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A Random Activity Network Generator

Author

Listed:
  • E. Demeulemeester

    (Catholic University of Leuven, Leuven, Belgium)

  • B. Dodin

    (University of California, Riverside, California)

  • W. Herroelen

    (Catholic University of Leuven, Leuven, Belgium)

Abstract

Exact and heuristic procedures are often developed to obtain optimal and near-optimal solutions to decision problems modeled as activity networks. Testing the accuracy and efficiency of these procedures requires the use of activity networks with various sizes, structures, and parameters. The size of the network is determined by its number of nodes and arcs, where the structure is chosen from the set of all structures for the specified network size. The network parameters depend on the nature of the decision problem. Often, it is desirable for test problems to be generated at random from the space of all feasible networks. This paper deals with the problem of generating the size and structure of the network at random from the space of all feasible networks. It develops a theory which guarantees the randomness of the network structure. The theory is the basis for two methods. One can be used to generate dense networks, where the other is used to generate nondense networks. The methods, which are practical and easy to use, have been programmed for use on mainframe or personal computers. CPU time requirements are negligible. Copies of the computer program can be obtained from the authors.

Suggested Citation

  • E. Demeulemeester & B. Dodin & W. Herroelen, 1993. "A Random Activity Network Generator," Operations Research, INFORMS, vol. 41(5), pages 972-980, October.
    • Handle: RePEc:inm:oropre:v:41:y:1993:i:5:p:972-980
    DOI: 10.1287/opre.41.5.972
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    Citations

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    Cited by:

    1. Kolisch, R. & Padman, R., 2001. "An integrated survey of deterministic project scheduling," Omega, Elsevier, vol. 29(3), pages 249-272, June.
    2. De Reyck, Bert & Herroelen, Willy, 1996. "On the use of the complexity index as a measure of complexity in activity networks," European Journal of Operational Research, Elsevier, vol. 91(2), pages 347-366, June.
    3. Drexl, Andreas & Nissen, Rudiger & Patterson, James H. & Salewski, Frank, 2000. "ProGen/[pi]x - An instance generator for resource-constrained project scheduling problems with partially renewable resources and further extensions," European Journal of Operational Research, Elsevier, vol. 125(1), pages 59-72, August.
    4. Vanhoucke, Mario & Coelho, Jose & Debels, Dieter & Maenhout, Broos & Tavares, Luis V., 2008. "An evaluation of the adequacy of project network generators with systematically sampled networks," European Journal of Operational Research, Elsevier, vol. 187(2), pages 511-524, June.
    5. Van Eynde, Rob & Vanhoucke, Mario, 2022. "New summary measures and datasets for the multi-project scheduling problem," European Journal of Operational Research, Elsevier, vol. 299(3), pages 853-868.
    6. Mori, Masao & Tseng, Ching Chih, 1997. "A genetic algorithm for multi-mode resource constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 100(1), pages 134-141, July.
    7. Dayal Madhukar & Verma, Sanjay, 2015. "Multi-processor Exact Procedures for Regular Measures of the Multi-mode RCPSP," IIMA Working Papers WP2015-03-25, Indian Institute of Management Ahmedabad, Research and Publication Department.
    8. Nicholas G. Hall & Marc E. Posner, 2001. "Generating Experimental Data for Computational Testing with Machine Scheduling Applications," Operations Research, INFORMS, vol. 49(6), pages 854-865, December.
    9. Rummel, Jeffrey L. & Walter, Zhiping & Dewan, Rajiv & Seidmann, Abraham, 2005. "Activity consolidation to improve responsiveness," European Journal of Operational Research, Elsevier, vol. 161(3), pages 683-703, March.
    10. Wolfram Wiesemann & Daniel Kuhn & Berç Rustem, 2012. "Multi-resource allocation in stochastic project scheduling," Annals of Operations Research, Springer, vol. 193(1), pages 193-220, March.
    11. Tavares, L. V., 2002. "A review of the contribution of Operational Research to Project Management," European Journal of Operational Research, Elsevier, vol. 136(1), pages 1-18, January.
    12. Dodin, B. & Elimam, A.A., 2008. "Integration of equipment planning and project scheduling," European Journal of Operational Research, Elsevier, vol. 184(3), pages 962-980, February.
    13. Bowers, J., 1996. "Identifying critical activities in stochastic resource constrained networks," Omega, Elsevier, vol. 24(1), pages 37-46, February.
    14. Weglarz, Jan & Józefowska, Joanna & Mika, Marek & Waligóra, Grzegorz, 2011. "Project scheduling with finite or infinite number of activity processing modes - A survey," European Journal of Operational Research, Elsevier, vol. 208(3), pages 177-205, February.
    15. Agrawal, M. K. & Elmaghraby, S. E. & Herroelen, W. S., 1996. ": A generator of testsets for project activity nets," European Journal of Operational Research, Elsevier, vol. 90(2), pages 376-382, April.
    16. Demeulemeester, Erik L. & Herroelen, Willy S. & Elmaghraby, Salah E., 1996. "Optimal procedures for the discrete time/cost trade-off problem in project networks," European Journal of Operational Research, Elsevier, vol. 88(1), pages 50-68, January.
    17. Yan, Shangyao & Luo, So-Chang, 1999. "Probabilistic local search algorithms for concave cost transportation network problems," European Journal of Operational Research, Elsevier, vol. 117(3), pages 511-521, September.
    18. Yang, Kum-Khiong, 1998. "A comparison of dispatching rules for executing a resource-constrained project with estimated activity durations," Omega, Elsevier, vol. 26(6), pages 729-738, December.

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