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Hybrid biased random sampling for multiple resourse-constrained project scheduling problems

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  • Kolisch, Rainer
  • Drexl, Andreas

Abstract

In this paper we propose a new heuristic to solve the well-known multiple resource-constrained project scheduling problem. The method is basically a biased random sampling procedure which shows extremely good results by use of the following features: A problem-based selection of the solution space, a sample-size-based guidance of the search, application of a priority rule superior to so-far existing rules, and finally the application of global and local (lower) bounds. Evaluating this new heuristic on a set of widely used benchmark-instances we show that it derives superior results than all other existing polynomially bounded algorithms.

Suggested Citation

  • Kolisch, Rainer & Drexl, Andreas, 1994. "Hybrid biased random sampling for multiple resourse-constrained project scheduling problems," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 354, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    • Handle: RePEc:zbw:cauman:354
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    References listed on IDEAS

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    1. A. Alan B. Pritsker & Lawrence J. Waiters & Philip M. Wolfe, 1969. "Multiproject Scheduling with Limited Resources: A Zero-One Programming Approach," Management Science, INFORMS, vol. 16(1), pages 93-108, September.
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    11. Andreas Drexl, 1991. "Scheduling of Project Networks by Job Assignment," Management Science, INFORMS, vol. 37(12), pages 1590-1602, December.
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    Full references (including those not matched with items on IDEAS)

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