A decision support methodology for stochastic multi-criteria linear programming using spreadsheets
Decision Support Systems
Total allowable catch for managing squat lobster fishery using stochastic nonlinear programming
Computers and Operations Research
Approximate dynamic programming: lessons from the field
Proceedings of the 40th Conference on Winter Simulation
Management of disruption risk in global supply chains
IBM Journal of Research and Development
Production planning for semiconductor manufacturing via simulation optimization
Proceedings of the Winter Simulation Conference
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Linear programming is a fundamental planning tool. It is often difficult to precisely estimate or forecast certain critical data elements of the linear program. In such cases, it is necessary to address the impact of uncertainty during the planning process. We discuss a variety of LP-based models that can be used for planning under uncertainty. In all cases, we begin with a deterministic LP model and show how it can be adapted to include the impact of uncertainty. We present models that range from simple recourse policies to more general two-stage and multistage SLP formulations. We also include a discussion of probabilistic constraints. We illustrate the various models using examples taken from the literature. The examples involve models developed for airline yield management, telecommunications, flood control, and production planning.