Stochastic rollout and justification to solve the resource-constrained project scheduling problem

  • Authors:

  • Ningxiong Xu;Linda Nozick;Orr Bernstein;Dean Jones

  • Affiliations:

  • Cornell University, Ithaca, N.Y.;Cornell University, Ithaca, N.Y.;Sandia National Laboratories, Alburquerque, NM;Sandia National Laboratories, Alburquerque, NM

  • Venue:
  • Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
  • Year:
  • 2007

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Abstract

The key question addressed by the resource-constrained project scheduling problem (RCPSP) is to determine the start times for each activity such that precedence and resource constraints are satisfied while achieving some objective. Priority rule-based heuristics are widely used for large problems and more recently justification has been shown to be an important extension. Xu et al. further augments priority rule heuristics by creating rollout procedures and proves their effectiveness. However, that procedure generates just one schedule. We extend that method using sampling to generate a set of schedules using probabilistic techniques and select the best schedule from this sample. Using the 600 problem instances in PSLIB, we present empirical evidence that this procedure produces solutions that are better than the rollout procedure alone but at a computational cost.