PLANET : an intelligent decision support system for the formulation and investigation of formal planning models / (original) (raw)

This paper describes a problem solver called PLANET that has been developed in collaboration with a large computer manufacturing company to assist planning managers with the formulation and maintenance of planning models for resource allocation. PLANET is equipped with the primitives that enable it to preserve much of the richness of the process of the planning activity, namely, the generation of symbolic alternatives, and for the expression of domain specific knowledge which enables it to synthesize these alternatives into an overall planning model. This knowledge is maintained in a llmeta-model.w In contrast to modeling systems which allow for parametric perturbations of an algebraic model, PLANET1s meta-model provides it with the capability for systematic variations in the symbolic model assumptions, with concomitant structural variations induced in the algebraic model that reflect the interdependencies of those assumptions. Whenever previously held assumptions change, PLANET uses the existing model as a point of departure in formulating the revised plan. In this way, the program is able to take cognizance of the ongoing nature of organizational problem solving, and can serve an important decision support function in maintaining and reasoning about evolving plans. Center for Digital Economy Research Stem School of Business IVorking Paper IS-85-24 ''A good human decision support staff has two jobs to do. First it must reduce the set of all possible actions to the few that look potentially realistic, feasible, and good. It is this small handful that the top level decision maker actually considers when he reaches his final decision. Second, both in winnowing through the alternatives, and in projecting their consequences, the staff somehow must deal directly with the interrelations among the various parties involved. This is the only way it can hope to apply its knowledge about the parties, their goals, their resources, and the constraints under which they must operate. In general, however, we simply do not yet know how to incorporate such knowledge in numerical projection models. As a result, there is a real ceiling to what we can expect of decision support systems cast in current molds." (Reitman, 1981). Perhaps a more serious limitation of existing computer-based systems is their inability to take cognizance of the ongoing, evolutiona_lly: nature of organizational problem solving, that is, to preserve and reason about previous decisions and changes to them-something that is an integral part of a manager's job. If we pose Reitman's question again, we realize that many good alternatives a > in fact generated or synthesized in the course of formulating a plan. However, only a small subset of these become part of the "finalff plan and reflected in the algebraic model that is derived from it. Unfortunately, much of the knowledge about issues and choices that were available, and the rationales for choosing or rejecting alternatives end up in filing cabinets or voluminous reports, often permanently. This is not altogether surprising. Given the effort involved in formulating the plan in the first place, and the difficulty of coordinating the diverse inputs from the various parties involved, a systematic assessment of the ramifications of changes can become overwhelming. Yet, in the absence of this knowledge, the existing algebraic model provided by a modeling system can have the effect of unnecessarily confining users to its limited view of an inherently flexible situation. For such problems, the real decision support needed is not in helping fine tune an existing model, but one of exposing a decision maker to the multiple perspectives brought about by changes in assumptions, and of interactively assisting in the reformulation model of the situation.