-306,共25页复杂系统建模与仿真（英文）

基　　金：This work was jointly supported by the National Natural Science Foundation of Shandong Province(No.ZR2023MF022);National Natural Science Foundation of China(Nos.61973203,62173216,and 62173356);Guangyue Youth Scholar Innovation Talent Program Support from Liaocheng University(No.LCUGYTD2022-03).

摘　　要：The distributed hybrid flow shop scheduling problem(DHFSP),which integrates distributed manufacturing models with parallel machines,has gained significant attention.However,in actual scheduling,some adjacent machines do not have buffers between them,resulting in blocking.This paper focuses on addressing the DHFSP with blocking constraints(DBHFSP)based on the actual production conditions.To solve DBHFSP,we construct a mixed integer linear programming(MILP)model for DBHFSP and validate its correctness using the Gurobi solver.Then,an advanced iterated greedy(AIG)algorithm is designed to minimize the makespan,in which we modify the Nawaz,Enscore,and Ham(NEH)heuristic to solve blocking constraints.To balance the global and local search capabilities of AIG,two effective inter-factory neighborhood search strategies and a swap-based local search strategy are designed.Additionally,each factory is mutually independent,and the movement within one factory does not affect the others.In view of this,we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the objective.Finally,two shaking strategies are incorporated into the algorithm to mitigate premature convergence.Five advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances,and experimental results illustrate that the makespan and the relative percentage increase(RPI)obtained by AIG are 1.0%and 86.1%,respectively,better than the comparative algorithms.

关 键 词：BLOCKING distributed hybrid flow shop neighborhood search iterated greedy algorithm 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]