A separation method for maximal covering location problems with fuzzy parameters

Our paper discusses a novel computational approach to the extended Maximal Covering Location Problem (MCLP). We consider a fuzzy-type formulation of the generic MCLP and develop the necessary theoretical and numerical aspects of the proposed Separation Method (SM). A speciffic structure of the originally given MCLP makes it possible to reduce it to two auxiliary Knapsack-type problems. The equivalent separation we propose reduces essentially the complexity of the resulting computational algorithms. This algorithm also incorporates a conventional relaxation technique and the scalarizing method applied to an auxiliary multiobjective optimization problem. The proposed solution methodology is next applied to Supply Chain optimization in the presence of incomplete information. We study two illustrative examples and give a rigorous analysis of the obtained results.

• Fecha de publicación: 2017
• Institución: Universidad de Medellín
• Repositorio: Repositorio UDEM
• Idioma: eng
• OAI Identifier: oai:repository.udem.edu.co:11407/4286
• Acceso en línea: http://hdl.handle.net/11407/4286
• Palabra clave: Integer optimization; MCLP; Numerical optimization; Integer programming; Multiobjective optimization; Numerical methods; Separation; Site selection; Supply chains; Computational algorithm; Integer optimization; Maximal covering location problems; Maximal covering location problems (MCLP); MCLP; Multi-objective optimization problem; Numerical optimizations; Supply chain optimization; Optimization
• License: http://purl.org/coar/access_right/c_16ec