A Novel Numerical Approach to the MCLP Based Resilent Supply Chain Optimization

This paper deals with the Maximal Covering Location Problem (MCLP) for Supply Chain optimization in the presence of incomplete information. A specific linear-integer structure of a generic mathematical model for Resilient Supply Chain Management System (RSCMS) makes it possible to reduce the origina...

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Tipo de recurso:
Fecha de publicación:
2016
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
eng
OAI Identifier:
oai:repository.udem.edu.co:11407/4379
Acceso en línea:
http://hdl.handle.net/11407/4379
Palabra clave:
Computational complexity
Integer programming
Supply chain management
Complexity of algorithm
Computational methodology
Equivalent transformations
Incomplete information
Maximal covering location problems (MCLP)
Numerical approaches
Supply chain management system
Supply chain optimization
Optimization
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oai_identifier_str oai:repository.udem.edu.co:11407/4379
network_acronym_str REPOUDEM2
network_name_str Repositorio UDEM
repository_id_str
dc.title.spa.fl_str_mv A Novel Numerical Approach to the MCLP Based Resilent Supply Chain Optimization
title A Novel Numerical Approach to the MCLP Based Resilent Supply Chain Optimization
spellingShingle A Novel Numerical Approach to the MCLP Based Resilent Supply Chain Optimization
Computational complexity
Integer programming
Supply chain management
Complexity of algorithm
Computational methodology
Equivalent transformations
Incomplete information
Maximal covering location problems (MCLP)
Numerical approaches
Supply chain management system
Supply chain optimization
Optimization
title_short A Novel Numerical Approach to the MCLP Based Resilent Supply Chain Optimization
title_full A Novel Numerical Approach to the MCLP Based Resilent Supply Chain Optimization
title_fullStr A Novel Numerical Approach to the MCLP Based Resilent Supply Chain Optimization
title_full_unstemmed A Novel Numerical Approach to the MCLP Based Resilent Supply Chain Optimization
title_sort A Novel Numerical Approach to the MCLP Based Resilent Supply Chain Optimization
dc.contributor.affiliation.spa.fl_str_mv Azhmyakov, V., Departamento de Ciencias Basicas, Universidad de Medellin, Medellin, Colombia
Fernández-Gutiérrez, J.P., Departamento de Ciencias Basicas, Universidad de Medellin, Medellin, Colombia
Gadi, S.K., Facultad de Ingenieria Mecanica y Electrica, Universidad Autonoma de Coahuila, Torreon, Mexico
Pickl, S., Department of Computer Science, Universität der Bundeswehr München, München, Germany
dc.subject.keyword.eng.fl_str_mv Computational complexity
Integer programming
Supply chain management
Complexity of algorithm
Computational methodology
Equivalent transformations
Incomplete information
Maximal covering location problems (MCLP)
Numerical approaches
Supply chain management system
Supply chain optimization
Optimization
topic Computational complexity
Integer programming
Supply chain management
Complexity of algorithm
Computational methodology
Equivalent transformations
Incomplete information
Maximal covering location problems (MCLP)
Numerical approaches
Supply chain management system
Supply chain optimization
Optimization
description This paper deals with the Maximal Covering Location Problem (MCLP) for Supply Chain optimization in the presence of incomplete information. A specific linear-integer structure of a generic mathematical model for Resilient Supply Chain Management System (RSCMS) makes it possible to reduce the originally given MCLP to two auxiliary optimization Knapsack-type problems. The equivalent transformation (separation) we propose provides a useful tool for an effective numerical treatment of the original MCLP and reduces the complexity of algorithms. The computational methodology we follow involves a specific Lagrange relaxation procedure. We give a rigorous formal analysis of the resulting algorithm and apply it to a practically oriented example of an optimal RSCMS design. © 2016
publishDate 2016
dc.date.created.none.fl_str_mv 2016
dc.date.accessioned.none.fl_str_mv 2017-12-19T19:36:52Z
dc.date.available.none.fl_str_mv 2017-12-19T19:36:52Z
dc.type.eng.fl_str_mv Article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.identifier.issn.none.fl_str_mv 24058963
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11407/4379
dc.identifier.doi.none.fl_str_mv 10.1016/j.ifacol.2016.12.175
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad de Medellín
dc.identifier.instname.spa.fl_str_mv instname:Universidad de Medellín
identifier_str_mv 24058963
10.1016/j.ifacol.2016.12.175
reponame:Repositorio Institucional Universidad de Medellín
instname:Universidad de Medellín
url http://hdl.handle.net/11407/4379
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.isversionof.spa.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012868634&doi=10.1016%2fj.ifacol.2016.12.175&partnerID=40&md5=26ac509f85a752096da4a6e849f29c78
dc.relation.ispartofes.spa.fl_str_mv IFAC-PapersOnLine
IFAC-PapersOnLine Volume 49, Issue 31, 2016, Pages 137-142
dc.relation.references.spa.fl_str_mv Alexandris, G., & Giannikos, I. (2010). A new model for maximal coverage exploiting GIS capabilities. European Journal of Operational Research, 202(2), 328-338. doi:10.1016/j.ejor.2009.05.037
Atkinson, K., & Han, W. (2001). Theoretical Numerical Analysis.
