A no-delay single machine scheduling problem to minimize total weighted early and late work

dc.contributor.authorKrimi, Issam
dc.contributor.authorBenmansour, Rachid
dc.contributor.authorTodosijević, Raca
dc.contributor.authorMladenovic, Nenad
dc.contributor.authorRatli, Mustapha
dc.date.accessioned2022-07-06T07:02:15Z
dc.date.accessioned2023-08-23T05:12:46Z
dc.date.available2022-07-06T07:02:15Z
dc.date.available2023-08-23T05:12:46Z
dc.date.issued2022
dc.description.abstractThis paper investigates the no-delay single machine scheduling problem to minimize the total weighted early and late work. This criterion is one of the most important objectives in practice but has not been studied so far in the literature. First, we formulate the problem as a 0–1 integer programming formulation. Since the complexity of the problem is proven to be NP-hard, we propose two metaheuristics, namely General Variable Neighborhood Search and hybrid GRASP-VND, based on new neighborhood structures. The results demonstrate that all proposed methods can solve optimally instances up to 30 jobs. However, extensive computational experimentation, on large-sized instances, show that the quality of the solutions given by GVNS is better than that obtained by hybrid GRASP-VND algorithm.en_US
dc.identifier.citationKrimi, I., Benmansour, R., Todosijević, R., Mladenovic, N., & Ratli, M. (2023). A no-delay single machine scheduling problem to minimize total weighted early and late work. Optimization Letters, 17(9), 2113-2131.en_US
dc.identifier.doihttps://doi.org/10.1007/s11590-022-01849-x
dc.identifier.urihttps://dspace-uat.adu.ac.ae/handle/1/3886
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectSingle machine schedulingen_US
dc.subjectEarly and late worken_US
dc.subjectGRASP-VNDen_US
dc.subjectGeneral Variable Neighborhood Searchen_US
dc.subject0–1 Integer Programmingen_US
dc.titleA no-delay single machine scheduling problem to minimize total weighted early and late worken_US
dc.title.alternativeOptimization Lettersen_US
dc.typeArticleen_US

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