A solution framework based on packet scheduling and dispatching rule for job-based scheduling problems

Abstract

Job-based scheduling problems have inherent similarities and relations. However, the current researches on these scheduling problems are isolated and lack references. We propose a unified solution framework containing two innovative strategies: the packet scheduling strategy and the greedy dispatching rule. It can increase the diversity of solutions and help in solving the problems with large solution space effectively. In addition, we propose an improved particle swarm optimization (PSO) algorithm with a variable neighborhood local search mechanism and a perturbation strategy. We apply the solution framework combined with the improved PSO to the benchmark instances of different job-based scheduling problems. Our method provides a self-adaptive technique for various job-based scheduling problems, which can promote mutual learning between different areas and provide guidance for practical applications.