An Improved Shuffled Frog-Leaping Algorithm for Job-Shop Scheduling Problem

Abstract

Job-shop Scheduling Problem (JSP) is a well-known combinatorial optimization problem. In past decades, many meta-heuristic algorithms have been used to solve it. Shuffled Frog-Leaping Algorithm (SFLA) is a novel nature-inspired meta-heuristic algorithm. However, to the best of our knowledge, so far there have been few papers studying on the solutions to JSP using SFLA. In this study, we develop an improved SFLA for JSP. In order to extend SFLA to deal with JSP efficiently, the random keys encoding scheme is introduced to the proposed SFLA. The results of experiments carried out with 14 well-known benchmark JSP instances have shown that the improved SFLA possesses outstanding performance in terms of convergence and stability, as compared to some existing meta-heuristic algorithms, such as GA and IPSO. Thus, the presented SFLA in this work is very effective and superior to solve JSP.