A Joint Optimization of Maintenance and Scheduling for Unrelated Parallel Machine Problem Based on Hybrid Discrete Spider Monkey Optimization Algorithm

Abstract

In general, the parallel machine scheduling problem that minimizes maximum completion time is NP-hard in a strong sense; a lot of heuristics have been proposed for this kind of problem. In this paper, the unrelated parallel machine scheduling problem with maintainability (UPMSPM) is studied, in which the reliability of machines obeys exponential distribution. A hybrid algorithm HDSMO, which combines the discrete spider monkey algorithm (SMO) with the crossover and mutation operation, is proposed to solve UPMSPM. In view of the lack of local search capability in the later iteration of the traditional SMO algorithm, inertial weights are introduced to update the local leader and the global leader. Computational experiments with randomly generated instances demonstrate that the proposed HDSMO algorithm can obtain significantly better solutions in a shorter time than GA and SMO algorithms.