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Resource allocation in stochastic, finite-capacity, multi-project systems through the cross entropy methodology

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Abstract

This paper addresses the problem of resource allocation in a finite-capacity, stochastic (random) and dynamic multi-project system. The system is modeled as a queuing network that is controlled by limiting the number of concurrent projects. We propose a Cross Entropy (CE) based approach to determine near-optimal resource allocations to the entities that execute the projects. The performance of the suggested approach is demonstrated through numerical experiments and compared to that of a heuristic, rough-cut based method.

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References

  • Adler, P. S., Mandelbaum, A., Nguyen, V., & Schwerer, E. (1995). From project to process management: an empirically-based framework for analyzing product development time. Management Science, 41(3), 458–484.

    Google Scholar 

  • Anavi-Isakow, S., & Golany, B. (2003). Managing multi-project environments through constant work-in-process. International Journal of Project Management, 21(1), 9–18.

    Article  Google Scholar 

  • Cohen, I., Mandelbaum, A., & Shtub, A. (2004). Multi-project scheduling and control: A process-based comparative study of the critical chain methodology and some alternatives. Project Management Journal, 35(2), 39–50.

    Google Scholar 

  • Cohen, I., Golany, B., & Shtub, A. (2005). Managing stochastic, finite-capacity, multi-project systems through the Cross Entropy methodology. Annals of Operations Research, 134, 183–199.

    Article  Google Scholar 

  • Gemmill, D. D., & Edwards, M. L. (1999). Improving resource-constrained project schedules with look-ahead techniques. Project Management Journal, 30(3), 44–55.

    Google Scholar 

  • Griffin, A. (2002). Product development cycle time for business-to-business products. Industrial Marketing Management, 31, 291–304.

    Article  Google Scholar 

  • Hopp, W. J., & Spearman, M. L. (1996). Factory physics—foundations of manufacturing management. Boston: Irwin.

    Google Scholar 

  • Kapur, J. N., & Kesavan, H. K. (1992). Entropy optimization principles with applications. New York: Academic.

    Google Scholar 

  • Kropp, D. H., & Carlson, R. C. (1984). A lot-sizing algorithm for reducing nervousness in MRP systems. Management Science, 30, 240–244.

    Article  Google Scholar 

  • Kurtulus, I., & Davis, E. W. (1982). Multi-project scheduling: Categorization of heuristic rules performance. Management Science, 28(2), 161–172.

    Google Scholar 

  • Law, A. M., & Kelton, W. D. (1991). Simulation modeling and analysis. New York: McGraw–Hill.

    Google Scholar 

  • Lee, B., & Miler, J. (2004). Multi-project management in software engineering using simulation modeling. Software Quality Journal, 12(1), 59–82.

    Article  Google Scholar 

  • Levy, N., & Globerson, S. (1997). Improving multiproject management by using a queuing theory approach. Project Management Journal, 28(4), 40–46.

    Google Scholar 

  • Leung, H. K. N. (2002). Estimating maintenance effort by analogy. Empirical Software Engineering, 7, 157–175.

    Article  Google Scholar 

  • Martien, H. A., Hendricks, B. V. & Leon, H. K. (2002). Human resource allocation in a multiproject research and development environment. In: J. S. Pennypacker, L. D. Dye (Eds.), Managing multiple projects (pp. 249–262). New York: Marcel Dekker.

    Google Scholar 

  • Rubinstein, R. Y. (1997). Optimization of computer simulation models with rare events. European Journal of Operations Research, 99, 89–112.

    Article  Google Scholar 

  • Rubinstein, R. Y., & Kroese, D. P. (2004). The cross entropy method: A unified approach to Monte Carlo simulation, randomized optimization and machine learning. Berlin: Springer.

    Google Scholar 

  • Rubinstein, R., & Melamed, B. (1998). Modern simulation and modeling. Berlin: Wiley.

    Google Scholar 

  • Speranza, M. G., & Vercellis, C. (1993). Hierarchical models for multi-project planning and scheduling. European Journal of Operational Research, 64, 312–325.

    Article  Google Scholar 

Download references

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Correspondence to Boaz Golany.

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Cohen, I., Golany, B. & Shtub, A. Resource allocation in stochastic, finite-capacity, multi-project systems through the cross entropy methodology. J Sched 10, 181–193 (2007). https://doi.org/10.1007/s10951-007-0013-0

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  • DOI: https://doi.org/10.1007/s10951-007-0013-0

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