| Article ID: | iaor20043329 |
| Country: | Netherlands |
| Volume: | 36 |
| Issue: | 1 |
| Start Page Number: | 99 |
| End Page Number: | 116 |
| Publication Date: | Sep 2003 |
| Journal: | Decision Support Systems |
| Authors: | Ragsdale Cliff T., Novak David C. |
| Keywords: | artificial intelligence: decision support, decision theory: multiple criteria, stochastic processes, spreadsheets |
In recent years, tools for solving optimization problems have become widely available through the integration of optimization software (or solvers) with all major spreadsheet packages. These solvers are highly effective on traditional linear programming (LP) problems with known, deterministic parameters. However, thoughtful analysts may rightly question the quality and robustness of optimal solutions to problems where point estimates are substituted for model parameters that are stochastic in nature. Additionally, while many LP problems implicitly involve multiple objectives, current spreadsheet solvers provide no convenient facility for dealing with more than one objective. This paper introduces a decision support methodology for identifying robust solutions to LP problems involving stochastic parameters and multiple criteria using spreadsheets.