A non-dominated ensemble fitness ranking algorithm for multi-objective flexible job-shop scheduling problem considering worker flexibility and green factors

The worker flexibility and green production related factors are two key aspects that widely exit in real-life production and seriously affect the production efficiency and ecological environment, while both of them are usually neglected in the existing scheduling studies. In this paper, a multi-objective flexible job-shop scheduling problem considering worker flexibility and green factors (MO-FJSPG) is explored with the criteria of minimizing makespan, labor cost and green production related factors. A non-linear integer programming model is constructed for MO-FJSPG and a new non-dominated ensemble fitness ranking algorithm (NEFRL) is proposed to solve this problem. In the NEFRL, a specific three-layer representation method is proposed; a non-dominated ensemble fitness ranking method is developed to rank and select solutions for the next generation; a local search method based on critical path is designed to strengthen the exploitation ability of the algorithm and used to optimize the non-dominated solutions further in each iteration. Finally, 31 instances are constructed and the effectiveness of NEFRL is verified by comparing with other multi-objective algorithms. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study highlights the importance of considering worker flexibility and green production related factors in a multi-objective flexible job-shop scheduling problem. A new non-dominated ensemble fitness ranking algorithm (NEFRL) is proposed to address this issue, and its effectiveness is demonstrated through comparisons with other multi-objective algorithms in 31 instances.