A genetic algorithm/mathematical programming approach to solve a two-level soft drink production problem

dc.creatorToledo, CFM
dc.creatorde Oliveira, L
dc.creatorPereira, RD
dc.creatorFranca, PM
dc.creatorMorabito, R
dc.date2014
dc.dateAUG
dc.date2014-08-01T12:54:25Z
dc.date2015-11-26T17:42:42Z
dc.date2014-08-01T12:54:25Z
dc.date2015-11-26T17:42:42Z
dc.date.accessioned2018-03-29T00:24:40Z
dc.date.available2018-03-29T00:24:40Z
dc.identifierComputers & Operations Research. Pergamon-elsevier Science Ltd, v. 48, n. 40, n. 52, 2014.
dc.identifier0305-0548
dc.identifier1873-765X
dc.identifierWOS:000336471900005
dc.identifier10.1016/j.cor.2014.02.012
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/75865
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/75865
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1287378
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionThis study applies a genetic algorithm embedded with mathematical programming techniques to solve a synchronized and integrated two-level lot sizing and scheduling problem motivated by a real-world problem that arises in soft drink production. The problem considers a production process compounded by raw material preparation/storage and soft drink bottling. The lot sizing and scheduling decisions should be made simultaneously for raw material preparation/storage in tanks and soft drink bottling in several production lines minimizing inventory, shortage and setup costs. The literature provides mixed-integer programming models for this problem, as well as solution methods based on evolutionary algorithms and relax-and-fix approaches. The method applied by this paper uses a new approach which combines a genetic algorithm (GA) with mathematical programming techniques. The GA deals with sequencing decisions for production lots, so that an exact method can solve a simplified linear programming model, responsible for lot sizing decisions. The computational results show that this evolutionary/mathematical programming approach outperforms the literature methods in terms of production costs and run times when applied to a set of real-world problem instances provided by a soft drink company. (C) 2014 Elsevier Ltd. All rights reserved.
dc.description48
dc.description40
dc.description52
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionCNPq [483474/2013-4]
dc.descriptionFAPESP [2010/10133-0]
dc.languageen
dc.publisherPergamon-elsevier Science Ltd
dc.publisherOxford
dc.publisherInglaterra
dc.relationComputers & Operations Research
dc.relationComput. Oper. Res.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectGenetic algorithms
dc.subjectMathematical programming
dc.subjectMathheuristics
dc.subjectSoft drink industry
dc.subjectProduction planning
dc.subjectLot sizing and scheduling
dc.subjectDependent Setup Costs
dc.subjectLot-sizing Problem
dc.subjectScheduling Problem
dc.subjectMemetic Algorithm
dc.subjectHeuristics
dc.subjectModels
dc.subjectComplexity
dc.subjectSearch
dc.subjectPlant
dc.titleA genetic algorithm/mathematical programming approach to solve a two-level soft drink production problem
dc.typeArtículos de revistas


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