 Combinatorial artificial bee colony algorithm hybridised with a new release of iterated local search for job shop scheduling problemAmaria Ouis Khedim, Mehdi Souier and Zaki Sari International Journal of Operational Research, 2022, vol. 44, issue 4, 435-461 Abstract: Job shop scheduling problem (JSP) is recognised as an attractive subject in production management and combinatorial optimisation. However, it is known as one of the most difficult scheduling problems. The present paper investigates the job shop scheduling problem in order to minimise the makespan with a new hybrid combinatorial artificial bee colony algorithm. Firstly, the proposed combinatorial version integrates a position based crossover for the updating of solutions and the rank-based selection for selecting solutions to be updated in the onlooker bees phase. Another purpose of this study consists to highlight the impact of its sequential hybridisation with a new release of iterated local search method called 'simple iterated local search (SILS)'. The proposed approaches are tested on many benchmark problems taken from the Operations Research Library (OR-Library). The simulation results show that the hybrid CABC performs the best in most of the studied cases. Keywords: job shop scheduling problem; JSP; metaheuristics; artificial bee colony algorithm; iterated local search. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:44:y:2022:i:4:p:435-461 Access Statistics for this article More articles in International Journal of Operational Research from Inderscience Enterprises Ltd |
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