Knowledge-driven two-stage memetic algorithm for energy-efficient flexible job shop scheduling with machine breakdowns

Abstract

This paper focuses on the multi-objective energy-efficient flexible job shop scheduling problem with machine breakdowns. To mitigate the impact of machine breakdowns, a rescheduling strategy is implemented in the scheduling process. In addition to sequencing the operations, the current problem is to determine the appropriate allocation of the machine and the proper speed of the machine to minimize both makespan and total energy consumption simultaneously. A mixed integer linear programming model is established to describe the considered problem. With the aim of effectively solving this problem, a knowledge-driven two-stage memetic algorithm (KTMA) is proposed. In the first stage, a hybrid initialization strategy that combines three problem-specific heuristics is applied to generate a high-quality initial population. Then, a knowledge-driven variable neighborhood search approach is designed for quickly converging and fully exploiting the solution space of the KTMA. In the second stage, two energy-saving strategies are designed to further reduce the total energy consumption. Extensive experiments carried out to compare the KTMA with some well-known algorithms confirm that the proposed KTMA can efficiently solve this problem.