Title: A hybrid fruit fly optimisation algorithm to solve the flow shop scheduling problems with multi-objectives
Authors: M.K. Marichelvam; A. Azhagurajan; M. Geetha
Addresses: Department of Mechanical Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, Tamilnadu, India ' Department of Mechanical Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, Tamilnadu, India ' Department of Mathematics, Kamaraj College of Engineering and Technology, Virudhunagar, Tamilnadu, 626 001, India
Abstract: Scheduling is one of the most important decision making processes in production and operations management. Different scheduling environments are addressed by the researchers in the literature. In this paper, flow shop scheduling problems are studied. Many researchers tackled with single objective flow shop scheduling problems with makespan objective only. However, scheduling problems consist of many conflict objectives. Hence, in this paper an attempt is made to solve the multi-objective flow shop scheduling problems. The objective is to minimise the weighted sum of makespan, mean flow time, earliness and tardiness. The flow shop scheduling problems have been proved to be NP-hard. Hence, the problems cannot be solved by the exact algorithms. In this paper, a relatively new meta-heuristic algorithm called as hybrid fruit fly optimisation algorithm is proposed to solve the multi-objective flow shop scheduling problems. Two constructive heuristics and a dispatching rule are incorporated with the fruit fly optimisation algorithm and hence the solution quality is improved. The proposed algorithm is tested on random problem instances and the results are compared with many other meta-heuristic algorithms. The results show that the proposed algorithm is more effective and better than other meta-heuristic algorithms.
Keywords: flowshop scheduling; NP-hard; makespan; earliness; tardiness; mean flow time; metaheuristics; fruit fly optimisation; multi-objective optimisation; metaheuristics.
International Journal of Advanced Intelligence Paradigms, 2017 Vol.9 No.2/3, pp.164 - 185
Received: 14 Jan 2016
Accepted: 28 May 2016
Published online: 17 Mar 2017 *