Scenario-Based Model for the Location of Multiple Uncapacitated Facilities: Case Study in an Agro-Food Supply Chain
Supply chains for agricultural products have taken on great importance in recent times due to the need that exists for consumers to acquire fresh products in excellent quality conditions. In the design of supply networks for this type of product, the location of facilities and allocation decisions a...
- Autores:
-
Mendoza-Ortega, Gean Pablo
Soto, Manuel
Ruiz-Meza, José
Salgado, Rodrigo
Torregroza, Angelica
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12182
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12182
- Palabra clave:
- Agro-food supply chain
Facility location
Mixed integer programming
Scenario-based optimization
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Scenario-Based Model for the Location of Multiple Uncapacitated Facilities: Case Study in an Agro-Food Supply Chain |
| title |
Scenario-Based Model for the Location of Multiple Uncapacitated Facilities: Case Study in an Agro-Food Supply Chain |
| spellingShingle |
Scenario-Based Model for the Location of Multiple Uncapacitated Facilities: Case Study in an Agro-Food Supply Chain Agro-food supply chain Facility location Mixed integer programming Scenario-based optimization |
| title_short |
Scenario-Based Model for the Location of Multiple Uncapacitated Facilities: Case Study in an Agro-Food Supply Chain |
| title_full |
Scenario-Based Model for the Location of Multiple Uncapacitated Facilities: Case Study in an Agro-Food Supply Chain |
| title_fullStr |
Scenario-Based Model for the Location of Multiple Uncapacitated Facilities: Case Study in an Agro-Food Supply Chain |
| title_full_unstemmed |
Scenario-Based Model for the Location of Multiple Uncapacitated Facilities: Case Study in an Agro-Food Supply Chain |
| title_sort |
Scenario-Based Model for the Location of Multiple Uncapacitated Facilities: Case Study in an Agro-Food Supply Chain |
| dc.creator.fl_str_mv |
Mendoza-Ortega, Gean Pablo Soto, Manuel Ruiz-Meza, José Salgado, Rodrigo Torregroza, Angelica |
| dc.contributor.author.none.fl_str_mv |
Mendoza-Ortega, Gean Pablo Soto, Manuel Ruiz-Meza, José Salgado, Rodrigo Torregroza, Angelica |
| dc.subject.keywords.spa.fl_str_mv |
Agro-food supply chain Facility location Mixed integer programming Scenario-based optimization |
| topic |
Agro-food supply chain Facility location Mixed integer programming Scenario-based optimization |
| description |
Supply chains for agricultural products have taken on great importance in recent times due to the need that exists for consumers to acquire fresh products in excellent quality conditions. In the design of supply networks for this type of product, the location of facilities and allocation decisions are among the most relevant decisions in operations management, which in many cases involve aspects of uncertainty. This research proposes to plan the distribution of multiple agro-food products taking into account the location and capacities of producers, potential location of facilities and variations in crop yields. A scenario-based optimization model for the location of multiple uncapacitated facilities is developed. The model is tested in the cassava agro-food chain in Sucre, Colombia. First, the description for the construction of the scenarios for the uncertainty associated with the yield per hectare of cassava crops is presented. Next, the mixed integer programming model (MIP) for the location of uncapacitated facilities (UFLP) is presented. The aim of the model is to minimize operational distribution costs. The results of the case study were obtained with the help of the CPLEX Solver integrated in GAMS in low computational time. A reduction of costs by almost 60% of the distribution costs is obtained. |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021-09-29 |
| dc.date.accessioned.none.fl_str_mv |
2023-07-19T21:17:03Z |
| dc.date.available.none.fl_str_mv |
2023-07-19T21:17:03Z |
| dc.date.submitted.none.fl_str_mv |
2023-07 |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/draft |
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http://purl.org/coar/resource_type/c_6501 |
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draft |
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https://hdl.handle.net/20.500.12585/12182 |
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10.1007/978-3-030-86702-7_33 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12182 |
