A meta-heuristic approach to a strategic mixed inventory-location model: formulation and application
In the present day, it is increasingly more important for the companies to have a distribution network that minimize the logistic costs without reducing the level of service to the customer (delivery time, enough inventory, etc.). To reach conciliation within these objectives that may look conflicti...
- Autores:
-
Orozco-Fontalvo, Mauricio
Cantillo, Victor
Miranda, Pablo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2017
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8745
- Acceso en línea:
- https://hdl.handle.net/11323/8745
https://repositorio.cuc.edu.co/
- Palabra clave:
- Distribution network
inventory location
distribution centers location
genetic algorithm
exhaustive revision
- Rights
- openAccess
- License
- Attribution-NonCommercial 4.0 International
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A meta-heuristic approach to a strategic mixed inventory-location model: formulation and application |
| title |
A meta-heuristic approach to a strategic mixed inventory-location model: formulation and application |
| spellingShingle |
A meta-heuristic approach to a strategic mixed inventory-location model: formulation and application Distribution network inventory location distribution centers location genetic algorithm exhaustive revision |
| title_short |
A meta-heuristic approach to a strategic mixed inventory-location model: formulation and application |
| title_full |
A meta-heuristic approach to a strategic mixed inventory-location model: formulation and application |
| title_fullStr |
A meta-heuristic approach to a strategic mixed inventory-location model: formulation and application |
| title_full_unstemmed |
A meta-heuristic approach to a strategic mixed inventory-location model: formulation and application |
| title_sort |
A meta-heuristic approach to a strategic mixed inventory-location model: formulation and application |
| dc.creator.fl_str_mv |
Orozco-Fontalvo, Mauricio Cantillo, Victor Miranda, Pablo |
| dc.contributor.author.spa.fl_str_mv |
Orozco-Fontalvo, Mauricio Cantillo, Victor Miranda, Pablo |
| dc.subject.spa.fl_str_mv |
Distribution network inventory location distribution centers location genetic algorithm exhaustive revision |
| topic |
Distribution network inventory location distribution centers location genetic algorithm exhaustive revision |
| description |
In the present day, it is increasingly more important for the companies to have a distribution network that minimize the logistic costs without reducing the level of service to the customer (delivery time, enough inventory, etc.). To reach conciliation within these objectives that may look conflicting requires developing some tools that allow decision-making. Having this in mind, the authors present a strategic inventory-location model, multiproduct and different with demand periods. This is a complex problem of integer mixed programming, that allow to determine the optimum distribution network given the fixed, transportation and inventory costs. The problem is illustrated by applying it to a real case of a steel company in Colombia, to resolve it, exhaustive revision and a genetic algorithm were used. The results obtained reveal the importance of the making joint strategic-tactic decisions, as well as the impact of each of the variables considered in the logistics costs. |
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2017 |
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2017 |
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2021-09-22T19:55:13Z |
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2021-09-22T19:55:13Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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2352-1465 |
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https://hdl.handle.net/11323/8745 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
