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Application of Hybrid Firefly Algorithm-Tabu Search Technique to Minimize the Makespan in Job Shop Scheduling problem

Application of Hybrid Firefly Algorithm-Tabu Search Technique to Minimize the Makespan in Job Shop Scheduling problem
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Author(s): Manoj Govind Kharat (National Institute of Industrial Engineering (NITIE), Mumbai, India), Siddhant Sanjeev Khadke (National Institute of Industrial Engineering (NITIE), Mumbai, India), Rakesh D. Raut (National Institute of Industrial Engineering (NITIE), Mumbai, India), Sachin S. Kamble (National Institute of Industrial Engineering (NITIE), Mumbai, India), Sheetal Jaisingh Kamble (National Institute of Industrial Engineering (NITIE), Mumbai, India) and Mukesh Govind Kharat (National Institute of Industrial Engineering (NITIE), Mumbai, India)
Copyright: 2016
Volume: 3
Issue: 2
Pages: 21
Source title: International Journal of Applied Industrial Engineering (IJAIE)
Editor(s)-in-Chief: Lanndon Ocampo (Cebu Technological University, Philippines)
DOI: 10.4018/IJAIE.2016070101

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Abstract

The Job shop scheduling problem is an important concern in the manufacturing systems. In this paper, the authors have proposed a hybrid firefly algorithm-tabu search combination technique to solve the Job shop scheduling problems. In the proposed algorithm, a complete scheme of algorithm for Job shop scheduling problems is designed and tabu search algorithm is incorporated with the aim of searching for local optimum of each individual. In order to improve the quality of solutions, in each step of the hybrid algorithms, an effective heuristic is proposed. The proposed heuristic reduces the overtime costs of operations by efficient use of the operation's slack. The performance of the proposed algorithm is tested and evaluated solving well-known benchmarked problems. Finally, the computational results are provided for evaluating the performance and effectiveness of the proposed solution approaches. The results have proved the superiority of proposed approach to other methods such as particle swarm optimization, genetic algorithm in terms of both efficiency and success rate.

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