An evolutionary programming approach for solving the capacitated facility location problem with risk pooling

A. Diabat, T. Aouam, L. Ozsen

Research output: Contribution to journalArticle

Abstract

In this paper, we propose a genetic algorithm as an alternative technique for solving the capacitated facility location problem with risk pooling (CLMRP). The CLMRP is a joint location-inventory problem involving a single supplier and multiple retailers that face stochastic demand. Due to the stochasticity of demand associated with each retailer, risk pooling may be achieved by allowing some retailers to serve as distribution centres (DCs). This is a combinatorial optimisation problem that has been shown to be NP-hard. A genetic algorithm that is computationally very efficient is developed to solve the problem. A computational experiment is conducted to test the performance of the developed technique and computational results are reported. The algorithm can easily find optimal or near optimal solutions for benchmark test problems from the literature, where the Lagrangian relaxation approach was used.

Original languageEnglish (US)
Pages (from-to)389-405
Number of pages17
JournalInternational Journal of Applied Decision Sciences
Volume2
Issue number4
DOIs
StatePublished - Jan 1 2009

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Keywords

  • Evolutionary programming
  • Genetic algorithms
  • Location-inventory problems
  • Meta-heuristics
  • Non-linear integer programming
  • Supply chain management

ASJC Scopus subject areas

  • Economics and Econometrics
  • Strategy and Management
  • Management Science and Operations Research
  • Information Systems and Management

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