A Hybrid Multi-Objective Evolutionary Algorithm With Heuristic Adjustment Strategies and Variable Neighbor-Hood Search for Flexible Job-Shop Scheduling Problem Considering Flexible Rest Time

With the development of society, people have a higher demand for the work environment. It has aroused extensive attention of enterprises. One of the most important demands for workers is to have enough rest time to regain strength and energy during the working day. In this paper, a comprehensive mathematical model is established for the multi-objective flexible job-shop scheduling problem with flexible rest time (MOFJSP-FRT). Then a hybrid multi-objective evolutionary algorithm (MOEA) with heuristic adjustment strategies and variable neighborhood search (VNS), named HMOEAV, is proposed to solve the MOFJSP-FRT with the objectives to minimize the makespan and the machines loads balancing simultaneously. In the proposed hybrid algorithm, the machine-based encoding scheme is designed to improve search effectiveness by reducing computational complexity. Two heuristic adjustment strategies considering both the problem characteristics and the objective features are employed to initialize a high-quality population. To adequately emphasize the local exploitation ability of MOEA, VNS is incorporated into it. The non-dominated solutions got by MOEA are the initial solutions for VNS, in which three types of neighborhood structures according to problem structures are designed. The practical case in a steel structure enterprise is carried out to demonstrate the effectiveness of the proposed model and hybrid algorithm. The influences of rest time length on the MOFJSP-FRT are analyzed to give enterprises new insights to improve the scheduling efficiency while ensuring that employees have enough time to rest.