Constraint programing for solving four complex flexible shop scheduling problems

作     者：Meng, Leilei Lu, Chao Zhang, Biao Ren, Yaping Lv, Chang Sang, Hongyan Li, Junqing Zhang, Chaoyong 

作者机构：Liaocheng Univ Sch Comp Sci Liaocheng Shandong Peoples R China China Univ Geosci Sch Comp Sci Wuhan Peoples R China Jinn Univ Sch Intelligent Syst Sci & Engn Zhuhai Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China 

出 版 物：《IET COLLABORATIVE INTELLIGENT MANUFACTURING》 (IET Collab. Intell. Manuf.)

年 卷 期：2021年第3卷第2期

页      面：147-160页

核心收录：

基　　金：Basic and Applied Basic Research Foundation of Guangdong Province of China [2019A1515110399] Research Fund Project of Liaocheng University National Natural Science Foundation of China Project of International Cooperation and Exchanges NSFC 

主　　题：constraint handling CP method flexible job shop scheduling problem job shop scheduling constraint programing hybrid flow shop scheduling problem minimisation sequence-dependent setup time complex constraints flow shop scheduling Gurobi semiconductor final testing problem complex flexible shop scheduling problems Cplex 

摘      要：In recent years, with the advent of robust solvers such as Cplex and Gurobi, constraint programing (CP) has been widely applied to a variety of scheduling problems. This paper presents CP models for formulating four scheduling problems with minimal makespan and complex constraints: the no-wait hybrid flow shop scheduling problem, the hybrid flow shop scheduling problem with sequence-dependent setup times, the flexible job shop scheduling problem with worker flexibility and the semiconductor final testing problem. The advantages of CP method in solving these four complex scheduling problems are explored. Finally, a set of benchmark instances are adopted to demonstrate the effectiveness and efficiency of the CP method. Experiment results show that the proposed CP models outperform existing algorithms;in particular, several best-known solutions of benchmark instances are improved by our CP method.