Reducing noise pollution by flexible job-shop scheduling with worker flexibility: Multi-subpopulation evolutionary algorithm

Abstract

Flexible job-shop scheduling is one of the most critical production management topics. In this paper, it is also assumed job interruption due to the machine breakdown is allowed, and the processing time depends on the speed of the machines and requires both human and machine resources to process the jobs. Although, as the speed of the machine increases, the time of job’ completion reduces, an increase in speed results in an increase in noise pollution in the production environment, and with the aim of applying a cleaner production that is a preventative approach, it has been tried to reduce noise pollution by minimizing the increase in speed. After modeling the problem using the mixed-integer programming and solving it using the ε-constraint method, since the problem is NP-hard, a multi-subpopulation evolutionary algorithm is proposed to solve it. Finally, the proposed algorithm was compared with the NSGAII and the obtained results showed that it performs better than the NSGAII.