A Search Economics Based Algorithm for Job Shop Scheduling Problem

• Main Authors: Shao-Juan Wang, 王韶娟
• Other Authors: Ming-Chao Chiang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/y75m4r

碩士 === 國立中山大學 === 資訊工程學系研究所 === 107 === The job-shop scheduling problem (JSSP) is certainly one of the most important scheduling problems because many famous scheduling problems are either a derived case or a special case of the JSSP. Since JSSP is NP-hard, many metaheuristic algorithms, such as genetic algorithm and particle swarm optimization, are proposed to solve the JSSP. These algorithms have two major drawbacks. First, they are very sensitive to the initial solutions. Second, they are likely to fall into a local optimum at later iterations. As such, this thesis presents a novel algorithm, called search economics for job-shop scheduling problem (SEJSP), to deal with these issues. The results show that SEJSP is an efficient algorithm for the JSSP in the sense that it gives a better result compared to all the other algorithms compared in this thesis.