Solving goal programming problems using multi-objective genetic algorithms

Goal programming is a technique often used in engineering design activities primarily to find a compromised solution which will simultaneously satisfy a number of design goals. In solving goal programming problems, classical methods reduce the multiple goal-attainment problem into a single objective of minimizing a weighted sum of deviations from goals. In this paper, we pose the goal programming problem as a multi-objective optimization problem of minimizing deviations from individual goals. This procedure eliminates the need of having extra constraints needed with classical formulations and also eliminates the need of any user-defined weight factor for each goal. The proposed technique can also solve goal programming problems having a non-convex trade-off region, which are difficult to solve using classical methods. The efficacy of the proposed method is demonstrated by solving a number of test problems and by solving an engineering design problem. The results suggest that the proposed approach is a unique, effective, and practical tool for solving goal programming problems.

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