Mostrar el registro sencillo del ítem
A MILP facility location model with distance value adjustments for demand fulfillment using Google Maps
| dc.contributor.author | Palomino, Kevin | |
| dc.contributor.other | Garcia, David | |
| dc.contributor.other | Berdugo, Carmen | |
| dc.date.accessioned | 2022-11-15T19:25:05Z | |
| dc.date.available | 2022-11-15T19:25:05Z | |
| dc.date.issued | 2021-10-13 | |
| dc.date.submitted | 2020-01-27 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12834/808 | |
| dc.description.abstract | 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. | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.language.iso | eng | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.source | Journal of Engineering Research | spa |
| dc.title | A MILP facility location model with distance value adjustments for demand fulfillment using Google Maps | spa |
| dcterms.bibliographicCitation | Afify, B. et al. 2019. Evolutionary learning algorithm for reliable facility location under disruption. Expert Systems with Applications 115: p.223–244. | spa |
| dcterms.bibliographicCitation | Biajoli, F.L., Chaves, A.A., & Lorena, L.A.N. 2019. A biased random-key genetic algorithm for the two-stage capacitated facility location problem. Expert Systems with Applications 115: p.418–426. | spa |
| dcterms.bibliographicCitation | Boujelben, M.K., Gicquel, C., & Minoux, M. 2016. A MILP model and heuristic approach for facility location under multiple operational constraints. Computers & Industrial Engineering 98: p.446–461. | spa |
| dcterms.bibliographicCitation | Branson, S. et al. 2018. From Google Maps to a fine-grained catalog of street trees. ISPRS Journal of Photogrammetry and Remote Sensing 135: p.13–30. | spa |
| dcterms.bibliographicCitation | Cadarso, L., Codina, E., Escudero, L.F., & Marín, A. 2017. Rapid transit network design: Considering recovery robustness and risk aversion measures. Transportation Research Procedia 22: p.255–264. | spa |
| dcterms.bibliographicCitation | Catal, C., Fenerci, A., Ozdemir, B., & Gulmez, O. 2015. Improvement of Demand Forecasting Models with Special Days. Procedia Computer Science 59: p.262–267. | spa |
| dcterms.bibliographicCitation | Cedolin, M., Göker, N., Dogu, E., & Esra Albayrak, Y. 2018. Facility Location Selection Employing Fuzzy DEA and Fuzzy Goal Programming Techniques. In J. Kacprzyk, E. Szmidt, S. Zadrożny, K. T. Atanassov, & M. Krawczak (eds) Advances in Fuzzy Logic and Technology 2017, 466–476. Cham: Springer International Publishing | spa |
| dcterms.bibliographicCitation | Chalupa, D., & Nielsen, P. 2019. A simple and robust Monte Carlo hybrid local search algorithm for the facility location problem. Engineering Optimization 51(5): p.832–845. | spa |
| dcterms.bibliographicCitation | Chopra, S., & Meindl, P. 2016. Supply Chain Management: Strategy, Planning, and Operation. Chapter 1: Understanding the Supply Chain. Section 1.4 Decision phases in a supply chain 6th ed. Pearson Education (ed). USA. | spa |
| dcterms.bibliographicCitation | Coniglio, S., Fliege, J., & Walton, R. 2017. Facility Location with Item Storage and Delivery. In A. Sforza & C. Sterle (eds) Optimization and Decision Science: Methodologies and Applications, 287–294. Cham: Springer International Publishing | spa |
| dcterms.bibliographicCitation | Correia, I., & Melo, T. 2016. Multi-period capacitated facility location under delayed demand satisfaction. European Journal of Operational Research 255(3): p.729–746. | spa |
| dcterms.bibliographicCitation | Correia, I., & Melo, T. 2017. A multi-period facility location problem with modular capacity adjustments and flexible demand fulfillment. Computers & Industrial Engineering 110: p.307–321. | spa |
| dcterms.bibliographicCitation | Council of Supply Chain Management Professionals (CSCMP). 2013. Supply chain management: Terms and Glossary. : p.222. Available at: https://cscmp.org/CSCMP/Educate/SCM_Definitions_and_Glossary_of_Terms/CSCMP/Educate/. | spa |
| dcterms.bibliographicCitation | Ding, L., & Zhang, N. 2016. A Travel Mode Choice Model Using Individual Grouping Based on Cluster Analysis. Procedia Engineering 137: p.786–795. | spa |
| dcterms.bibliographicCitation | Drezner, T., Drezner, Z., & Schöbel, A. 2018. The Weber obnoxious facility location model: A Big Arc Small Arc approach. Computers & Operations Research 98: p.240–250. | spa |
| dcterms.bibliographicCitation | Du, B., Zhou, H., & Leus, R. 2020. A two-stage robust model for a reliable p-center facility location problem. Applied Mathematical Modelling 77: p.99–114. | spa |
