Linear Programming and Genetic Algorithm Based Optimization for the Weighting Scheme of a Value Focused Thinking Hierarchy
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Deriving weights for a Value Focused Thinking (VFT) hierarchy demands considerable time and input from Decision Makers (DM) and Subject Matter Experts (SME). Often, the DMs and SMEs are the leaders of companies and organizations, and this required time is unrealistic with their schedules. In these situations, as well as scenarios where the are no available DMs / SMEs, conventional means of weighting a VFT hierarchy are impossible, and any VFT analysis is halted. When historical data exists on evaluation measures and performance of alternatives, linear programming and genetic algorithm based op...
Deriving weights for a Value Focused Thinking (VFT) hierarchy demands considerable time and input from Decision Makers (DM) and Subject Matter Experts (SME). Often, the DMs and SMEs are the leaders of companies and organizations, and this required time is unrealistic with their schedules. In these situations, as well as scenarios where the are no available DMs / SMEs, conventional means of weighting a VFT hierarchy are impossible, and any VFT analysis is halted. When historical data exists on evaluation measures and performance of alternatives, linear programming and genetic algorithm based optimization may be used to derive historically optimal weights for a hierarchy. Analysis may then be done to determine the utility of transposing these weights into a hierarchy to evaluate a current list of alternatives. This type of analysis is also useful in "first cut" weighting of a hierarchy, and therefore reduces the time demands for DMs/SMEs to complete the weighting process. This methodology can provide insight into any situation where historical information exists on ordinally ranked, competing alternatives.
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