Name
Abstract | ||
|---|---|---|
In this work we focus on efficient heuristics for solving a class of stochastic planning problems that arise in a variety of business, investment, and industrial applications. The problem is best described in terms of future buy and sell contracts. By buying less reliable, but less expensive, buy (supply) contracts, a company or a trader can cover a position of more reliable and more expensive sell contracts. The goal is to maximize the expected net gain (profit) by constructing a close to optimum portfolio out of the available buy and sell contracts. This stochastic planning problem can be formulated as a two-stage stochastic linear programming problem with recourse. However, this formalization leads to solutions that are exponential in the number of possible failure combinations. Thus, this approach is not feasible for large scale problems. In this work we investigate heuristic approximation techniques alleviating the efficiency problem. We primarily focus on the clustering approach and devise heuristics for finding clusterings leading to good approximations. We illustrate the quality and feasibility of the approach through experimental data. |
Year | Venue | Keywords |
|---|---|---|
2013 | UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence | clustering approach,available buy,efficient heuristics,two-stage stochastic linear programming,efficiency problem,large stochastic matching problems,large stochastic,experimental data,large scale problem,future buy,expensive sell contract,stochastic planning problem,linear program,profitability |
DocType | Volume | ISBN |
Journal | abs/1301.2277 | 1-55860-800-1 |
Citations | PageRank | References |
3 | 0.43 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Milos Hauskrecht | 1 | 921 | 90.70 |
| Eli Upfal | 2 | 4310 | 743.13 |