A hybrid genetic-gravitational search algorithm for a multi-objective flow shop scheduling problem

Abstract

Many real-world problems in manufacturing system, for instance, the scheduling problems, are formulated by defining several objectives for problem solving and decision making. Recently, research on dispatching rules allocation has attracted substantial attention. Although many dispatching rules methods have been developed, multi-objective scheduling problems remain inherently difficult to solve by any single rule. In this paper, a hybrid genetic-based gravitational search algorithm (GSA) in weighted dispatching rule is proposed to tackle a scheduling problem by achieving both time and job-related objectives. Genetic algorithm (GA) is used to select two appropriate dispatching rules to combine as a weighted multi-attribute function, while the GSA is used to optimize the contribution weightage of each rule in each stage of the flow shop. The results show that the proposed algorithm is significantly better than the traditional dispatching rules and the rules allocation algorithm. The proposed algorithm not only improved the quality of the schedule in multi-objective problems but also maintained the advantages of traditional dispatching rules in terms of ease of implementation.

Energy Dispatching Based on an Improved PSO-ACO Algorithm

“…However, there are few research studies on energy dispatching algorithms between diferent sites. Te mainstream algorithms in current dispatching research are mainly intelligent optimization algorithms, including genetic algorithm(GA) [11], PSO algorithm [12], ACO algorithm [13], grey wolf optimizer(GWO) algorithm [14], deep learning(DL) [15], diferential evolution(DE) algorithm [16], fruit fy optimization Algorithm(FOA) algorithm [17], and so on.…”