Multi-Objective Evaluation Model With Fuzzy Goal Programming: A Satisficing Method

The paradigm of multi-criteria decision making considers not only many attributes in the decision-making process but also the optimization of a number of objectives to accomplish certain targets. A multi-objective programming problem is a problem involving the simultaneous optimization of various, often conflicting, objective functions under certain given constraints to find compromise solutions 1991). The solution of multi-objective optimization problems is dependent upon the decision-makers preferences and often involves a search for the most preferred compromise solution. Although in real-life situations, many problems have been studied from the optimization point of view, an optimization approach does not always provide the best practical solution because decision-making is highly influenced by social and economic environments. Moreover, in the real world, there exist multiple objectives that are optimized only in parallel with iterative, trial-and-error processes because those objectives are fundamentally conflicting, incommensurable, imprecise or fuzzy in nature. This nature often leads multi-objective decision making to seek the most preferred compromise (satisfied) solution rather than an optimal solution. This solution can be accomplished by setting goals and then seeking the particular allocation that offers the best promise of achieving those goals (Saaty, 1980). Given the inherent issues with optimization, a satisfaction approach should be a much better solution method in many practical situations. One of the solutions to address the mathematical programming problem with multiple objectives is the goal programming (GP) model (Charnes and Cooper, 1961), which is developed based on a satisfaction philosophy. The GP model enables the decision-maker to evaluate many goals simultaneously and choose the