A Pareto based discrete Jaya algorithm for multi-objective flexible job shop scheduling problem

Research on the development of Pareto-based multi-objective algorithms to address scheduling problems has attracted a lot of attention in recent years. In this work, a multi-objective discrete Jaya algorithm (MODJA) is proposed to address the flexible job shop scheduling problem (FJSSP) considering the minimization of makespan, total workload of machines, and workload of critical machine as performance measures. A discrete Jaya algorithm is proposed to handle the problem under consideration. A problem specific neighborhood-based local search technique is integrated into the proposed approach to enhance its exploitation capability. Further, a dynamic mutation operator and a modified crowding distance measure are proposed to enhance the diversity in the search process. Extensive computational experiments are carried out considering 203 instances of FJSSP from literature. The Taguchi method of design is employed to identify the best set of key parameters based on three instances. In the experimentation phase, initially, the contribution of the proposed local search technique and crowding distance measure is investigated. Then, a comparison of MODJA with the weighted sum version of the approach and other multi-objective evolutionary algorithms is performed. Computational results demonstrate the effectiveness of the proposed MODJA in obtaining diverse and improved Pareto-optimal solutions.

Automated summary

The study introduces a multi-objective discrete Jaya algorithm (MODJA) to address flexible job shop scheduling problems, incorporating a problem-specific local search technique and modified crowding distance measure for enhanced performance. Computational experiments demonstrate the effectiveness of MODJA in obtaining diverse and improved Pareto-optimal solutions.