Permutation flow-shop scheduling with early and late penalty costs using the Jaya algorithm

Raunaque Paraveen and Manoj Kumar Khurana

International Journal of Operational Research, 2025, vol. 54, issue 3, 310-333

Abstract: The purpose of this study is to use the most efficient meta-heuristic methodologies in permutation flow shop to identify the ideal sequence of jobs with the least amount of penalties for being early and late. The permutation flow shop is a common job shop problem in which all jobs must pass through all machines in a predefined order. Numerous meta-heuristic algorithms have been developed to tackle this problem. However, users often struggle with selecting appropriate algorithm parameters due to the problem's complexity. To address these challenges, this research adopts the recently developed Jaya algorithm, which stands out for being a parameter-less approach that aims to achieve success while avoiding failure. The Jaya algorithm was tested alongside a genetic algorithm using a simulated industry dataset. This dataset contained different scenarios with varying numbers of jobs and machines. The Jaya algorithm consistently outperformed the genetic algorithm, providing superior results for the given problem.

Keywords: permutation flow-shop scheduling; Jaya algorithm; tardiness penalties; earliness penalties. (search for similar items in EconPapers)
Date: 2025
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