Aytug, H., & Saydam, C. (2002). Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study. European Journal of Operational Research, 141(3), 480-494. doi:10.1016/S0377-2217(01)00260-0
Azhmyakov, V., Basin, M., & Reincke-Collon, C. (2014). Optimal LQ-type switched control design for a class of linear systems with piecewise constant inputs. Paper presented at the IFAC Proceedings Volumes (IFAC-PapersOnline), , 19 6976-6981.
Azhmyakov, V., Cabrera, J., & Poznyak, A. (2016). Optimal fixed - levels control for non - linear systems with quadratic cost functionals.
Azhmyakov, V., & Juarez, R. (2015). On the projected gradient method for switched - mode systems optimization. In: Proceedings of the 5th IFAC Conference on Analysis and Design of Hybrid Systems.
Azhmyakov, V., & Schmidt, W. (2006). Approximations of relaxed optimal control problems. Journal of Optimization Theory and Applications, 130(1), 61-77. doi:10.1007/s10957-006-9085-9
Batanović, V., Petrović, D., & Petrović, R. (2009). Fuzzy logic based algorithms for maximum covering location problems. Information Sciences, 179(1-2), 120-129. doi:10.1016/j.ins.2008.08.019
Berman, O., Kalcsics, J., Krass, D., & Nickel, S. (2009). The ordered gradual covering location problem on a network. Discrete Applied Mathematics, 157(18), 3689-3707. doi:10.1016/j.dam.2009.08.003
Berman, O., & Wang, J. (2011). The minmax regret gradual covering location problem on a network with incomplete information of demand weights. European Journal of Operational Research, 208(3), 233-238. doi:10.1016/j.ejor.2010.08.016
Bertsekas, D. P. (1995). Nonlinear Programming.
Canbolat, M. S., & Massow, M. v. (2009). Planar maximal covering with ellipses. Computers and Industrial Engineering, 57(1), 201-208. doi:10.1016/j.cie.2008.11.015
Church, R., & ReVelle, C. (1974). The maximal covering location problem. Papers of the Regional Science Association, 32(1), 101-118. doi:10.1007/BF01942293
Galvão, R. D., Espejo, L. G. A., & Boffey, B. (2000). A comparison of lagrangean and surrogate relaxations for the maximal covering location problem. European Journal of Operational Research, 124(2), 377-389.
Ghiani, G., Laporte, G., & Musmanno, R. (2004). Introduction to Logistics Systems Planning and Control.
Ji, G., & Han, S. (2014). A strategy analysis in dual-channel supply chain based on effort levels. Proceedings of the 1th International Conference on Service Systems and Service Management.
Kellerer, H., Pferschy, U., & Pisinger, D. (2004). Knapsack Problems
Mitsos, A., Chachuat, B., & Barton, P. I. (2009). Mccormick-based relaxations of algorithms. SIAM Journal on Optimization, 20(2), 573-601. doi:10.1137/080717341
Moore, G. C., & ReVelle, C. (1982). The hierarchical service location problem. Management Science, 28(7), 775-780.
Polak, E. (1997). Optimization.
ReVelle, C., Scholssberg, M., & Williams, J. (2008). Solving the maximal covering location problem with heuristic concentration. Computers and Operations Research, 35(2), 427-435. doi:10.1016/j.cor.2006.03.007
Roubíček, T. (1997). Relaxation in Optimization Theory and Variational Calculus.
