A MILP facility location model with distance value adjustments for demand fulfillment using Google Maps
In this article, a facility location model was designed to support logistics operations, considering service distance limitations for demand fulfillment and a list of candidate locations within a supply chain. Consequently, an allocation model was designed using Mixed-Integer Linear Programming (MIL...
- Autores:
-
Palomino, Kevin
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad del Atlántico
- Repositorio:
- Repositorio Uniatlantico
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniatlantico.edu.co:20.500.12834/808
- Acceso en línea:
- https://hdl.handle.net/20.500.12834/808
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131575059&doi=10.36909%2fjer.10473&partnerID=40&md5=b012d0960da0bdc95dc52eb31826ff3e
- Palabra clave:
- Facility location
Logistics engineering
MILP
Supply chain management
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc/4.0/
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A MILP facility location model with distance value adjustments for demand fulfillment using Google Maps |
| title |
A MILP facility location model with distance value adjustments for demand fulfillment using Google Maps |
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A MILP facility location model with distance value adjustments for demand fulfillment using Google Maps Facility location Logistics engineering MILP Supply chain management |
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A MILP facility location model with distance value adjustments for demand fulfillment using Google Maps |
| title_full |
A MILP facility location model with distance value adjustments for demand fulfillment using Google Maps |
| title_fullStr |
A MILP facility location model with distance value adjustments for demand fulfillment using Google Maps |
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A MILP facility location model with distance value adjustments for demand fulfillment using Google Maps |
| title_sort |
A MILP facility location model with distance value adjustments for demand fulfillment using Google Maps |
| dc.creator.fl_str_mv |
Palomino, Kevin |
| dc.contributor.author.none.fl_str_mv |
Palomino, Kevin |
| dc.contributor.other.none.fl_str_mv |
Garcia, David Berdugo, Carmen |
| dc.subject.keywords.spa.fl_str_mv |
Facility location Logistics engineering MILP Supply chain management |
| topic |
Facility location Logistics engineering MILP Supply chain management |
| description |
In this article, a facility location model was designed to support logistics operations, considering service distance limitations for demand fulfillment and a list of candidate locations within a supply chain. Consequently, an allocation model was designed using Mixed-Integer Linear Programming (MILP), in which a finite number of demand nodes could be satisfied by a set of supply nodes, considering not only the costs related to these locations, but also restrictions aimed at improving the level of service based on distance. Besides, an integrated solution scheme was proposed that includes a macro in VBA language that calculates the distance between nodes using the web mapping service developed by Google Maps and solving the model through a branch and cut algorithm. Subsequently, a case study was executed, where the supply operation of an important Colombian retail company is analyzed. The results reflected positive effects not only on costs, but also on the prioritization of average distance traveled and on the satisfaction of store demand by distribution centers. Thus, the conditions in which the implementation of this model provides strategic benefits were verified, functioning as a tool to support decision making. |
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2020 |
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2020-01-27 |
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2021-10-13 |
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2022-11-15T19:25:05Z |
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2022-11-15T19:25:05Z |
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10.36909/jer.10473 |
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Universidad del Atlántico |
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Repositorio Universidad del Atlántico |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131575059&doi=10.36909%2fjer.10473&partnerID=40&md5=b012d0960da0bdc95dc52eb31826ff3e |
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eng |
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Palomino, Kevin51e6bf73-5bd7-4787-91cb-1cc7fb94f53eGarcia, DavidBerdugo, Carmen2022-11-15T19:25:05Z2022-11-15T19:25:05Z2021-10-132020-01-27https://hdl.handle.net/20.500.12834/80810.36909/jer.10473Universidad del AtlánticoRepositorio Universidad del Atlánticohttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85131575059&doi=10.36909%2fjer.10473&partnerID=40&md5=b012d0960da0bdc95dc52eb31826ff3eIn this article, a facility location model was designed to support logistics operations, considering service distance limitations for demand fulfillment and a list of candidate locations within a supply chain. Consequently, an allocation model was designed using Mixed-Integer Linear Programming (MILP), in which a finite number of demand nodes could be satisfied by a set of supply nodes, considering not only the costs related to these locations, but also restrictions aimed at improving the level of service based on distance. Besides, an integrated solution scheme was proposed that includes a macro in VBA language that calculates the distance between nodes using the web mapping service developed by Google Maps and solving the model through a branch and cut algorithm. Subsequently, a case study was executed, where the supply operation of an important Colombian retail company is analyzed. The results reflected positive effects not only on costs, but also on the prioritization of average distance traveled and on the satisfaction of store demand by distribution centers. Thus, the conditions in which the implementation of this model provides strategic benefits were verified, functioning as a tool to support decision making.application/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/Attribution-NonCommercial 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Journal of Engineering ResearchA MILP facility location model with distance value adjustments for demand fulfillment using Google MapsPúblico generalFacility locationLogistics engineeringMILPSupply chain managementinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1BarranquillaIngeniería IndustrialSede NorteAfify, B. et al. 2019. Evolutionary learning algorithm for reliable facility location under disruption. Expert Systems with Applications 115: p.223–244.Biajoli, F.L., Chaves, A.A., & Lorena, L.A.N. 2019. 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Transportation Research Part B: Methodological 104: p.82–105.http://purl.org/coar/resource_type/c_2df8fbb1ORIGINAL10473-Article Text-72561-2-10-20220530.pdf10473-Article Text-72561-2-10-20220530.pdfapplication/pdf1333936https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/808/1/10473-Article%20Text-72561-2-10-20220530.pdfd42e83b15cee419ede9399309ac6454fMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/808/2/license_rdf24013099e9e6abb1575dc6ce0855efd5MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81306https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/808/3/license.txt67e239713705720ef0b79c50b2ececcaMD5320.500.12834/808oai:repositorio.uniatlantico.edu.co:20.500.12834/8082022-11-15 14:25:06.585DSpace de la Universidad de Atlánticosysadmin@mail.uniatlantico.edu.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 |