Expectation and Chance-Constrained Models and Algorithms for Insuring Critical Paths

Published Online:https://doi.org/10.1287/mnsc.1100.1208

References

  • Benders J. F. Partitioning procedures for solving mixed variables programming problems. Numerische Mathematik (1962) 4(1):238–252CrossrefGoogle Scholar
  • Bertsimas D., Natarajan K., Teo C.-P. Persistence in discrete optimization under data uncertainty. Math. Programming, Ser. B (2006) 108(2–3):251–274CrossrefGoogle Scholar
  • Bowman R. A. Efficient estimation of arc criticalities in stochastic activity networks. Management Sci. (1995) 41(1):58–67LinkGoogle Scholar
  • Bowman R. A., Muckstadt J. A. Stochastic analysis of cyclic schedules. Oper. Res. (1993) 41(5):947–958LinkGoogle Scholar
  • Brucker P., Drexl A., Moehring R., Neumann K., Pesch E. Resource-constrained project scheduling: Notation, classification, models, and methods. Eur. J. Oper. Res. (1999) 112(1):3–41CrossrefGoogle Scholar
  • Burt J. M., Garman M. B. Conditional Monte Carlo: A simulation technique for stochastic network analysis. Management Sci. (1971) 18(3):207–217LinkGoogle Scholar
  • Chen X., Sim M., Sun P., Zhang J. A linear-decision based approximation approach to stochastic programming. Oper. Res. (2008) 56(2):344–357LinkGoogle Scholar
  • Chtourou H., Haouari M. A two-stage-priority-rule-based algorithm for robust resource-constrained project scheduling. Comput. Indust. Engrg. (2008) 55(1):183–194CrossrefGoogle Scholar
  • Cormican K. J., Morton D. P., Wood R. K. Stochastic network interdiction. Oper. Res. (1998) 46(2):184–197LinkGoogle Scholar
  • Demeulemeester E. L., Herroelen W. S.Project Scheduling: A Research Handbook (2002) (Springer, Norwell, MA) Google Scholar
  • Elmaghraby S. E., Ferreira A. A., Tavares L. V. Optimal start times under stochastic activity durations. Internat. J. Production Econom. (2000) 64(1–3):153–164CrossrefGoogle Scholar
  • Goldratt E. M.Critical Chain (1997) (North River Press, Great Barrington, MA) Google Scholar
  • Golenko-Ginzburg D., Gonik A. A heuristic for network project scheduling with random activity durations depending on the resource allocation. Internat. J. Production Econom. (1998) 55(2):149–162CrossrefGoogle Scholar
  • Golenko-Ginzburg D., Gonik A., Sitniakovski S. Resource supportability model for stochastic network projects under a chance constraint. Comm. Dependability Quality Management (2000) 3(1):89–102Google Scholar
  • Gutjahr W. J., Strauss C., Wagner E. A stochastic branch-and-bound approach to activity crashing in project management. INFORMS J. Comput. (2000) 12(2):125–135LinkGoogle Scholar
  • Hagstrom J. N. Computing the probability distribution of project duration in a PERT network. Networks (1990) 20(2):231–244CrossrefGoogle Scholar
  • Herroelen W., Leus R. On the merits and pitfalls of critical chain scheduling. J. Oper. Management (2001) 19(5):559–577CrossrefGoogle Scholar
  • Herroelen W., Leus R. Project scheduling under uncertainty: Survey and research potentials. Eur. J. Oper. Res. (2005) 165(2):289–306CrossrefGoogle Scholar
  • Hindelang T. J., Muth J. F. A dynamic programming algorithm for decision CPM networks. Oper. Res. (1979) 27(2):225–241LinkGoogle Scholar
  • Iida T. Computing bounds on project duration distributions for stochastic PERT networks. Naval Res. Logist. (2000) 47(7):559–580CrossrefGoogle Scholar
  • ILOG CPLEX 11.0 User's Manual. (2008) (ILOG, Armonk, NY) Google Scholar
  • Janjarassuk U., Linderoth J. T. Reformulation and sampling to solve a stochastic network interdiction problem. Networks (2008) 52(3):120–132CrossrefGoogle Scholar
