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The generalized assignment problem with minimum quantities

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  • Krumke, Sven O.
  • Thielen, Clemens

Abstract

We consider a variant of the generalized assignment problem (GAP) where the amount of space used in each bin is restricted to be either zero (if the bin is not opened) or above a given lower bound (a minimum quantity). We provide several complexity results for different versions of the problem and give polynomial time exact algorithms and approximation algorithms for restricted cases. For the most general version of the problem, we show that it does not admit a polynomial time approximation algorithm (unless P=NP), even for the case of a single bin. This motivates to study dual approximation algorithms that compute solutions violating the bin capacities and minimum quantities by a constant factor. When the number of bins is fixed and the minimum quantity of each bin is at least a factor δ>1 larger than the largest size of an item in the bin, we show how to obtain a polynomial time dual approximation algorithm that computes a solution violating the minimum quantities and bin capacities by at most a factor 1-1δ and 1+1δ, respectively, and whose profit is at least as large as the profit of the best solution that satisfies the minimum quantities and bin capacities strictly. In particular, for δ=2, we obtain a polynomial time (1,2)-approximation algorithm.

Suggested Citation

  • Krumke, Sven O. & Thielen, Clemens, 2013. "The generalized assignment problem with minimum quantities," European Journal of Operational Research, Elsevier, vol. 228(1), pages 46-55.
  • Handle: RePEc:eee:ejores:v:228:y:2013:i:1:p:46-55
    DOI: 10.1016/j.ejor.2013.01.027
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    References listed on IDEAS

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    1. Hans Georg Seedig, 2011. "Network Flow Optimization with Minimum Quantities," Operations Research Proceedings, in: Bo Hu & Karl Morasch & Stefan Pickl & Markus Siegle (ed.), Operations Research Proceedings 2010, pages 295-300, Springer.
    2. 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.
    3. Pentico, David W., 2007. "Assignment problems: A golden anniversary survey," European Journal of Operational Research, Elsevier, vol. 176(2), pages 774-793, January.
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    Cited by:

    1. Andreas Kleine & Andreas Dellnitz, 2017. "Allocation of seminar applicants," Journal of Business Economics, Springer, vol. 87(7), pages 927-941, October.
    2. Andreas Dellnitz & Damian Pozo & Jochen Bauer & Andreas Kleine, 2023. "Practice Summary: Seminar Assignments in a University—MATLAB-Based Decision Support," Interfaces, INFORMS, vol. 53(4), pages 307-311, July.
    3. Fanrong Xie & Anuj Sharma & Zuoan Li, 2022. "An alternate approach to solve two-level priority based assignment problem," Computational Optimization and Applications, Springer, vol. 81(2), pages 613-656, March.
    4. Prabhjot Kaur & Kalpana Dahiya & Vanita Verma, 2021. "Time-cost trade-off analysis of a priority based assignment problem," OPSEARCH, Springer;Operational Research Society of India, vol. 58(2), pages 448-482, June.
    5. Wang, Dian & Zhao, Jun & Peng, Qiyuan, 2022. "Optimizing the loaded train combination problem at a heavy-haul marshalling station," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).

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