| dcterms.bibliographicCitation | Emirhüseyinoğlu, G., & Ekici, A. 2019. Dynamic facility location with supplier selection under quantity discount. Computers & Industrial Engineering 134: p.64–74. | spa |
| dcterms.bibliographicCitation | Farahani, R.Z., Fallah, S., Ruiz, R., Hosseini, S., & Asgari, N. 2019. OR models in urban service facility location: A critical review of applications and future developments. European Journal of Operational Research 276(1): p.1–27. | spa |
| dcterms.bibliographicCitation | Golpîra, H. 2020. Optimal integration of the facility location problem into the multi-project multi-supplier multiresource Construction Supply Chain network design under the vendor managed inventory strategy. Expert Systems with Applications 139: p.112841. | spa |
| dcterms.bibliographicCitation | Google LLC. 2017a. Distance Matrix API: Developer Guide. Google Maps Platform. Available at: https://developers.google.com/maps/documentation/distance-matrix/intro [Accessed January 10, 2017]. | spa |
| dcterms.bibliographicCitation | Google LLC. 2017b. Google Maps Platform. Google Cloud. Available at: https://cloud.google.com/mapsplatform/ maps/ [Accessed January 10, 2017]. | spa |
| dcterms.bibliographicCitation | Guo, P., Cheng, W., & Wang, Y. 2017. Hybrid evolutionary algorithm with extreme machine learning fitness function evaluation for two-stage capacitated facility location problems. Expert Systems with Applications 71: p.57–68. | spa |
| dcterms.bibliographicCitation | Hajipour, V., Fattahi, P., Tavana, M., & Caprio, D. Di. 2016. Multi-objective multi-layer congested facility location-allocation problem optimization with Pareto-based meta-heuristics. Applied Mathematical Modelling 40(7): p.4948–4969. | spa |
| dcterms.bibliographicCitation | Jakubovskis, A. 2017. Strategic facility location, capacity acquisition, and technology choice decisions under demand uncertainty: Robust vs. non-robust optimization approaches. European Journal of Operational Research 260(3): p.1095–1104. | spa |
| dcterms.bibliographicCitation | Karatas, M. 2017. A multi-objective facility location problem in the presence of variable gradual coverage performance and cooperative cover. European Journal of Operational Research 262(3): p.1040–1051. | spa |
| dcterms.bibliographicCitation | Karatas, M., & Yakıcı, E. 2018. An iterative solution approach to a multi-objective facility location problem. Applied Soft Computing 62: p.272–287. | spa |
| dcterms.bibliographicCitation | Khosravi, S., & Jokar, M.R.A. 2017. Facility and hub location model based on gravity rule. Computers & Industrial Engineering 109: p.28–38. | spa |
| dcterms.bibliographicCitation | Kınay, Ö.B., Saldanha-da-Gama, F., & Kara, B.Y. 2019. On multi-criteria chance-constrained capacitated single-source discrete facility location problems. Omega 83: p.107–122. | spa |
| dcterms.bibliographicCitation | Kumarage, S. 2018. Use of Crowdsourced Travel Time Data in Traffic Engineering Applications. University of Moratuwa. | spa |
| dcterms.bibliographicCitation | Land, A.H., & Doig, A.G. 1960. An Automatic Method of Solving Discrete Programming Problems. Econometrica 28(3): p.497–520. | spa |
| dcterms.bibliographicCitation | Lin, Y. et al. 2018. An improved artificial bee colony for facility location allocation problem of end-of-life vehicles recovery network. Journal of Cleaner Production 205: p.134–144. | spa |
| dcterms.bibliographicCitation | Manthey, B., & Tijink, M.B. 2018. Perturbation resilience for the facility location problem. Operations Research Letters 46(2): p.215–218. | spa |
| dcterms.bibliographicCitation | Mei, L., Li, M., Ye, D., & Zhang, G. 2019. Facility location games with distinct desires. Discrete Applied Mathematics 264: p.148–160. | spa |
| dcterms.bibliographicCitation | Nasiri, M.M., Mahmoodian, V., Rahbari, A., & Farahmand, S. 2018. A modified genetic algorithm for the capacitated competitive facility location problem with the partial demand satisfaction. Computers & Industrial Engineering 124: p.435–448. | spa |
| dcterms.bibliographicCitation | Orjuela-Castro, J.A., Sanabria-Coronado, L.A., & Peralta-Lozano, A.M. 2017. Coupling facility location models in the supply chain of perishable fruits. Research in Transportation Business & Management 24: p.73–80. | spa |
| dcterms.bibliographicCitation | Pusponegoro, N.H., Rachmawati, R.N., Notodiputro, K.A., & Sartono, B. 2017. Linear Mixed Model for Analyzing Longitudinal Data: A Simulation Study of Children Growth Differences. Procedia Computer Science 116: p.284–291. | spa |
