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 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

• 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
• License: http://purl.org/coar/access_right/c_16ec