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10.1007/978-3-030-86702-7_33 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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eng |
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eng |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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13 páginas |
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Pdf |
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application/pdf |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
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Communications in Computer and Information Science - Vol. 1431 (2021) |
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Universidad Tecnológica de Bolívar |
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Mendoza-Ortega, Gean Pablobd374d14-9f4b-4a82-9239-bd5cf2188cd1Soto, Manuelc97241e0-ee78-45ec-bcba-fccd0c0625a3Ruiz-Meza, Joséc98cae4b-3334-4570-8eb3-9a5fc32e24a2Salgado, Rodrigo796b1e56-67e8-4414-9aba-0e0e9000ae72Torregroza, Angelica43ca98f0-5ee9-43b9-a46d-91bd8f394c0d2023-07-19T21:17:03Z2023-07-19T21:17:03Z2021-09-292023-07https://hdl.handle.net/20.500.12585/1218210.1007/978-3-030-86702-7_33Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarSupply chains for agricultural products have taken on great importance in recent times due to the need that exists for consumers to acquire fresh products in excellent quality conditions. In the design of supply networks for this type of product, the location of facilities and allocation decisions are among the most relevant decisions in operations management, which in many cases involve aspects of uncertainty. This research proposes to plan the distribution of multiple agro-food products taking into account the location and capacities of producers, potential location of facilities and variations in crop yields. A scenario-based optimization model for the location of multiple uncapacitated facilities is developed. The model is tested in the cassava agro-food chain in Sucre, Colombia. First, the description for the construction of the scenarios for the uncertainty associated with the yield per hectare of cassava crops is presented. Next, the mixed integer programming model (MIP) for the location of uncapacitated facilities (UFLP) is presented. The aim of the model is to minimize operational distribution costs. The results of the case study were obtained with the help of the CPLEX Solver integrated in GAMS in low computational time. A reduction of costs by almost 60% of the distribution costs is obtained.13 páginasPdfapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Communications in Computer and Information Science - Vol. 1431 (2021)Scenario-Based Model for the Location of Multiple Uncapacitated Facilities: Case Study in an Agro-Food Supply Chaininfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Agro-food supply chainFacility locationMixed integer programmingScenario-based optimizationCartagena de IndiasChen, Z.-L.: Integrated production and distribution operations. In: Simchi-Levi, D., Wu, S.D., Shen, Z.J. (eds.) Handbook of Quantitative Supply Chain Analysis, pp. 711–745. Springer, Boston (2004). https://doi.org/10.1007/978-1-4020-7953-5 17Chopra, S., Meindl, P. (2013) Administración De La Cadena De Suministro. Cited 62 times. Pearson educaciónMuñoz Aguilar, R.A., Roldan Zuluaga, S. Competitividad Y Cadenas De Abastec-imiento En El Sector Productivo Del Valle Del Cauca, Colombia (Competitiveness and supply chain in the productive sector of Valle Del Cauca, Colombia) (2015) Revista Global De Negocios, 4 (1), pp. 77-87. Cited 3 times.Reina, M.L., Adarme, W. Logística de distribución de productos perecederos: Estudios de caso Fuente de Oro (Meta) y Viotá (Cundinamarca) (2017) Rev. Colomb. Ciencias Hortícolas, 8 (1), pp. 80-91. Cited 6 times.Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., Shankar, R.: Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies. Tata McGraw-Hill Education, New York (2008)Paam, P., Berretta, R., Heydar, M., Middleton, R.H., García-Flores, R., Juliano, P. Planning models to optimize the agri-fresh food supply chain for loss minimization: A review (2016) Ref. Module Food Sci.. Cited 15 timesAhumada, O., Villalobos, J.R. Application of planning models in the agri-food supply chain: A review (2009) European Journal of Operational Research, 196 (1), pp. 1-20. Cited 638 times. doi: 10.1016/j.ejor.2008.02.014Salin, V. Information technology in agri-food supply chains (1998) International Food and Agribusiness Management Review, 1 (3), pp. 329-334. Cited 102 times. http://www.ifama.org/http://www.ifama.org/i4a/pages/index.cfm?pageid=3353http://www.wageningenacademic.com/loi/ifamr doi: 10.1016/s1096-7508(99)80003-2Amorim, P., Günther, H.