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Monte Carlo bounding techniques for determining solution quality in stochastic programs
It is shown that, in expectation, z^*"n is a lower bound on z* and that this bound monotonically improves as n increases, and confidence intervals are constructed on the optimality gap for any candidate solution x@^ to SP. Expand
Assessing solution quality in stochastic programs
  • D. Morton, G. Bayraksan
  • Mathematics, Computer Science
  • Algorithms for Optimization with Incomplete…
  • 1 September 2006
This paper develops Monte Carlo sampling-based procedures for assessing solution quality in stochastic programs and proposes using ɛ-optimal solutions to strengthen the performance of these procedures. Expand
Stochastic Network Interdiction
A stochastic version of the interdictor's problem: Minimize the expected maximum flow through the network when interdiction successes are binary random variables is formulated and solved. Expand
Cut sharing for multistage stochastic linear programs with interstage dependency
Methodology for sharing cuts in decomposition algorithms for stochastic programs that satisfy certain interstage dependency models enable sampling-based algorithms to handle a richer class of multistage problems, and may also be used to accelerate the convergence of exact decompose algorithms. Expand
A Sequential Sampling Procedure for Stochastic Programming
A sequential sampling procedure for a class of stochastic programs that estimates the optimality gap of a candidate solution from a sequence of feasible solutions generated by solving a series of sampling problems with increasing sample size. Expand
Stochastic Vehicle Routing with Random Travel Times
This work considers stochastic vehicle routing problems on a network with random travel and service times and provides bounds on optimal objective function values and conditions under which reductions to simpler models can be made. Expand
Models for nuclear smuggling interdiction
We describe two stochastic network interdiction models for thwarting nuclear smuggling. In the first model, the smuggler travels through a transportation network on a path that maximizes theExpand
Assignment and Allocation Optimization of Partially Multiskilled Workforce
Multiskilling is a workforce strategy that has been shown to reduce indirect labor costs, improve productivity, and reduce turnover. A multiskilled workforce is one in which the workers possess aExpand
Optimizing Military Airlift
A large-scale linear programming model for optimizing strategic (intercontinental) airlift capability and analyses for the U.S. Air Force system concerning fleet modernization and concerning the allocation of resources that affect the processing capacity of airfields are described. Expand
Workforce planning at USPS mail processing and distribution centers using stochastic optimization
The results indicate that significant savings are likely when the recourse problem is used to help structure the workforce and a large-scale integer program that embodies the full set of contractual agreements and labor rules governing the design of the workforce at a P&DC. Expand