Stochastic programming models static or dynamic optimal decision making under uncertainty. In contrast to deterministic mathematical programming, stochastic programming generally uses expectation functionals in objective or constraints over known or partially known distributions of the problem data. Its features many decision variables under complicated constraints over discrete time periods. Its...
Funding risky Research and Development (R&D) and New Product Development (NPD) projects is crucial for the long term health of any large company. Such funding decisions are multiobjective and are undertaken with sparse data. Therefore prescriptive or algorithmic solutions are not adopted by practitioners. Descriptive solutions that provide managerial insights...
Operations Management (OM) is concerned with the processes involved in delivering goods and services to customers (Hopp and Spearman 2000, Shim and Siegel 1999). While recent surge in service and professional white collar work has greatly changed the arena of OM practice, OM research has not yet well address the...
This dissertation studies the management of operations that match surplus inventory of one party to meet the need of another. The first part is concerned with the efficient and robust design of transshipment networks in a commercial environment. The second part is concerned with a sequential resource allocation problem in...
Both individual and institutional investors face a number of constraints in their consumption and investment decisions. We look at well-motivated constraints on the consumption process as well as liquidity constraints and study their impact on optimal consumption and investment policies under a dynamic discrete time setting.
The most important managerial criteria in supply chains are how to manage product, information and cash flows, and how to maximize profits by either increasing the revenue or decreasing the costs. Although the maximum benefits can be achieved if everyone follows the central planner's suggestions; unfortunately, the individual maximum profits...
In this dissertation, we explore modeling and solution methods for intermodal drayage operations. This research is motivated by the need to provide operational choices in drayage operations to increase efficiency; however, as shown in our work, the introduction of this flexibility in modeling and solution methods is challenging.
Intermodal freight...
In this thesis we discuss the issue of solving stochastic optimization problems using sampling methods. Numerical results have shown that using variance reduction techniques from statistics can result in significant improvements over Monte Carlo sampling in terms of the number of samples needed for convergence of the optimal objective value...
Markov models are widely employed in cost-effectiveness analysis of healthcare interventions. Although such models are usually formulated at the individual level, it is also useful to examine outcomes at the population level. Analysts may wish to know the impact of a health intervention on a whole population instead of an...
Nelson and Staum derived ranking-and-selection procedures that employ control-variate (CV) estimators instead of sample means to obtain greater statistical efficiency. However, control-variate estimators require more computational effort than sample means, and effective controls must be identified. In this dissertation, we present a new CV screening procedure to avoid much of...