Optimisation of job shop scheduling problem using genetic algorithm and simulated annealing: a case study of manufacturing industry

Rakesh Kumar Phanden (), Shrajal Gupta (), Biruk Wolde (), Ravinder Kumar () and Ayon Chakraborty ()
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Rakesh Kumar Phanden: Federation University Australia
Shrajal Gupta: Jagan Institute of Management Studies
Biruk Wolde: Arba Minch University
Ravinder Kumar: Amity University Uttar Pradesh
Ayon Chakraborty: Federation University Australia

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 3, No 1, 883-892

Abstract: Abstract Production scheduling is an important activity within the manufacturing system to improve its performance. It is a process of assigning resources to the task or vice versa, which depends upon the configuration of the shop floor and the type of products to be manufactured. In job shops, scheduling is a very complex task since it involves a variety of products to process on a limited number of machines to cut down on the amount of time it takes to do tasks. In the present work, a case study from the manufacturing industry has been taken to maximise the amount of time it takes to do tasks (i.e., makespan) having job shop configuration. Two distinguished nature-inspired algorithms, viz Simulated annealing (SA) and Genetic algorithm (GA), have been pragmatic in optimising the existing schedule. The results show that GA outperform the SA by a 1.76% increment in the makespan value. Also, the GA and SA possessed better results than the company’s existing production schedule by 32.23 and 31.02%, respectively.

Keywords: Production scheduling; Job shop scheduling; Simulated annealing; And genetic algorithm; Case study (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13198-025-02714-7

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