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dc.contributor.authorYuce, B
dc.contributor.authorMastrocinque, E
dc.date.accessioned2022-06-23T14:34:53Z
dc.date.issued2020-01-01
dc.date.updated2022-06-23T13:33:22Z
dc.description.abstractSupply chain network design is one of the most important strategic issues in operations management. The main objective in designing a supply chain is to keep the cost as low as possible. However, the modelling of a supply chain requires more than single-objective such as lead-time minimization, service level maximization, and environmental impact maximization among others. Usually these objectives may cause conflicts such as increasing the service level usually causes a growth in costs. Therefore, the aim should be to find trade-off solutions to satisfy the conflicting objectives. The aim of this chapter is to propose a new method based on a hybrid version of the Bees Algorithm with Slope Angle Computation and Hill Climbing Algorithm to solve a multi-objective supply chain network design problem. A real case from the literature has been selected and solved in order to show the potentiality of the proposed method in solving a large scale combinatorial problem.en_GB
dc.format.extent266-283
dc.identifier.citationIn: Supply Chain and Logistics Management: Concepts, Methodologies, Tools, and Applications, pp. 266-283en_GB
dc.identifier.doihttps://doi.org/10.4018/978-1-7998-0945-6.ch013
dc.identifier.urihttp://hdl.handle.net/10871/130036
dc.identifierORCID: 0000-0002-9937-1535 (Yuce, Baris)
dc.language.isoenen_GB
dc.publisherIGI Globalen_GB
dc.rights© 2020 IGI Globalen_GB
dc.titleSupply chain network design using an enhanced hybrid swarm-based optimization algorithmen_GB
dc.typeBook chapteren_GB
dc.date.available2022-06-23T14:34:53Z
dc.identifier.isbn9781799809456
dc.descriptionThis is the final version. Available from IGI Global via the DOI in this record. en_GB
dc.relation.ispartofSupply Chain and Logistics Management
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2020-01-01
rioxxterms.typeBook chapteren_GB
refterms.dateFCD2022-06-23T14:31:04Z
refterms.versionFCDAM
refterms.dateFOA2022-06-23T14:34:57Z
refterms.dateFirstOnline2020-01-01


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