A RAMP Algorithm for Large-Scale Single Source Capacitated Facility Location Problems

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Oliveira, O (Autor) Outra; Matos, T (Autor) Outra; Gamboa, D (Autor) Outra

Ata de Conferência Internacional

Título: LEARNING AND INTELLIGENT OPTIMIZATION, LION

Páginas: 171-183

13th International Conference on Learning and Intelligent Optimization (LION)

Chania, GREECE, MAY 27-31, 2019

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00R-KEH

DOI: 10.1007/978-3-030-38629-0_14

Abstract (EN): We propose a Relaxation Adaptive Memory Programming (RAMP) algorithm for the solution of the Single Source Capacitated Facility Location Problem (SSCFLP). This problem considers a set of possible locations for opening facilities and a set of clients whose demand must be satisfied. The objective is to minimize the cost of assigning the clients to the facilities, ensuring that all clients are served by only one facility without exceeding the capacity of the facilities. The RAMP framework efficiently explores the relation between the primal and the dual sides of combinatorial optimization problems. In our approach, the dual problem, obtained through a lagrangean relaxation, is solved by subgradient optimization. Computational experiments of the effectiveness of this approach are presented and discussed.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13