-O., Almada-Lobo, B. Multi-objective integrated production and distribution planning of perishable products (2012) International Journal of Production Economics, 138 (1), pp. 89-101. Cited 204 times. doi: 10.1016/j.ijpe.2012.03.005Daskin, M.S., Snyder, L.V., Berger, R.T.: Facility location in supply chain design. In: Langevin, A., Riopel, D. (eds.) Logistics Systems: Design and Optimization, pp. 39–65. Springer, Boston (2005). https://doi.org/10.1007/0-387-24977-X 2Solimanpur, M., Kamran, M.A. Solving facilities location problem in the presence of alternative processing routes using a genetic algorithm (2010) Computers and Industrial Engineering, 59 (4), pp. 830-839. Cited 24 times. doi: 10.1016/j.cie.2010.08.010Al-Sultan, K.S., Al-Fawzan, M.A.: A tabu search approach to the uncapacitated facility location problem. Ann. Oper. Res. 86, 91–103 (1999). https://doi.org/10. 1023/A:1018956213524Min, H., Zhou, G. Supply chain modeling: Past, present and future (2002) Computers and Industrial Engineering, 43 (1-2), pp. 231-249. Cited 679 times. doi: 10.1016/S0360-8352(02)00066-9Everingham, Y.L., Muchow, R.C., Stone, R.C., Inman-Bamber, N.G., Singels, A., Bezuidenhout, C.N. Enhanced risk management and decision-making capability across the sugarcane industry value chain based on seasonal climate forecasts (2002) Agricultural Systems, 74 (3), pp. 459-477. Cited 97 times. doi: 10.1016/S0308-521X(02)00050-1Sun, M. Solving the uncapacitated facility location problem using tabu search (2006) Computers and Operations Research, 33 (9), pp. 2563-2589. Cited 106 times. doi: 10.1016/j.cor.2005.07.014De Armas, J., Juan, A.A., Marquès, J.M., Pedroso, J.P. Solving the deterministic and stochastic uncapacitated facility location problem: From a heuristic to a simheuristic (Open Access) (2017) Journal of the Operational Research Society, 68 (10), pp. 1161-1176. Cited 66 times. https://www.tandfonline.com/loi/tjor20 doi: 10.1057/s41274-016-0155-6Hakli, H., Ortacay, Z. An improved scatter search algorithm for the uncapacitated facility location problem (2019) Computers and Industrial Engineering, 135, pp. 855-867. Cited 34 times. https://www.journals.elsevier.com/computers-and-industrial-engineering doi: 10.1016/j.cie.2019.06.060Cited 153 times. Verter, V.: Uncapacitated and capacitated facility location problems. In: Eiselt, H.A., Marianov, V. (eds.) Foundations of Location Analysis. ISORMS, vol. 155, pp. 25–37. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-7572-0 2Ramshani, M., Ostrowski, J., Zhang, K., Li, X. Two level uncapacitated facility location problem with disruptions (2019) Computers and Industrial Engineering, 137, art. no. 106089. Cited 17 times. https://www.journals.elsevier.com/computers-and-industrial-engineering doi: 10.1016/j.cie.2019.106089Mousavi, S.M., Tavakkoli-Moghaddam, R., Siadat, A. 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Rediseño de una red de distribución con variabilidad de demanda usando la metodología de escenarios (2013) Rev. Fac. Ing., 21 (32), pp. 9-19. Cited 17 times.Quinteros, M., Alonso, A., Escudero, L., Guignard, M., Weintraub, A. Una aplicación de programación estocástica en un problema de gestión forestal (2006) Rev. Ing. Sist. XX. Cited 3 times.Alonso-Ayuso, A., Escudero, L.F., Guignard, M., Weintraub, A. On dealing with strategic and tactical decision levels in forestry planning under uncertainty (2020) Computers and Operations Research, 115, art. no. 104836. Cited 8 times. www.elsevier.com/inca/publications/store/3/0/0/ doi: 10.1016/j.cor.2019.104836Ramos, A., Alonso-Ayuso, A., Pérez, G. (2011) Optimización Bajo Incertidumbre. Universidad Pontificia Comillas. Cited 7 times.de Económico, S. (2007) Encuesta Nacional Agropecuaria De Los Periodos aAgronet: Base Agricola EVA 2007-2017 (2017). http://www.agronet.gov.co/estadistica/Paginas/default.aspx. Accessed 4 Oct 2018Govindan, K., Fattahi, M., Keyvanshokooh, E. Supply chain network design under uncertainty: A comprehensive review and future research directions (2017) European Journal of Operational Research, 263 (1), pp. 108-141. Cited 412 times. doi: 10.1016/j.ejor.2017.04.009Martínez Bencardino, C. Estadística y muestreo. 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