A Variable Neighborhood Based Memetic Algorithm for Scheduling Single Batch Processing Machine with Non-Identical Job Sizes

Abstract

A variable neighborhood based memetic algorithm (VNMA) is proposed to minimize makespan for a single batch processing machine in this paper. Random instances were generated to verify the effectiveness of VNMA. Comparisons are made through using a genetic algorithm (GA) addressed in the literature as a comparator method. Computational results demonstrate that VNMA outperformed the GA with respect to solutions quality and run times.

A Two-Population Cooperative Multiobjective Differential Evolution Algorithm for Batching Scheduling Problem

“…Since then, many scholars have studied BPMSP. To minimize the makespan of a single machine BPMSP, Zarook et al [12] and Huang et al [19] proposed a heuristic algorithm, respectively, with the consideration of machine maintenance; Kashan et al [20] proposed an effective hybrid genetic algorithm (HGA) with consideration of nonidentical job sizes; Guo [21] proposed a variable neighborhood based memetic algorithm (VNMA). To minimize the maximum lateness of the single machine BPMSP, Zhou et al [13] presented a modified particle swarm optimization (MPSO) algorithm with the consideration of nonidentical job sizes and release dates.…”

“…e last n integers in vector correspond to the first n integers one by one and represent the processing machine of each job. Compared with the most job permutation code [21,23], this method provides a more comprehensive search space and can support a variety of coding strategies proposed here. At the same time, the expression of the solution improves the probability of finding Pareto frontier solution.…”