A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion

dc.contributor.author M. Fatih Tasgetiren
dc.contributor.author Quanke Pan
dc.contributor.author Ponnuthurai Nagaratnam Suganthan
dc.contributor.author Adalet Oner
dc.date.accessioned 2025-10-06T17:52:46Z
dc.date.issued 2013
dc.description.abstract In this paper we present a discrete artificial bee colony algorithm to solve the no-idle permutation flowshop scheduling problem with the total tardiness criterion. The no-idle permutation flowshop problem is a variant of the well-known permutation flowshop scheduling problem where idle time is not allowed on machines. In other words the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions: First of all a discrete artificial bee colony algorithm is presented to solve the problem on hand first time in the literature. Secondly some novel methods of calculating the total tardiness from makespan are introduced for the no-idle permutation flowshop scheduling problem. Finally the main contribution of the paper is due to the fact that a novel speed-up method for the insertion neighborhood is developed for the total tardiness criterion. The performance of the discrete artificial bee colony algorithm is evaluated against a traditional genetic algorithm. The computational results show its highly competitive performance when compared to the genetic algorithm. Ultimately we provide the best known solutions for the total tardiness criterion with different due date tightness levels for the first time in the literature for the Taillard's benchmark suit. © 2013 Elsevier Inc. © 2013 Elsevier B.V. All rights reserved.
dc.identifier.doi 10.1016/j.apm.2013.02.011
dc.identifier.issn 0307904X
dc.identifier.issn 0307-904X
dc.identifier.uri https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878203450&doi=10.1016%2Fj.apm.2013.02.011&partnerID=40&md5=e309850bf524a161999dcc56ea818823
dc.identifier.uri https://gcris.yasar.edu.tr/handle/123456789/10100
dc.language.iso English
dc.relation.ispartof Applied Mathematical Modelling
dc.source Applied Mathematical Modelling
dc.subject Artificial Bee Colony Algorithm, Evolutionary Algorithms, Genetic Algorithm, Metaheuristics, No-idle Permutation Flowshop Scheduling Problem, Artificial Bee Colony Algorithms, Competitive Performance, Computational Results, Meta Heuristics, No-idle Permutation Flowshop Scheduling Problems, Permutation Flow Shops, Permutation Flowshop Scheduling Problems, Traditional Genetic Algorithms, Evolutionary Algorithms, Genetic Algorithms, Scheduling, Scheduling Algorithms, Problem Solving
dc.subject Artificial bee colony algorithms, Competitive performance, Computational results, Meta heuristics, No-idle permutation flowshop scheduling problems, Permutation flow shops, Permutation flowshop scheduling problems, Traditional genetic algorithms, Evolutionary algorithms, Genetic algorithms, Scheduling, Scheduling algorithms, Problem solving
dc.title A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion
dc.type Article
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gdc.description.endpage 6779
gdc.description.startpage 6758
gdc.description.volume 37
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gdc.oaire.keywords no-idle permutation flowshop scheduling problem
gdc.oaire.keywords metaheuristics
gdc.oaire.keywords Deterministic scheduling theory in operations research
gdc.oaire.keywords :Engineering::Electrical and electronic engineering [DRNTU]
gdc.oaire.keywords genetic algorithm
gdc.oaire.keywords artificial bee colony algorithm
gdc.oaire.keywords evolutionary algorithms
gdc.oaire.keywords Approximation methods and heuristics in mathematical programming
gdc.oaire.popularity 2.8462065E-8
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gdc.oaire.sciencefields 0202 electrical engineering, electronic engineering, information engineering
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gdc.opencitations.count 84
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oaire.citation.endPage 6779
oaire.citation.startPage 6758
person.identifier.scopus-author-id Tasgetiren- M. Fatih (6505799356), Pan- Quanke (15074237600), Suganthan- Ponnuthurai Nagaratnam (7003996538), Oner- Adalet (48361901600)
project.funder.name M. Fatih Tasgetiren acknowledges the support provided by the TUBITAK (The Scientific and Technological Research Council of Turkey) under the grant # 110M622 . In addition this research is partially supported by National Science Foundation of China under Grants 61174187 .
publicationissue.issueNumber 10-11
publicationvolume.volumeNumber 37
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