Title: A genetic algorithm-based schedule optimisation of a job shop with parallel resources

Authors: Ahmad Wasim; Zawar A. Nawaz Bhatti; Mirza Jahanzaib; Salman Hussain

Addresses: Department of Industrial Engineering, University of Engineering and Technology, Taxila, Pakistan ' Department of Industrial Engineering, University of Engineering and Technology, Taxila, Pakistan ' Department of Industrial Engineering, University of Engineering and Technology, Taxila, Pakistan ' Department of Industrial Engineering, University of Engineering and Technology, Taxila, Pakistan

Abstract: This paper presents a genetic algorithm for a job shop scheduling problem with parallel machines. The objective is to minimise the makespan. After solving an example, the performance of the proposed algorithm was examined on a set of test problems. The computational test was performed with moderate benchmark instances given in the literature. Now, many industrial work centres are shifting their focus towards implementation of job shop scheduling model. Large throughput and just-in-time restrictions have augmented the requirement of additional parallel machines at various production stages. This generates the scope for research on optimising job shop problem with parallel machines. However, research on this topic is very limited as compared to other job shop problems. A genetic algorithm has been proposed in this research work which effectively finds the near optimal makespan schedules for the job shop problem with parallel machines. The algorithm was implemented on the modified benchmarks from the literature and the results were compared with heuristics methods available in literature for solving this problem. It is shown that the proposed GA performs reasonably well when compared to the other techniques under consideration.

Keywords: makespan; work centres; benchmarks; just in time.

DOI: 10.1504/IJQI.2017.090551

International Journal of Quality and Innovation, 2017 Vol.3 No.2/3/4, pp.229 - 246

Received: 06 Jan 2017
Accepted: 29 Oct 2017

Published online: 12 Mar 2018 *

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