• The Journal of the Korea Contents Association
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• v.10 no.9
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• pp.107-116
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• 2010

The facility location problem is one of the traditional optimization problems. In this paper, we deal with the single source capacitated facility location problem (SSCFLP) and it is known as an NP-hard problem. Thus, it seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the SSCFLP. This paper introduces a new efficient evolutionary algorithm for the SSCFLP. The proposed algorithm is devised by incorporating a general adaptive link adjustment evolutionary algorithm and three heuristic local search methods. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds optimum solutions at almost all middle size test instances and very stable solutions at larger size test instances.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.137-144
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• 2003

We consider Two-echelon Single source Capacitated Facility Location Problem (TSCFLP). TSCFLP is a variant or Capacitated Facility Location Problem (CFLP). which has been an important issue in boa academic and industrial aspects. Given a set or possible facility locations in two echelons (warehouse / plant), a set or customers, TSCFLP is a decision problem to find a set or facility locations to open and to determine an allocation schedule that satisfies the demands or the customers and the capacity constraints or the facilities, while minimizing the overall cost. It ran be shown that TSCFLP Is strongly NP-hard For TSCFLf, few algorithms are known. which are heuristics. We propose a disaggregated version or the standard mixed integer programming formulation or TSCFLP We also provide a class or valid Inequalities Branch-and-price algorithm with rutting plane method Is used to find an optimal solution Efficient branching strategy compatible with subproblem optimization problems Is also provided. We report computational results or tests on 15 randomly generated instances.