Shavandi, H., & Mahlooji, H. (2006). A fuzzy queuing location model with a genetic algorithm for congested systems. Applied Mathematics and Computation, 181(1), 440-456. doi:10.1016/j.amc.2005.12.058
Sitek, P., & Wikarek, J. (2013). A hybrid approach to modeling and optimization for supply chain management with multimodal transport. Paper presented at the 2013 18th International Conference on Methods and Models in Automation and Robotics, MMAR 2013, 777-782.
Zarandi, M. H. F., Sisakht, A. H., & Davari, S. (2011). Design of a closed-loop supply chain (CLSC) model using an interactive fuzzy goal programming. International Journal of Advanced Manufacturing Technology, 56(5-8), 809-821. doi:10.1007/s00170-011-3212-y
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
rights_invalid_str_mv http://purl.org/coar/access_right/c_16ec
dc.publisher.spa.fl_str_mv Elsevier B.V.
dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias Básicas
dc.source.spa.fl_str_mv Scopus
institution Universidad de Medellín
repository.name.fl_str_mv Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv repositorio@udem.edu.co
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spelling 2017-12-19T19:36:52Z2017-12-19T19:36:52Z201624058963http://hdl.handle.net/11407/437910.1016/j.ifacol.2016.12.175reponame:Repositorio Institucional Universidad de Medellíninstname:Universidad de MedellínThis paper deals with the Maximal Covering Location Problem (MCLP) for Supply Chain optimization in the presence of incomplete information. A specific linear-integer structure of a generic mathematical model for Resilient Supply Chain Management System (RSCMS) makes it possible to reduce the originally given MCLP to two auxiliary optimization Knapsack-type problems. The equivalent transformation (separation) we propose provides a useful tool for an effective numerical treatment of the original MCLP and reduces the complexity of algorithms. The computational methodology we follow involves a specific Lagrange relaxation procedure. We give a rigorous formal analysis of the resulting algorithm and apply it to a practically oriented example of an optimal RSCMS design. © 2016engElsevier B.V.Facultad de Ciencias Básicashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85012868634&doi=10.1016%2fj.ifacol.2016.12.175&partnerID=40&md5=26ac509f85a752096da4a6e849f29c78IFAC-PapersOnLineIFAC-PapersOnLine Volume 49, Issue 31, 2016, Pages 137-142Alexandris, G., & Giannikos, I. (2010). A new model for maximal coverage exploiting GIS capabilities. European Journal of Operational Research, 202(2), 328-338. doi:10.1016/j.ejor.2009.05.037Atkinson, K., & Han, W. (2001). Theoretical Numerical Analysis.Aytug, H., & Saydam, C. (2002). Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study. European Journal of Operational Research, 141(3), 480-494. doi:10.1016/S0377-2217(01)00260-0Azhmyakov, V., Basin, M., & Reincke-Collon, C. (2014). Optimal LQ-type switched control design for a class of linear systems with piecewise constant inputs. Paper presented at the IFAC Proceedings Volumes (IFAC-PapersOnline), , 19 6976-6981.Azhmyakov, V., Cabrera, J., & Poznyak, A. (2016). Optimal fixed - levels control for non - linear systems with quadratic cost functionals.Azhmyakov, V., & Juarez, R. (2015). On the projected gradient method for switched - mode systems optimization. In: Proceedings of the 5th IFAC Conference on Analysis and Design of Hybrid Systems.Azhmyakov, V., & Schmidt, W. (2006). Approximations of relaxed optimal control problems. Journal of Optimization Theory and Applications, 130(1), 61-77. doi:10.1007/s10957-006-9085-9Batanović, V., Petrović, D., & Petrović, R. (2009). Fuzzy logic based algorithms for maximum covering location problems. Information Sciences, 179(1-2), 120-129. doi:10.1016/j.ins.2008.08.019Berman, O., Kalcsics, J., Krass, D., & Nickel, S. (2009). The ordered gradual covering location problem on a network. Discrete Applied Mathematics, 157(18), 3689-3707. doi:10.1016/j.dam.2009.08.003Berman, O., & Wang, J. (2011). The minmax regret gradual covering location problem on a network with incomplete information of demand weights. European Journal of Operational Research, 208(3), 233-238. doi:10.1016/j.ejor.2010.08.016Bertsekas, D. P. (1995). Nonlinear Programming.Canbolat, M. S., & Massow, M. v. (2009). Planar