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The Million-Variable “March” for Stochastic Combinatorial Optimization

By taking advantage of the loosely coupled structure, it is shown that decomposition-coordination methods provide highly effective algorithms, and surpass the scalability of even the most efficiently implemented backtracking search algorithms.
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Optimal Maintenance Strategies for Wind Turbine Systems Under Stochastic Weather Conditions

This work examines optimal repair strategies for wind turbines operated under stochastic weather conditions and derives a set of closed-form expressions for the optimal policy, and shows that it belongs to the class of monotonic four-region policies.
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Disjunctive Decomposition for Two-Stage Stochastic Mixed-Binary Programs with Random Recourse

This paper derives valid inequalities that allow for scenario subproblems for SIP with random recourse but deterministic technology matrix and right-hand side vector to share cut coefficients under a temporal decomposition setting of the SIP problem.
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DEVS-FIRE: design and application of formal discrete event wildfire spread and suppression models

Formal design specifications of fire spread and suppression agent models used in DEVS-FIRE are given and a new method for scheduling ignition events of forest cells more efficiently than the original onTime_Schedule event scheduling method is developed.
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Balancing Wildfire Risk and Power Outages Through Optimized Power Shut-Offs

The optimal power shut-off problem is proposed, an optimization model to support short-term operational decision making in the context of extreme wildfire risk, and the proposed optimization-based model reduces both wildfire risk and lost load shed relative to the benchmarks.
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Adaptive multicut aggregation for two-stage stochastic linear programs with recourse

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Forest Fire Spread and Suppression in DEVS

This model represents an advance toward developing a real-time decision support simulation system for predicting forest fire spread and the effects of water-based suppression attempts in uniform and nonuniform environmental conditions.
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A comparative study of decomposition algorithms for stochastic combinatorial optimization

  • Lewis NtaimoS. Sen
  • Computer Science
    Computational optimization and applications
  • 1 July 2008
Comparative computational results using three decomposition algorithms on a battery of instances drawn from two different applications show that convergence of the D2 methods for stochastic combinatorial optimization (SCO) is in fact attainable since the methods scale well with the number of scenarios.
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IIS branch-and-cut for joint chance-constrained stochastic programs and application to optimal vaccine allocation

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The time slot allocation problem under uncertain capacity

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