Recourse-based facility-location problems in hybrid uncertain environment

  • Authors:

  • Shuming Wang;Junzo Watada;Witold Pedrycz

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
  • Year:
  • 2010

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Abstract

The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search.