The compromise decision support problem: Modeling the deviation function as in physical programming

The Archimedean and preemptive formulations that are currently used in the goal formulation of the Compromise Decision Support Problem (DSP) suffer two major drawbacks related to the arbitrary definition of priority levels and targets for multiple objectives. The Archimedean formulation is very difficult to implement because there is no consistent way to determine a priori a correct set of weights. Thus, choosing weights is either done arbitrarily or by cumbersome iterations. In the preemptive approach one objective is assumed to be infinitely more important than the others. Also, in the Compromise DSP it is necessary to define targets for each goal, and usually those targets are selected based on ‘educated guesses’ or through an inefficient process of iteration. These shortcomings undermine the effectiveness of the Compromise DSP as a design tool. These problems can be amended by modifying the Compromise DSP according to the Linear Physical Programming formulation. In this paper the modification of the Compromise DSP and its advantages are described, and the method is illustrated with a case example of the preliminary design of an absorption chiller.