A Fitness-based Selection Method for Pareto Local Search for Many-Objective Job Shop Scheduling

Abstract

Genetic programming (GP) is considered the most popular method for automatically discovering and constructing dispatching rules for scheduling problems. Pareto Local Search (PLS) is a simple and effective local search method for tackling multi-objective combinatorial optimization problems. Researchers have studied the application of PLS to multiobjective evolutionary algorithms (MOEAs) with some success. In fact, by hybridizing global search with local search, the performance of many MOEAs can be noticeably improved. Despite its preliminary success, the practical use of PLS in GP is relatively limited. In this study, our aim is to enhance the quality of evolved dispatching rules for many-objective Job Shop Scheduling (JSS) through hybridizing GP with PLS techniques and designing an effective selection mechanism of initial solutions for PLS. In this paper, we propose a new GP-PLS algorithm that investigates whether the fitness-based selection mechanism for selecting initial solutions for PLS can increase the chance of discovering highly effective dispatching rules for many-objective JSS. To evaluate the effectiveness of our new algorithm, GPPLS is compared with the current state-of-the-art algorithms for many-objective JSS. The experimental results confirm that the proposed method can outperform the four recently proposed algorithms because of the proper use of local search techniques.