An Efficient Meta-Heuristic for Multi-Objective Flexible Job Shop Inverse Scheduling Problem

作     者：Wu, Rui Li, Yibing Guo, Shunsheng Li, Xixing 

作者机构：Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Hubei Peoples R China Hubei Univ Technol Sch Mech Engn Wuhan 430068 Hubei Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2018年第6卷

页      面：59515-59527页

核心收录：

基　　金：National Natural Science Foundation of China Science and Technology Support Program of Hubei Province in China [2015BAA063] Fundamental Research Funds for the Central Universities [2018III31GX] 

主　　题：Flexible job shop inverse scheduling problem inverse optimization multi-objective optimization multi-objective evolutionary algorithm based on decomposition particle swarm optimization 

摘      要：In reality, uncertainties may still encounter after a scheduling scheme is generated. These may make the original schedule non-optimal or even impossible. Traditional scheduling methods are not effective in dealing with these situations. In response to this phenomenon, by introducing the idea of inverse optimization into the scheduling field, a new scheduling strategy called inverse scheduling has been proposed. To the best of our knowledge, this is the first study to be conducted on flexible job shop inverse scheduling problem (FJISP). In this paper, first, a comprehensive mathematical model with adjustable processing time is established. Then, a hybrid multi-objective evolutionary algorithm based on decomposition and particle swarm optimization is adopted for solving FJISP. To make the proposed algorithm solving FJISP more efficiently, some new strategies are used. A 3-D coding scheme is employed to represent the particles, multiple strategies are designed for generating a high-quality initial population, and effective discrete crossover and mutation operators are specially designed according to the FJISP s characteristics. Finally, computational experiments are carried out using extended benchmarks, and the results demonstrate the effectiveness of the proposed algorithm for solving the FJISP.