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Allocation search methods for a generalized class of location-allocation problems


  • Bischoff, Martin
  • Dächert, Kerstin


We consider a generalized class of location-allocation problems, in which N new facilities are to be located in the plane with respect to M objects. Each object is associated with a convex cost function, specifying the expenses for serving the object from any location in the plane. For the resulting multi-dimensional mixed-integer optimization problem, we compare various traditional and new search methods. In particular, we apply multi-start, (variable) neighborhood search, tabu search, simulated annealing, an evolutionary algorithm and an ant colony optimization algorithm. They all have in common that they use the well-known alternate location and allocation algorithm [Cooper, L., 1964. Heuristic methods for location-allocation problems. SIAM Review 6, 37-53] as core local search function. We intend to impart a generalized view on these randomized search methods and also examine the efficiency of the different search strategies in solving the multi-connection location-allocation problem, a relatively new instance of the generalized class of location-allocation problems. Computational results show that the most crucial feature of the heuristics is the ability to combine a diversified search over the whole solution space with an intensified search near the best-known solution.

Suggested Citation

  • Bischoff, Martin & Dächert, Kerstin, 2009. "Allocation search methods for a generalized class of location-allocation problems," European Journal of Operational Research, Elsevier, vol. 192(3), pages 793-807, February.
  • Handle: RePEc:eee:ejores:v:192:y:2009:i:3:p:793-807

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    References listed on IDEAS

    1. Rosing, K. E., 1992. "An optimal method for solving the (generalized) multi-Weber problem," European Journal of Operational Research, Elsevier, vol. 58(3), pages 414-426, May.
    2. Bischoff, M. & Klamroth, K., 2007. "An efficient solution method for Weber problems with barriers based on genetic algorithms," European Journal of Operational Research, Elsevier, vol. 177(1), pages 22-41, February.
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