Ahuja, R. K., Magnanti, T. L. & Orlin, J. B., 1993. Network Flows: Theory, Algorithms, and applications. s.l.:Prentice Hall. Balas, E. & Carrera, M., 1996. A Dynamic Subgradient-Based Branch-and-Bound Procedure for Set Covering. Operations research. Berman, O., Krass, D. & Tajbakhsh, M., 2011. A cordinated inventory-location model. European Journal of Operational Research Chopra, S., 2008. Administración de la cadena de suministro. Tercera ed. s.l.:Pearson Church, R. & Revelle, C., 1974. The maximal covering location problem. Papers in regional science Daskin, M. & Coullard, C., 2002. An inventory location model: Formulation, solution algorithm and computacional results. Annals of operational research Daskin, M. & Susan, O. H., 1998. Strategic facility location: A review. European Journal of Operational Research. Diabat, A., Richard, J. & Codrington, C., 2013. A Lagrangian relaxation approach to simultaneous strategic and tactical planning in supply chain design. Annals of operations research Dooley, F., 2005. Logistics, Inventory Control, and Supply Chain Management. Choices. Eppen, G. D., 1979. Effects of Centralization on Expected Costs in a Multi-Location Newsboy Problem. Management Science. Farahani, Asgari, Heidari & Hosseininia, 2012. Covering Problems in Facility Location: A Review. Computer & Industrial Engineering, Volumen 62. Fisher, M. L., 2004. The Lagrangian relaxation method for solving integer programming problems. Management science. Goldberg, E., 1989. Genetic algorithms in search, optimization and machine learning. Addison-Wesley. Holland, J. H., 1975. Adaptation in natural and artifitial systems. University of Michigan press Kaya, O. & Urek, B., 2015. A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain. Computers & Operations Research. Melo, M., Nickel, S. & Saldanha-da-Gama, F., 2009. Facility location and supply chain management. European Journal of Operational Research. Miranda, P. & Garrido, R., 2006. A simultaneous inventory control and facility location model with stochastic capacity constraints. Networks and spatial economics, Volumen VI. Miranda, P. & Rodrigo, G., 2004. Incorporating inventory location control decisions into a strategic distribution network desing model with stochastic demand. Transportation Research Part E. Mousavi, S., Bahreininejad, A., Musa, S. & Yusof, F., 2014. A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. Journal of intelligent manufacturing. Mousavi, S. & Hajipour, V., 2013. Optimizing multi-item multi-period inventory control system with discounted cash flow and inflation: Two calibrated meta-heristic algorithms. Applied mathematical modelling. Pham, D. & Karaboga, D., 2000. Intelligent optimisation techniques. s.l.:Springer. Rao, S. S., 2009. Engineering Optimization: Theory and practice. Cuarta ed. s.l.:John Wiley & Sons, Inc. Saaty, T. L., 1990. How to make a decision: The analytic hierarchy process. European Journal of Operational Research. Shen, Z.-j. M. & Coullard, C., 2003. A joint inventory location model. Transportation Science. Shirley, C. & Winston, C., 2003. Firm inventory behavior and the returns from highway infrastructure investments. Journal of Urban Economics. Soleimani, H. & Kannan, G., 2015. A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network designs in large scale networks. Applied mathematical modelling. Taha, H., 2007. Operations research: An introduction. Octava ed. s.l.:Prentice Hall. Yalaoui, A. & Chehade, H., 2012. Optimization of logistics. s.l.:Wiley. Zanakins, S. &. E. J., 1981. Heuristic "Optimization": Why when and how to use it". Interfaces, Volumen V |
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Orozco-Fontalvo, MauricioCantillo, VictorMiranda, Pablo2021-09-22T19:55:13Z2021-09-22T19:55:13Z20172352-1465https://hdl.handle.net/11323/8745Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In the present day, it is increasingly more important for the companies to have a distribution network that minimize the logistic costs without reducing the level of service to the customer (delivery time, enough inventory, etc.). To reach conciliation within these objectives that may look conflicting requires developing some tools that allow decision-making. Having this in mind, the authors present a strategic inventory-location model, multiproduct and different with demand periods. This is a complex problem of integer mixed programming, that allow to determine the optimum distribution network given the fixed, transportation and inventory costs. The problem is illustrated by applying it to a real case of a steel company in Colombia, to resolve it, exhaustive revision and a genetic algorithm were used. The results obtained reveal the importance of the making joint strategic-tactic decisions, as well as the impact of each of the variables considered in the logistics