Solving the Job Shop Scheduling Problem by the Multi-Hybridization of Swarm Intelligence Techniques

Abstract

The industry is subject to strong competition, and customer requirements which are increasingly strong in terms of quality, cost, and deadlines. Consequently, the companies must improve their competitiveness. Scheduling is an essential tool for improving business performance. The production scheduling problem is usually an NP-hard problem, its resolution requires optimization methods dedicated to its degree of difficulty. This paper aims to develop multi-hybridization of swarm intelligence techniques to solve job shop scheduling problems. The performance of recommended techniques is evaluated by applying them to all well-known benchmark instances and comparing their results with the results of other techniques obtainable in the literature. The experiment results are concordant with other studies that have shown that the multi hybridization of swarm intelligence techniques improve the effectiveness of the method and they show how these recommended techniques affect the resolution of the job shop scheduling problem.

Dynamic Job Shop Scheduling Problem With New Job Arrivals Using Hybrid Genetic Algorithm

“…More recently, variationalQuantum algorithms (VQA) running on superconducting quantum processors have been applied to the JSP [29]. Hybridized metaheuristics have also been used, for instance, in Araki and Yoshitomi [29], who described a hybrid approach combining PSO and a Monte Carlo method, Hakim et al [30], who proposed to hybridize ABC with ACO, or Fontes et al [31], who designed a PSO-SA hybridization to address a variant of the JSP in which jobs are transported by a limited number of vehicles. In another scheme of hybridization, Matrenin [32] presented a parallel method where a GA played the role of a meta-optimizer to adjust the hyper-parameters of an ACO.…”