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AN INTERACTIVE ALGORITHM FOR MULTIOBJECTIVE DECISION MAKINGMonarchi, David Edward, 1944-; Department of Hydrology & Water Resources, The University of Arizona (Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1972-06)This research develops an algorithm for solving a class of multiple objective decision problems. These problems are characterized by continuous policy variables, nonlinear constraints, and nonlinear criterion functions. Our underlying philosophy is that of the Gestalt psychologists-- we cannot separate the problem and its solution from the environment in which the problem is placed. The decision maker is necessarily a part of this environment, thus implying that he, as an individual, must be part of the solution of the problem. Another central assumption in this research is that there is not an "optimal" answer to the problem, only "satisfactory" solutions. The reasons for this are based partly on the insensitivities of the body to minute changes and to the insensitivity of our preferences within certain ranges of acceptance. In addition, we assure that the individual is capable of solving decision situations involving a maximum of about 10 goals and that he operates upon them in some sort of serial manner as he searches for a satisfactory alternative. The serial manner is a reflection of his current ranking of the goals. Based on these assumptions we have developed a cyclical interactive algorithm in which the decision maker guides a search mechanism in attempting to find a satisfactory alternative. Each cycle in the search consists of an optimization phase and an evaluation phase, after which the decision maker can define a new direction of search or terminate the algorithm. The optimization phase is based on a linearization technique which has been quite effective in terms of the problems we have attempted to solve. It is capable of solving general nonlinear programming problems with a large number of nonlinear constraints. Although the constraint set must be convex in order to guarantee the location of a global optimum, we can use the method on concave sets recognizing that we may find only a local optimum. An extensive synthetic case study of a water pollution decision problem with 6 conflicting goals is provided to demonstrate the feasibility of the algorithm. Finally, the limitations of the research are discussed. We tentatively conclude that we have developed a method applicable to our research problem and that the method can be applied to "real world" decision situations.