Discrete particle swarm optimization to solve multi-objective limited-wait hybrid flow shop scheduling problem

International Conference on Industrial and Systems Engineering 2017

Santosa, B.^1 ; Siswanto, N.^1 ; Fiqihesa^1

Industrial Engineering Department, Institut Teknologi Sepuluh Nopember, Kampus ITS, Sukolilo-Surabaya

60111, Indonesia^1

关键词: Continuous optimization problems;    Discrete optimization problems;    Discrete particle swarm optimization;    Flow-shop scheduling;    Hybrid flow shop scheduling;    Meta-heuristics algorithms;    Objective functions;    Particle swam optimizations (PSO);   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/337/1/012006/pdf
DOI  :  10.1088/1757-899X/337/1/012006

来源: IOP

【 摘 要 】

This paper proposes a discrete Particle Swam Optimization (PSO) to solve limited-wait hybrid flowshop scheduing problem with multi objectives. Flow shop schedulimg represents the condition when several machines are arranged in series and each job must be processed at each machine with same sequence. The objective functions are minimizing completion time (makespan), total tardiness time, and total machine idle time. Flow shop scheduling model always grows to cope with the real production system accurately. Since flow shop scheduling is a NP-Hard problem then the most suitable method to solve is metaheuristics. One of metaheuristics algorithm is Particle Swarm Optimization (PSO), an algorithm which is based on the behavior of a swarm. Originally, PSO was intended to solve continuous optimization problems. Since flow shop scheduling is a discrete optimization problem, then, we need to modify PSO to fit the problem. The modification is done by using probability transition matrix mechanism. While to handle multi objectives problem, we use Pareto Optimal (MPSO). The results of MPSO is better than the PSO because the MPSO solution set produced higher probability to find the optimal solution. Besides the MPSO solution set is closer to the optimal solution.