| dcterms.bibliographicCitation | Qi, M., Xia, M., Zhang, Y., & Miao, L. 2017. Competitive facility location problem with foresight considering service distance limitations. Computers & Industrial Engineering 112: p.483–491. | spa |
| dcterms.bibliographicCitation | Rachmawati, R.N., & Pusponegoro, N.H. 2017. Hierarchical Linear Mixed Model for Poverty Analysis in Indonesia. Procedia Computer Science 116: p.182–189. | spa |
| dcterms.bibliographicCitation | Rohaninejad, M., Navidi, H., Nouri, B.V., & Kamranrad, R. 2017. A new approach to cooperative competition in facility location problems: Mathematical formulations and an approximation algorithm. Computers & Operations Research 83: p.45–53. | spa |
| dcterms.bibliographicCitation | SAS Institute Inc. 2014. SAS/OR® 13.2 User’s Guide: Mathematical Programming - The OPTMODEL Procedure. North Carolina, USA. | spa |
| dcterms.bibliographicCitation | SAS Institute Inc. 2018. SAS/OR(R) 14.1 User’s Guide: Mathematical Programming - The Mixed Integer Linear Programming Solver. Available at: http://support.sas.com/documentation/cdl/en/ormpug/68156/HTML/default/viewer.htm#ormpug_milpsolver _overview.htm [Accessed January 10, 2018]. | spa |
| dcterms.bibliographicCitation | Sauvey, C., Melo, T., & Correia, I. 2019. Heuristics for a multi-period facility location problem with delayed demand satisfaction. Computers & Industrial Engineering: p.106171. | spa |
| dcterms.bibliographicCitation | Semeida, A.M. 2014. Derivation of travel demand forecasting models for low population areas: the case of Port Said Governorate, North East Egypt. Journal of Traffic and Transportation Engineering (English Edition) 1(3): p.196–208. | spa |
| dcterms.bibliographicCitation | Tadros, S.A., Galal, N.M., Ghazy, M., & ElSayed, A.E. 2018. A multi-objective two echelon capacitated facility location problem. In 2018 7th International Conference on Industrial Technology and Management (ICITM), 260–264. | spa |
| dcterms.bibliographicCitation | Teye, C., Bell, M.G.H., & Bliemer, M.C.J. 2017. Urban intermodal terminals: The entropy maximising facility location problem. Transportation Research Part B: Methodological 100: p.64–81. | spa |
| dcterms.bibliographicCitation | Wang, Wei, Wang, Wanmei, Mosley, T.H., & Griswold, M.E. 2017. A SAS macro for the joint modeling of longitudinal outcomes and multiple competing risk dropouts. Computer Methods and Programs in Biomedicine 138: p.23–30. | spa |
| dcterms.bibliographicCitation | Yang, S.Y., & Hsu, C.L. 2016. A location-based services and Google maps-based information master system for tour guiding. Computers and Electrical Engineering 54: p.87–105. | spa |
| dcterms.bibliographicCitation | Yang, Z., Chen, H., Chu, F., & Wang, N. 2019. An effective hybrid approach to the two-stage capacitated facility location problem. European Journal of Operational Research 275(2): p.467–480. | spa |
| dcterms.bibliographicCitation | Yu, G., Haskell, W.B., & Liu, Y. 2017. Resilient facility location against the risk of disruptions. Transportation Research Part B: Methodological 104: p.82–105. | spa |
| datacite.rights | http://purl.org/coar/access_right/c_abf2 | spa |
| oaire.resourcetype | http://purl.org/coar/resource_type/c_2df8fbb1 | spa |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
| dc.audience | Público general | spa |
| dc.identifier.doi | 10.36909/jer.10473 | |
| dc.identifier.instname | Universidad del Atlántico | spa |
| dc.identifier.reponame | Repositorio Universidad del Atlántico | spa |
| dc.identifier.url | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131575059&doi=10.36909%2fjer.10473&partnerID=40&md5=b012d0960da0bdc95dc52eb31826ff3e | |
| dc.rights.cc | Attribution-NonCommercial 4.0 International | * |
| dc.subject.keywords | Facility location | spa |
| dc.subject.keywords | Logistics engineering | spa |
| dc.subject.keywords | MILP | spa |
| dc.subject.keywords | Supply chain management | spa |
| dc.type.driver | info:eu-repo/semantics/article | spa |
| dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | spa |
| dc.type.spa | Artículo | spa |
| dc.publisher.place | Barranquilla | spa |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | spa |
| dc.publisher.discipline | Ingeniería Industrial | spa |
| dc.publisher.sede | Sede Norte | spa |
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
Excepto si se señala otra cosa, la licencia del ítem se describe como http://creativecommons.org/licenses/by-nc/4.0/
Tecnología DSpace implementada por 