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Multi-resource allocation in stochastic project scheduling

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Abstract

We propose a resource allocation model for project scheduling. Our model accommodates multiple resources and decision-dependent activity durations inspired by microeconomic theory. First, we elaborate a deterministic problem formulation. In a second stage, we enhance this model to account for uncertain problem parameters. Assuming that the first and second moments of these parameters are known, the stochastic model minimises an approximation of the value-at-risk of the project makespan. As a salient feature, our approach employs a scenario-free formulation which is based on normal approximations of the activity path durations. We extend our model to situations in which the moments of the random parameters are ambiguous and describe an iterative solution procedure. Extensive numerical results are provided.

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References

  • Alizadeh, F., & Goldfarb, D. (2003). Second-order cone programming. Mathematical Programming, 95(1), 3–51.

    Article  Google Scholar 

  • Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), 203–228.

    Article  Google Scholar 

  • Bazaraa, M. S., Sherali, H. D., & Shetty, C. M. (2006). Nonlinear programming—theory and algorithms (3rd ed.). New York: Wiley.

    Book  Google Scholar 

  • Ben-Tal, A., & Nemirovski, A. (1998). Robust convex optimization. Mathematics of Operations Research, 23(4), 769–805.

    Article  Google Scholar 

  • Berk, K. N. (1973). A central limit theorem for m-dependent random variables with unbounded m. The Annals of Probability, 1(2), 352–354.

    Article  Google Scholar 

  • Bertsimas, D., & Sim, M. (2007). Robust conic optimization. Mathematical Programming, 107(1), 5–36.

    Article  Google Scholar 

  • Calafiore, G., & Campi, M. C. (2005). Uncertain convex programs: Randomized solutions and confidence levels. Mathematical Programming, 102(1), 25–46.

    Article  Google Scholar 

  • Calafiore, G., & Campi, M. C. (2006). The scenario approach to robust control design. IEEE Transactions on Automatic Control, 51(5), 742–753.

    Article  Google Scholar 

  • Chen, X., Sim, M., & Sun, P. (2007). A robust optimization perspective on stochastic programming. Operations Research, 55(6), 1058–1071.

    Article  Google Scholar 

  • Cohen, I., Golany, B., & Shtub, A. (2007). The stochastic time-cost tradeoff problem: A robust optimization approach. Networks, 49(2), 175–188.

    Article  Google Scholar 

  • Deckro, R. F., Hebert, J. E., Verdini, W. A., Grimsrud, P. H., & Venkateshwar, S. (1995). Nonlinear time/cost tradeoff models in project management. Computers & Industrial Engineering, 28(2), 219–229.

    Article  Google Scholar 

  • Demeulemeester, E. L., Dodin, B., & Herroelen, W. (1993). A random activity network generator. Operations Research, 41(5), 972–980.

    Article  Google Scholar 

  • Demeulemeester, E. L., & Herroelen, W. S. (2002). Project scheduling—a research handbook. Dordrecht: Kluwer Academic.

    Google Scholar 

  • Eppstein, D. (1994). Finding the k shortest paths. In IEEE symposium on foundations of computer science (pp. 154–165).

  • Erdoǧan, E., & Iyengar, G. (2007). On two-stage convex chance constrained problems. Mathematical Methods of Operations Research, 65(1), 115–140.

    Article  Google Scholar 

  • Fulkerson, D. R. (1961). A network flow computation for project cost curves. Management Science, 7(2), 167–178.

    Article  Google Scholar 

  • Goel, V., & Grossmann, I. E. (2006). A class of stochastic programs with decision dependent uncertainty. Mathematical Programming, 108(2–3), 355–394.

    Article  Google Scholar 

  • Henrion, R., & Strugarek, C. (2008). Convexity of chance constraints with independent random variables. Computational Optimization and Applications, 41(2), 263–276.

    Article  Google Scholar 

  • Herroelen, W. S., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research, 165(2), 289–306.

    Article  Google Scholar 

  • Horst, R., Pardalos, P. M., & Thoai, N. V. (2000). Introduction to global optimization (2nd ed.). Dordrecht: Kluwer Academic.

    Google Scholar 

  • Jain, A. S., & Meeran, S. (1999). Deterministic job-shop scheduling: Past, present and future. European Journal of Operational Research, 113(2), 390–434.

    Article  Google Scholar 

  • Jonsbraten, T. W., Wets, R. J.-B., & Woodruff, D. L. (1998). A class of stochastic programs with decision dependent random elements. Annals of Operations Research, 82, 83–106.

    Article  Google Scholar 

  • Jørgensen, T., & Wallace, S. W. (2000). Improving project cost estimation by taking into account managerial flexibility. European Journal of Operational Research, 127(2), 239–251.

    Article  Google Scholar 

  • Kelley, J. E. (1961). Critical-path planning and scheduling: Mathematical basis. Operations Research, 9(3), 296–320.

    Article  Google Scholar 

  • Kim, S.-J., Boyd, S. P., Yun, S., Patil, D. D., & Horowitz, M. A. (2007). A heuristic for optimizing stochastic activity networks with applications to statistical digital circuit sizing. Optimization and Engineering, 8(4), 397–430.

    Article  Google Scholar 

  • Korski, J., Pfeuffer, F., & Klamroth, K. (2007). Biconvex sets and optimization with biconvex functions: A survey and extensions. Mathematical Methods of Operations Research, 66(3), 373–407.

    Article  Google Scholar 

  • Luedtke, J., & Ahmed, S. (2008). A sample approximation approach for optimization with probabilistic constraints. SIAM Journal on Optimization, 19(2), 674–699.

    Article  Google Scholar 

  • Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.

    Article  Google Scholar 

  • Mas-Colell, A., Whinston, M. D., & Green, J. R. (1995). Microeconomic theory. London: Oxford University Press.

    Google Scholar 

  • Nemirovski, A., & Shapiro, A. (2006a). Convex approximations of chance constrained programs. SIAM Journal on Optimization, 17(4), 969–996.

    Article  Google Scholar 

  • Nemirovski, A., & Shapiro, A. (2006b). Scenario approximations of chance constraints. In G. Calafiore & F. Dabbene (Eds.), Probabilistic and randomized methods for design under uncertainty (pp. 3–47). Berlin: Springer.

    Chapter  Google Scholar 

  • Neumann, K. (1999). Scheduling of projects with stochastic evolution structure. In J. Weglarz (Ed.), Project scheduling: recent models, algorithms, and applications (pp. 309–332). Dordrecht: Kluwer Academic.

    Google Scholar 

  • Petrov, V. V. (1975). Sums of independent random variables. Berlin: Springer.

    Google Scholar 

  • Pflug, G. C., & Wozabal, D. (2007). Ambiguity in portfolio selection. Quantitative Finance, 7(4), 435–442.

    Article  Google Scholar 

  • Prékopa, A. (1995). Stochastic programming. Dordrecht: Kluwer Academic.

    Google Scholar 

  • Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2(3), 21–41.

    Google Scholar 

  • Wang, W., & Ahmed, S. (2007). Sample average approximation of expected value constrained stochastic programs. Operations Research Letters, 36(5), 515–519.

    Article  Google Scholar 

  • Zhu, S.-S., & Fukushima, M. (2006). Worst-case conditional value-at-risk with application to robust portfolio management (Working Paper).

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Correspondence to Wolfram Wiesemann.

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Wiesemann, W., Kuhn, D. & Rustem, B. Multi-resource allocation in stochastic project scheduling. Ann Oper Res 193, 193–220 (2012). https://doi.org/10.1007/s10479-008-0486-z

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