IDEA: Integrated Decomposition for Enterprise Analysis
Ohio State University Research Foundation -Do Not Use, Columbus OH
Investigators
Abstract
This project identifies a new class of stochastic programs referred to as "Distributed Stochastic Programs," and develops computational approaches for their solution. The methodology to be studied involves a marriage of the "Lagrangean Decomposition" method of Guignard and Kim with the "Stochastic Decomposition" methods of Higle and Sen. The primary motivation is the study of integrated models and solution methods for enterprise-based decision making under uncertainty. An enterprise-based model is one that couples a variety of component problems that are associated with different entities within the enterprise. Of necessity these component problems, which will typically reflect operational concerns,are finely detailed. Their individual solutions often require highly specialized computational approaches that challenge current computing resources. As a result, their direct incorporation within an enterprise-based model would likely lead to a model that greatly exceeds current computational capabilities. To remedy this situation, this project advocates the use of surrogate models of the component problems that reflect the uncertainty associated with the day-to-day operations, and are combined in a manner that permits guidance for strategic decisions. A primary goal of this project is the development of solution methods for the resulting class of problems based on decomposition schemes that exploit the general structure of the enterprise model. Over the past few years, the need to incorporate uncertainty within complex business environments has become increasingly obvious. Many firms have invested a great deal of time and resources in developing models that can be used to assist with operational decision making. For example, airlines typically use sophisticated models to assist with their air fleet assignments, crew scheduling, maintenance schedules, etc. In addition to these types of operational decisions, firms also undertake strategic, or long-term, decisions, such as when an airline determines what markets it will serve. In general, these strategic decisions impact the operational decisions, although neither this impact nor the uncertainty associated with day-to-day operational decisions is typically captured within the enterprise's strategic decision model. This project involves an exploration of integrated models and solution methods for enterprise-based decision making under uncertainty.
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