A decision support methodology for stochastic multi-criteria linear programming using spreadsheets

Authors:
David C. Novak;Cliff T. Ragsdale
Affiliations:
Department of Operations and Information Management, University of Connecticut, 1200 Hillside Road, Unit 1041 OPIM, Storrs, CT;Department of Business Information Technology, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA
Venue:
Decision Support Systems
Year:
2003

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Abstract

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.