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

Abstract

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.

Mitigation of Make Span Time in Job Shop Scheduling Problem Using Gannet Optimization Algorithm

“…It starts with the collection of random results represented in Eq. (7), at that time the best solution is considered to be the optimal best solution.…”

“…A population of randomly generated particles with each having a velocity and position serve as the foundation for this population-based search procedure [11]. The enormously complicated nature of production scheduling problems is also addressed by fuzzy logic, genetic algorithms [6], simulated annealing [8] and particle swarm optimization with Cauchy distribution [7].…”

Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems

“…Additionally, the algorithm was enhanced by a local search method. Evaluation of the performance of several PSO based algorithms for solving the JSSP can be found in [35].…”