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Resource allocation in dynamic PERT networks with finite capacity

Listed author(s):
  • Yaghoubi, Saeed
  • Noori, Siamak
  • Azaron, Amir
  • Tavakkoli-Moghaddam, Reza
Registered author(s):

    This article models the resource allocation problem in dynamic PERT networks with finite capacity of concurrent projects (COnstant Number of Projects In Process (CONPIP)), where activity durations are independent random variables with exponential distributions, and the new projects are generated according to a Poisson process. The system is represented as a queuing network with finite concurrent projects, where each activity of a project is performed at a devoted service station with one server located in a node of the network. For modeling dynamic PERT networks with CONPIP, we first convert the network of queues into a stochastic network. Then, by constructing a proper finite-state continuous-time Markov model, a system of differential equations is created to solve and find the completion time distribution for any particular project. Finally, we propose a multi-objective model with three conflict objectives to optimally control the resources allocated to the servers, and apply the goal attainment method to solve a discrete-time approximation of the original multi-objective problem.

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    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 215 (2011)
    Issue (Month): 3 (December)
    Pages: 670-678

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    Handle: RePEc:eee:ejores:v:215:y:2011:i:3:p:670-678
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    1. Linda Nozick & Mark Turnquist & Ningxiong Xu, 2004. "Managing Portfolios of Projects under Uncertainty," Annals of Operations Research, Springer, vol. 132(1), pages 243-256, November.
    2. Azaron, Amir & Tavakkoli-Moghaddam, Reza, 2007. "Multi-objective time-cost trade-off in dynamic PERT networks using an interactive approach," European Journal of Operational Research, Elsevier, vol. 180(3), pages 1186-1200, August.
    3. 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. Azaron, Amir & Katagiri, Hideki & Sakawa, Masatoshi & Kato, Kosuke & Memariani, Azizollah, 2006. "A multi-objective resource allocation problem in PERT networks," European Journal of Operational Research, Elsevier, vol. 172(3), pages 838-854, August.
    12. Amir Azaron & Hideki Katagiri & Masatoshi Sakawa, 2007. "Time-cost trade-off via optimal control theory in Markov PERT networks," Annals of Operations Research, Springer, vol. 150(1), pages 47-64, March.
    13. Jerome D. Wiest, 1967. "A Heuristic Model for Scheduling Large Projects with Limited Resources," Management Science, INFORMS, vol. 13(6), pages 359-377, February.
    14. Osorio, Carolina & Bierlaire, Michel, 2009. "An analytic finite capacity queueing network model capturing the propagation of congestion and blocking," European Journal of Operational Research, Elsevier, vol. 196(3), pages 996-1007, August.
    15. Jouini, Oualid & Dallery, Yves & Aksin, Zeynep, 2009. "Queueing models for full-flexible multi-class call centers with real-time anticipated delays," International Journal of Production Economics, Elsevier, vol. 120(2), pages 389-399, August.
    16. Lova, Antonio & Maroto, Concepcion & Tormos, Pilar, 2000. "A multicriteria heuristic method to improve resource allocation in multiproject scheduling," European Journal of Operational Research, Elsevier, vol. 127(2), pages 408-424, December.
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