maximal covering with ellipses. Computers and Industrial Engineering, 57(1), 201-208. doi:10.1016/j.cie.2008.11.015Church, R., & ReVelle, C. (1974). The maximal covering location problem. Papers of the Regional Science Association, 32(1), 101-118. doi:10.1007/BF01942293Galvão, R. D., Espejo, L. G. A., & Boffey, B. (2000). A comparison of lagrangean and surrogate relaxations for the maximal covering location problem. European Journal of Operational Research, 124(2), 377-389.Ghiani, G., Laporte, G., & Musmanno, R. (2004). Introduction to Logistics Systems Planning and Control.Ji, G., & Han, S. (2014). A strategy analysis in dual-channel supply chain based on effort levels. Proceedings of the 1th International Conference on Service Systems and Service Management.Kellerer, H., Pferschy, U., & Pisinger, D. (2004). Knapsack ProblemsMitsos, A., Chachuat, B., & Barton, P. I. (2009). Mccormick-based relaxations of algorithms. SIAM Journal on Optimization, 20(2), 573-601. doi:10.1137/080717341Moore, G. C., & ReVelle, C. (1982). The hierarchical service location problem. Management Science, 28(7), 775-780.Polak, E. (1997). Optimization.ReVelle, C., Scholssberg, M., & Williams, J. (2008). Solving the maximal covering location problem with heuristic concentration. Computers and Operations Research, 35(2), 427-435. doi:10.1016/j.cor.2006.03.007Roubíček, T. (1997). Relaxation in Optimization Theory and Variational Calculus.Shavandi, H., & Mahlooji, H. (2006). A fuzzy queuing location model with a genetic algorithm for congested systems. Applied Mathematics and Computation, 181(1), 440-456. doi:10.1016/j.amc.2005.12.058Sitek, P., & Wikarek, J. (2013). A hybrid approach to modeling and optimization for supply chain management with multimodal transport. Paper presented at the 2013 18th International Conference on Methods and Models in Automation and Robotics, MMAR 2013, 777-782.Zarandi, M. H. F., Sisakht, A. H., & Davari, S. (2011). Design of a closed-loop supply chain (CLSC) model using an interactive fuzzy goal programming. International Journal of Advanced Manufacturing Technology, 56(5-8), 809-821. doi:10.1007/s00170-011-3212-yScopusA Novel Numerical Approach to the MCLP Based Resilent Supply Chain OptimizationArticleinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Azhmyakov, V., Departamento de Ciencias Basicas, Universidad de Medellin, Medellin, ColombiaFernández-Gutiérrez, J.P., Departamento de Ciencias Basicas, Universidad de Medellin, Medellin, ColombiaGadi, S.K., Facultad de Ingenieria Mecanica y Electrica, Universidad Autonoma de Coahuila, Torreon, MexicoPickl, S., Department of Computer Science, Universität der Bundeswehr München, München, GermanyAzhmyakov V.Fernández-Gutiérrez J.P.Gadi S.K.Pickl S.Departamento de Ciencias Basicas, Universidad de Medellin, Medellin, ColombiaFacultad de Ingenieria Mecanica y Electrica, Universidad Autonoma de Coahuila, Torreon, MexicoDepartment of Computer Science, Universität der Bundeswehr München, München, GermanyComputational complexityInteger programmingSupply chain managementComplexity of algorithmComputational methodologyEquivalent transformationsIncomplete informationMaximal covering location problems (MCLP)Numerical approachesSupply chain management systemSupply chain optimizationOptimizationThis paper deals with the Maximal Covering Location Problem (MCLP) for Supply Chain optimization in the presence of incomplete information. A specific linear-integer structure of a generic mathematical model for Resilient Supply Chain Management System (RSCMS) makes it possible to reduce the originally given MCLP to two auxiliary optimization Knapsack-type problems. The equivalent transformation (separation) we propose provides a useful tool for an effective numerical treatment of the original MCLP and reduces the complexity of algorithms. The computational methodology we follow involves a specific Lagrange relaxation procedure. We give a rigorous formal analysis of the resulting algorithm and apply it to a practically oriented example of an optimal RSCMS design. © 2016http://purl.org/coar/access_right/c_16ec11407/4379oai:repository.udem.edu.co:11407/43792020-05-27 16:26:20.944Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

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