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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/620100 |
| Date | 06 1900 |
| Creators | Monarchi, David Edward |
| Contributors | Department of Hydrology & Water Resources, The University of Arizona |
| Publisher | Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ) |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Technical Report |
| Source | Provided by the Department of Hydrology and Water Resources. |
| Rights | Copyright © Arizona Board of Regents |
| Relation | Technical Reports on Hydrology and Water Resources, No. 6 |
Page generated in 0.0019 seconds