  • Kelley J. E. Critical-path planning and scheduling: Mathematical basis. Oper. Res. (1961) 9(3):296–320LinkGoogle Scholar
  • Kelley J. E. The critical path method: Resource planning and scheduling. Indust. Scheduling (1963) 2(1):347–365Google Scholar
  • Kleywegt A. J., Shapiro A., Homem-de-Mello T. The sample average approximation method for stochastic discrete optimization. SIAM J. Optim. (2001) 12(2):479–502CrossrefGoogle Scholar
  • Kulkarni V. G., Adlakha V. G. Markov and Markov-regenerative PERT networks. Oper. Res. (1986) 34(5):769–781LinkGoogle Scholar
  • Laslo Z. Activity time-cost tradeoffs under time and cost chance constraints. Comput. Indust. Engrg. (2003) 44(3):365–384CrossrefGoogle Scholar
  • Luedtke J., Ahmed S. A sample approximation approach for optimization with probabilistic constraints. SIAM J. Optim. (2008) 19(2):674–699CrossrefGoogle Scholar
  • Mak W. K., Morton D. P., Wood R. K. Monte Carlo bounding techniques for determining solution quality in stochastic programs. Oper. Res. Lett. (1999) 24(1):47–56CrossrefGoogle Scholar
  • Mitchell G., Klastorin T. An effective methodology for the stochastic project compression problem. IIE Trans. (2007) 39(10):957–969CrossrefGoogle Scholar
  • Moehring R. H. Minimizing costs of resource requirements in project networks subject to a fixed completion time. Oper. Res. (1984) 32(1):89–120LinkGoogle Scholar
  • Norkin V. I., Pflug G. Ch., Ruszczyński A. A branch-and-bound method for stochastic global optimization. Math. Programming (1998) 83(1–3):425–450CrossrefGoogle Scholar
  • Ozdamar L., Ulusoy G. A survey on the resource constrained project scheduling problem. IIE Trans. (1995) 27(5):574–586CrossrefGoogle Scholar
  • Patterson J. H. A comparison of exact procedures for solving the multiple constrained resource, project scheduling problem. Management Sci. (1984) 30(7):854–867LinkGoogle Scholar
  • Scholl A.Robuste Planung und Optimierung: Grundlagen, Konzepte, und Methoden (2001) (Physica-Verlag, Experimentelle Untersuchungen, Heidelberg, Germany) CrossrefGoogle Scholar
  • Schultz R. Stochastic programming with integer variables. Math. Programming (2003) 97(1–2):285–309CrossrefGoogle Scholar
  • Shapiro A., Homem-de-Mello T. On the rate of convergence of optimal solutions of Monte Carlo approximations of stochastic programs. SIAM J. Optim. (2000) 11(1):70–86CrossrefGoogle Scholar
  • Sherali H. D. On mixed-integer zero-one representations for separable lower-semicontinuous piecewise-linear functions. Oper. Res. Lett. (2001) 28(4):155–160CrossrefGoogle Scholar
  • Sherali H. D., Adams W. P. A hierarchy of relaxations between the continuous and convex hull representations for zero-one programming problems. SIAM J. Discrete Math. (1990) 3(3):411–430CrossrefGoogle Scholar
  • Sherali H. D., Adams W. P. A hierarchy of relaxations and convex hull characterizations for mixed-integer zero-one programming problems. Discrete Appl. Math. (1994) 52(1):83–106CrossrefGoogle Scholar
  • Sherali H. D., Fraticelli B. M. P. A modification of Benders' decomposition algorithm for discrete subproblems: An approach for stochastic programs with integer recourse. J. Global Optim. (2002) 22(1–4):319–342CrossrefGoogle Scholar
  • Sherali H. D., Smith J. C. Two-stage stochastic risk threshold and hierarchical multiple risk problems: Models and algorithms. Math. Programming, Ser. A (2009) 120(2):403–427CrossrefGoogle Scholar
  • Sherali H. D., Adams W. P., Driscoll P. J. Exploiting special structures in constructing a hierarchy of relaxations for 0-1 mixed integer problems. Oper. Res. (1998) 46(3):396–405LinkGoogle Scholar
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