Flexible job-shop scheduling and heterogeneous repairman assignment with maintenance time window and employee timetable constraints. (30th December 2021) - An, Youjun; Chen, Xiaohui; Li, Yinghe; Zhang, Ji; Jiang, Junwei
Abstract: This work focuses on a new flexible scheduling problem in the job-shop that considers both maintenance activity and repairman competence under maintenance time window and employee timetable constraints. To deal with this problem, a hybrid multi-objective biogeography-based optimization (HMOBBO) algorithm is proposed, which has the following features: (1) a flexible decoding mechanism that considers maintenance time window and heterogeneous repairman constraints is designed; (2) three calculation methods of habitat suitability index (HSI) are defined; (3) tabu search (TS) algorithm is incorporated into the presented algorithm; and (4) a new offspring population generation mechanism is constructed. In numerical simulation, the parameter setting is firstly analyzed to ensure its robustness for different datasets via comparing the performance of each critical parameter combination. Secondly, different HSIs and migration models are separately compared through multiply running the literature instances, it is shown that fitness function 2 (the reciprocal of the sum of the normalized objective function values) and migration model 1 (constant immigration and linear emigration model) are the most suitable and steady in our experiments. Thirdly, the superiority of HMOBBO is proved by confronting with other six intelligent algorithms. Finally, through contrast variant models, the significance of considering employee timetable and repairman assignment is verified, and the benefits of the integrated optimization model/method are also demonstrated by comparing with hierarchical optimization model/method. Highlights: A new scheduling problem on flexible job-shop is defined under multiple constraints. A hybrid multi-objective biogeography-based optimization algorithm is proposed. The performance of the proposed algorithm is tested on existing literature instances. The benefits of the integrated optimization model/method are demonstrated.