Chance-constrained fuzzy goal programming with penalty functions for academic resource planning in university management using genetic algorithm

Abstract

This chapter addresses grafting of penalty functions in the framework of fuzzy goal programming (FGP) for modeling and solving academic resource planning problem by employing genetic algorithm (GA). In model formulation, first incorporation of penalty functions to membership goals associated with percentage achievement of fuzzy objectives in different ranges is defined to obtain appropriate membership values of objectives in the decision horizon. Then, a set of chance constraints that are inherent to model is transformed into deterministic equivalents to solve the problem using FGP methodology. In solution search process, a GA scheme is employed iteratively to evaluate goal achievement function on the basis of assigned priorities to model goals of the problem. A sensitivity analysis with variations in priority structure of goals is also performed, and Euclidean distance function is used to identify appropriate priority structure to reach optimal decision. A demonstrative case example is considered to illustrate the approach.