Triangulation in decision support systems: Algorithms for product design

Abstract

Often complex decision problems requiring decision aids, such as a Decision Support System (DSS), do not have solution procedures that can generate an optimal solution in a realistic time period. This has led to the specification of heuristic solution procedures. However, the quality of the solution obtained using a heuristic in specific instances can be uncertain and may be open to debate. One approach to increase the confidence in the quality of the obtained solution is to use the triangulation approach recommended and often used in the social sciences. Thus, the result obtained with a specific heuristic can be considered ‘good’ (i.e., close to optimal) if that result is in the ball park of the result obtained through a maximally different method. In other words, using very different solution techniques helps provide benchmarks and thus enables the decision maker to avoid those solutions which are caught in local maxima. Based on this notion we have designed a prototype GENEtic algorithms based decision support SYStem (GENESYS) for the product design problem. The DSS provides three different solution techniques, specifically, complete enumeration (optimal solution) for small problems, heuristic dynamic programming and genetic algorithms, to address the product design problems.