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A Computational Study on Bounding the Makespan Distribution in Stochastic Project Networks

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  • Arfst Ludwig
  • Rolf Möhring
  • Frederik Stork

Abstract

Due to the practical importance of stochastic project networks (PERT-networks), many methods have been developed over the past decades in order to obtain information about the random project completion time. Of particular interest are methods that provide (lower and upper) bounds for its distribution, since these aim at balancing efficiency of calculation with accuracy of the obtained information. We provide a thorough computational evaluation of the most promising of these bounding algorithms with the aim to test their suitability for practical applications both in terms of efficiency and quality. To this end, we have implemented these algorithms and compare their behavior on a basis of nearly 2000 instances with up to 1200 activities of different test-sets. These implementations are based on a suitable numerical representation of distributions which is the basis for excellent computational results. Particularly a distribution-free heuristic based on the Central Limit Theorem provides an excellent tool to evaluate stochastic project networks. Copyright Kluwer Academic Publishers 2001

Suggested Citation

  • Arfst Ludwig & Rolf Möhring & Frederik Stork, 2001. "A Computational Study on Bounding the Makespan Distribution in Stochastic Project Networks," Annals of Operations Research, Springer, vol. 102(1), pages 49-64, February.
    • Handle: RePEc:spr:annopr:v:102:y:2001:i:1:p:49-64:10.1023/a:1010945830113
    DOI: 10.1023/A:1010945830113
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    Citations

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    Cited by:

    1. Mahtab Afsari & Hesam Javadi Vasigh, 2016. "A Mathematical Modeling for Delivery Time in Dynamic PERT Networks," Modern Applied Science, Canadian Center of Science and Education, vol. 10(3), pages 214-214, March.
    2. Selcuk Goren & Ihsan Sabuncuoglu & Utku Koc, 2012. "Optimization of schedule stability and efficiency under processing time variability and random machine breakdowns in a job shop environment," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(1), pages 26-38, February.
    3. Stephen P. Boyd & Seung-Jean Kim & Dinesh D. Patil & Mark A. Horowitz, 2005. "Digital Circuit Optimization via Geometric Programming," Operations Research, INFORMS, vol. 53(6), pages 899-932, December.
    4. Van de Vonder, Stijn & Demeulemeester, Erik & Herroelen, Willy & Leus, Roel, 2005. "The use of buffers in project management: The trade-off between stability and makespan," International Journal of Production Economics, Elsevier, vol. 97(2), pages 227-240, August.
    5. Nicole Megow & Rolf H. Möhring & Jens Schulz, 2011. "Decision Support and Optimization in Shutdown and Turnaround Scheduling," INFORMS Journal on Computing, INFORMS, vol. 23(2), pages 189-204, May.
    6. Creemers, Stefan & De Reyck, Bert & Leus, Roel, 2015. "Project planning with alternative technologies in uncertain environments," European Journal of Operational Research, Elsevier, vol. 242(2), pages 465-476.

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