2014
DOI: 10.1155/2014/214615
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Hybrid Metaheuristics for Solving a Fuzzy Single Batch-Processing Machine Scheduling Problem

Abstract: This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM) scheduling in the presence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA), variable neighborhood search (VNS), and simulated annealing (SA) frameworks. Be… Show more

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Cited by 5 publications
(1 citation statement)
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“…As stated above, meta-heuristics such as GA and TLBO have been applied to solve EHFSP; however, as an algorithm inspired by the sociopolitical behaviors, imperialist competitive algorithm (ICA) [40] is seldom used to deal with EHFSP. ICA possesses some new features such as good neighborhood search ability, effective global search property and good convergence rate [41] and also has the extensive applications to many production scheduling problems in single machine [42], parallel machines [43], flow shop [44], [45], job shop [46], [47] and open shop [48] etc, so it is necessary to investigate the advantages of ICA on solving EHFSP.…”
Section: Introductionmentioning
confidence: 99%
“…As stated above, meta-heuristics such as GA and TLBO have been applied to solve EHFSP; however, as an algorithm inspired by the sociopolitical behaviors, imperialist competitive algorithm (ICA) [40] is seldom used to deal with EHFSP. ICA possesses some new features such as good neighborhood search ability, effective global search property and good convergence rate [41] and also has the extensive applications to many production scheduling problems in single machine [42], parallel machines [43], flow shop [44], [45], job shop [46], [47] and open shop [48] etc, so it is necessary to investigate the advantages of ICA on solving EHFSP.…”
Section: Introductionmentioning
confidence: 99%