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This paper illustrates how the goal programming problem with fuzzy goals having linear membership functions may be formulated as a single goal programming problem. Also, a previously defined method for dealing with fuzzy weights for each of the goals is re‐examined.

AbstractThis paper illustrates the application of "fuzzy subsets" concepts to goal programming in a fuzzy environment. In contrast to a typical goal‐programming problem, the goals are stated imprecisely when the decision environment is fuzzy. The paper first considers a fuzzy goal‐programming problem with multiple goals having equal weights associated with them. A solution approach based on linear programming is developed. Next, the solution approach is extended to the case where unequal fuzzy weights are associated with multiple goals. Numerical examples are provided for both cases to illustrate the solution procedure.

One of the shortcomings of goal programming lies in its linearity, assumption, specifically in the objective function. This assumption compels one to work with constant marginal utilities and rates of substitution. In this paper a quadratic preference function, which is more consistent with economic theory and reality, is formulated and introduced into goal programming. In an effort to facilitate the understanding of the proposed procedure, two illustrative examples—one with symmetric preferences and the other with asymmetric preferences, both applied to the objective function—are solved and compared with a goal programming solution.

ABSTRACTIn recent years, decision making based on systematic analysis has been greatly emphasized. Yet, decision analysis is often carried out without analyzing the limitations of certain quantitative techniques. This paper presents the concept, solution method, and application potential of goal programming which eliminates many limitations of conventional linear optimization models.

This paper pertains to goal programming with fuzzy goals and fuzzy priorities. Hannan [1], in his paper on fuzzy goal programming, alludes to the difficulty of handling fuzzy priorities and further notes that a method that this author proposed [2] may lead to incorrect results. In this note, the general problem of goal programming with fuzzy priorities is reexamined, along with the solution to the specific example presented in my original paper [2]. It is shown that the method for handling fuzzy priorities originally proposed by this author does indeed capture the relative importance of goals.

AbstractThis paper concerns the integration of goal programming and scenario planning as an aid to decision making under uncertainty. Goal programming as a methodology emphasises the resolution of conflict among criteria; scenario planning focuses on the treatment of uncertainty relating to future states of the world. Integrating the two methodologies is based on the simple formulation of a super‐goal programme consisting of one scenario‐specific goal program in each scenario. Issues relating to the structuring of the super‐problem, aggregation both within and over scenarios, and the incorporation of probabilistic information are discussed. Copyright © 2005 John Wiley & Sons, Ltd.

The advantages of Goal Programming (GP) over linear programming
(LP) are discussed in the context of the healthcare industry. Decision
makers must give considerable attention to the formulation of a GP
model. However, long‐ and short‐term solutions must not be confused.
Solutions also require implementations, which may be impractical or
difficult. The full utilisation of facilities is recommended in an
attempt to reduce unit costs and increase output.

ABSTRACTThe media selection decision allocates advertising dollars among competing media so as to optimize promotional and corporate objectives. Linear programming attempts to model this process have been complicated by multiple and often conflicting management goals, the need for integer solutions, and nonlinearities. This study offers a technique that is sufficiently robust to simultaneously handle these problems. An alternative media selection framework is presented and the results of an illustrative application of integer goal programming are discussed. The proposed model improves on linear programming by success fully providing for optimal, integer solutions in settings that more realistically reflect the complexity of the media decision environment.