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The Maximum Flow Problem with Minimum Lot Sizes

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 6971)

Abstract

In many transportation systems, the shipment quantities are subject to minimum lot sizes in addition to regular capacity constraints. That is, either the quantity must be zero, or it must be between the two bounds. In this work, we consider a directed graph, where a minimum lot size and a flow capacity are defined for each arc, and study the problem of maximizing the flow from a given source to a given terminal. We prove that the problem is NP-hard. Based on a straightforward mixed integer programming formulation, we develop a Lagrangean relaxation technique, and demonstrate how this can provide strong bounds on the maximum flow. For fast computation of near-optimal solutions, we develop a heuristic that departs from the zero solution and gradually augments the set of flow-carrying (open) arcs. The set of open arcs does not necessarily constitute a feasible solution. We point out how feasibility can be checked quickly by solving regular maximum flow problems in an extended network, and how the solutions to these subproblems can be productive in augmenting the set of open arcs. Finally, we present results from preliminary computational experiments with the construction heuristic.

Keywords

  • Setup Cost
  • Construction Heuristic
  • Residual Network
  • Residual Graph
  • Maximum Flow Problem

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Haugland, D., Eleyat, M., Hetland, M.L. (2011). The Maximum Flow Problem with Minimum Lot Sizes. In: Böse, J.W., Hu, H., Jahn, C., Shi, X., Stahlbock, R., Voß, S. (eds) Computational Logistics. ICCL 2011. Lecture Notes in Computer Science, vol 6971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24264-9_13

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  • DOI: https://doi.org/10.1007/978-3-642-24264-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24263-2

  • Online ISBN: 978-3-642-24264-9

  • eBook Packages: Computer ScienceComputer Science (R0)