This chapter describes a GP procedure for modelling and solving academic resource planning problems in university management system with interval data uncertainty. In the proposed approach, the interval goals are first converted into the standard goals by using interval arithmetic technique. Certain objectives having the characteristics of fractional programming are transformed into linear goals by using linearization approach to solve the problem by employing linear GP methodology. In the model formulation of the problem, both the aspects of GP, minsum and minmax approaches, are addressed to construct the goal achievement function for minimising the possible regret towards achieving the goal values within the target intervals specified by the DM in the decision making environment. The potential use of the approach is illustrated by a case example. The model solution is compared with the solutions of the models studied previously.

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This article presents a fuzzy goal programming (FGP) procedure for solving a stochastic multiobjective decision making (MODM) problem having the finite probabilistic aspiration levels for achievement of the chance constrained goals.In the proposed approach, the different chance parameters involved with the stochastically defined goal constraints are considered continuously distributed independent random variables.In the model formulation, first the membership functions of the fuzzy probablistic goals are constructed by defining the tolerance limits for achievement of the goals in the decision situation. Then, the defined membership functions are converted into their deterministic equivalent according to the prescribed distribution function of the random parameters.In the solution process, achievement of the membership goals defined for the deterministic membership functions to the highest value (unity) to the extent possible on the basis of the weights of importance of achieving the goals is considered.Two numerical examples are solved to illustrate the approach.

This chapter presents how Genetic Algorithm (GA) is effectively employed to Goal Programming (GP) formulation of an agricultural planning problem having interval model parameters and a set of chance constraints for optimal production of seasonal crops in uncertain environment. In model formulation, the planned-interval goals associated with objectives of the problem are converted into their equivalent two-objective deterministic goals. The chance constraints are also converted into their deterministic equivalents to solve the problem by using GP methodology. In goal achievement function, minimization of deviational variables associated with model goals is evaluated on the basis of priorities by employing a GA scheme to reach optimal decision. In the decision process, sensitivity analysis with variations of priority structure of goals is performed, and then the notion of Euclidean distance function is used to identify the priority structure under which optimal production of crops can be obtained in the decision environment. A case example is considered to demonstrate the approach.