The Discrete Particle Swarm Optimization Algorithms For Permutation Flowshop Scheduling Problem

Abstract

In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study is a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both proposed algorithm can solve the problem well in the short computational time. The result of Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides the population size and maximum iteration have any impact on the quality of solutions produced by HDPSO and MPSO algorithms

A Crossbreed Discrete Artificial Bee Colony for Permutation Flow Shop Scheduling Problem to Minimize Total Earliness and Tardiness

“…[13] proposed a genetic algorithm. To solve PFSP, [14] presented two discrete particle swarm optimization (DPSO) algorithms. [8] developed a mix of Variable Neighbourhood Search (VNS) and Tabu Search (TS) which are stochastic search strategies capable of resolving the Multi-Objective ET Scheduling Problem (MOETSP).…”