costs.Orozco-Fontalvo, Mauricio-will be generated-orcid-0000-0003-0514-4647-600Cantillo, Victor-will be generated-orcid-0000-0003-1184-2580-600Miranda, Pabloapplication/pdfengTransportation Research ProcediaAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2World Conference on Transport Research - WCTR 2016 Shanghaihttps://reader.elsevier.com/reader/sd/pii/S2352146517307615?token=2374F4FA9D2D9650D87EFDD21B20824DA710A9992650C0B5BF14C88790A3E5180B3729C96563CAF3647F28B624CD1BEC&originRegion=us-east-1&originCreation=20210922192249Distribution networkinventory locationdistribution centers locationgenetic algorithmexhaustive revisionA meta-heuristic approach to a strategic mixed inventory-location model: formulation and applicationArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionAhuja, R. K., Magnanti, T. L. & Orlin, J. B., 1993. Network Flows: Theory, Algorithms, and applications. s.l.:Prentice Hall.Balas, E. & Carrera, M., 1996. A Dynamic Subgradient-Based Branch-and-Bound Procedure for Set Covering. Operations research.Berman, O., Krass, D. & Tajbakhsh, M., 2011. A cordinated inventory-location model. European Journal of Operational ResearchChopra, S., 2008. Administración de la cadena de suministro. Tercera ed. s.l.:PearsonChurch, R. & Revelle, C., 1974. The maximal covering location problem. Papers in regional scienceDaskin, M. & Coullard, C., 2002. An inventory location model: Formulation, solution algorithm and computacional results. Annals of operational researchDaskin, M. & Susan, O. H., 1998. Strategic facility location: A review. European Journal of Operational Research.Diabat, A., Richard, J. & Codrington, C., 2013. A Lagrangian relaxation approach to simultaneous strategic and tactical planning in supply chain design. Annals of operations researchDooley, F., 2005. Logistics, Inventory Control, and Supply Chain Management. Choices.Eppen, G. D., 1979. Effects of Centralization on Expected Costs in a Multi-Location Newsboy Problem. Management Science.Farahani, Asgari, Heidari & Hosseininia, 2012. Covering Problems in Facility Location: A Review. Computer & Industrial Engineering, Volumen 62.Fisher, M. L., 2004. The Lagrangian relaxation method for solving integer programming problems. Management science.Goldberg, E., 1989. Genetic algorithms in search, optimization and machine learning. Addison-Wesley.Holland, J. H., 1975. Adaptation in natural and artifitial systems. University of Michigan pressKaya, O. & Urek, B., 2015. A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain. Computers & Operations Research.Melo, M., Nickel, S. & Saldanha-da-Gama, F., 2009. Facility location and supply chain management. European Journal of Operational Research.Miranda, P. & Garrido, R., 2006. A simultaneous inventory control and facility location model with stochastic capacity constraints. Networks and spatial economics, Volumen VI.Miranda, P. & Rodrigo, G., 2004. Incorporating inventory location control decisions into a strategic distribution network desing model with stochastic demand. Transportation Research Part E.Mousavi, S., Bahreininejad, A., Musa, S. & Yusof, F., 2014. A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. Journal of intelligent manufacturing.Mousavi, S. & Hajipour, V., 2013. Optimizing multi-item multi-period inventory control system with discounted cash flow and inflation: Two calibrated meta-heristic algorithms. Applied mathematical modelling.Pham, D. & Karaboga, D., 2000. Intelligent optimisation techniques. s.l.:Springer.Rao, S. S., 2009. Engineering Optimization: Theory and practice. Cuarta ed. s.l.:John Wiley & Sons, Inc.Saaty, T. L., 1990. How to make a decision: The analytic hierarchy process. European Journal of Operational Research.Shen, Z.-j. M. & Coullard, C., 2003. A joint inventory location model. Transportation Science.Shirley, C. & Winston, C., 2003. Firm inventory behavior and the returns from highway infrastructure investments. Journal of Urban Economics.Soleimani, H. & Kannan, G., 2015. A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network designs in large scale networks. Applied mathematical modelling.Taha, H., 2007. Operations research: An introduction. Octava ed. s.l.:Prentice Hall.Yalaoui, A. & Chehade, H., 2012. Optimization of logistics. s.l.:Wiley.Zanakins, S. &. E. J., 1981. Heuristic "Optimization": Why when and how to use it". 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