Performance Evaluation of Continuous and Discrete Particle Swarm Optimization in Job-Shop Scheduling Problems

International Conference on Recent Advances in Industrial Engineering and Manufacturing

工业技术（总论）

Anuar, N.I.^1 ; Fauadi, M.H.F.M.^2 ; Saptari, A.^3

Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Ayer-Keroh, Melaka

75450, Malaysia^1

Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka

76100, Malaysia^2

Department of Industrial Engineering, President University, Jl. Ki Hajar Dewantara, Kota Jababeka, Cikarang Baru, Bekasi

17550, Indonesia^3

关键词: Bench-mark problems;    Combinatorial optimization problems;    Continuous spaces;    Discrete particle swarm optimization;    Discrete problems;    Job shop scheduling problems;    Optimization method;    Scheduling problem;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/530/1/012044/pdf
DOI  :  10.1088/1757-899X/530/1/012044

学科分类：工业工程学

来源: IOP

【 摘 要 】

The Particle Swarm Optimization (PSO) is an optimization method that was modeled based on the social behavior of organisms, such as bird flocks or swarms of bees. It was initially applied for cases defined over continuous spaces, but it can also be modified to solve problems in discrete spaces. Such problems include scheduling problems, where the Job-shop Scheduling Problem (JSP) is among the hardest combinatorial optimization problems. Although the JSP is a discrete problem, the continuous version of PSO has been able to handle the problem through a suitable mapping. Subsequently, its modified model, namely the discrete PSO, has also been proposed to solve it. In this paper, the performance of continuous and discrete PSO in solving JSP are evaluated and compared. The benchmark tests used are FT06 and FT10 problems available in the OR-library, where the goal is to minimize the maximum completion time of all jobs, i.e. the makespan. The experimental results show that the discrete PSO outperforms the continuous PSO for both benchmark problems.