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Adaptive approach heuristics for the generalized assignment problem

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Abstract

The Generalized Assignment Problem consists in assigning a set of tasks to a set of agents with minimum cost. Each agent has a limited amount of a single resource and each task must be assigned to one and only one agent, requiring a certain amount of the resource of the agent. We present new metaheuristics for the generalized assignment problem based on hybrid approaches. One metaheuristic is a MAX-MIN Ant System (MMAS), an improved version of the Ant System, which was recently proposed by Stutzle and Hoos to combinatorial optimization problems, and it can be seen has an adaptive sampling algorithm that takes in consideration the experience gathered in earlier iterations of the algorithm. Moreover, the latter heuristic is combined with local search and tabu search heuristics to improve the search. A greedy randomized adaptive search heuristic (GRASP) is also proposed. Several neighborhoods are studied, including one based on ejection chains that produces good moves without increasing the computational effort. We present computational results of the comparative performance, followed by concluding remarks and ideas on future research in generalized assignment related problems.

Suggested Citation

  • Helena Ramalhinho-Lourenço & Daniel Serra, 1998. "Adaptive approach heuristics for the generalized assignment problem," Economics Working Papers 288, Department of Economics and Business, Universitat Pompeu Fabra.
  • Handle: RePEc:upf:upfgen:288
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    References listed on IDEAS

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    1. Laguna, Manuel & Kelly, James P. & Gonzalez-Velarde, JoseLuis & Glover, Fred, 1995. "Tabu search for the multilevel generalized assignment problem," European Journal of Operational Research, Elsevier, vol. 82(1), pages 176-189, April.
    2. Cattrysse, Dirk. G. & Salomon, Marc & Van Wassenhove, Luk N., 1994. "A set partitioning heuristic for the generalized assignment problem," European Journal of Operational Research, Elsevier, vol. 72(1), pages 167-174, January.
    3. Cattrysse, Dirk G. & Van Wassenhove, Luk N., 1992. "A survey of algorithms for the generalized assignment problem," European Journal of Operational Research, Elsevier, vol. 60(3), pages 260-272, August.
    4. Marshall L. Fisher & R. Jaikumar & Luk N. Van Wassenhove, 1986. "A Multiplier Adjustment Method for the Generalized Assignment Problem," Management Science, INFORMS, vol. 32(9), pages 1095-1103, September.
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    Cited by:

    1. Francisco Silva & Daniel Serra, 2008. "Incorporating waiting time in competitive location models: Formulations and heuristics," Economics Working Papers 1091, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Helena Ramalhinho-Lourenço & Rafael Martí & Manuel Laguna, 2001. "Assigning proctors to exams with scatter search," Economics Working Papers 534, Department of Economics and Business, Universitat Pompeu Fabra.

    More about this item

    Keywords

    Metaheuristics; generalized assignment; local search; GRASP; tabu search; ant systems;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • L80 - Industrial Organization - - Industry Studies: Services - - - General

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