Comparison of Two Goal Programming Models for Budget Allocation Problem - A Case Study

Each organization has an array of major goals. At a superficial look, they seem coordinated and congruent but when managers break them down into smaller goals and try to allocate the budget of each of them, multiple conflicting goals pop up and make budget planning seriously challenging. Conflict among organizational goals is a common feature of multi-objective entities and causes an imbalance in achievement of the set of goals. Namely, one goal is achieved at the expense of deviation or less achievement of another goal. Because organizations such as universities, municipalities, financial and credit corporations and industrial firms are multi-objective systems, they are not an exception to imbalanced achievement of goals. The imbalanced achievement of goals has a very dysfunctional effect on organizations all-out performance. To avoid this, all budgeters and resource allocators should be aware of efficient techniques for budget planning in multi-objective systems. Since multi-objective nature of organizational systems endogenously brings about an amount of imbalance over achieving the goals, reducing this imbalance is always regarded as a very important problem for OR experts so that many efforts have been made to solve it up to now. Generally, many mathematical models have been formulated for this purpose from which the Goal programming (GP) seems to be the most promising one. Unlike Linear Programming (LP), GP is multi-objective and different variants of its have been developed for multi-objective problems, among which Lexicographic Goal Programming (LGP) Weighted Goal Programming (IWGP) have been frequently used for budget planning. But, because LGP and WGP fail in achieving a good balance between the achievements of the set of goals, especially when decision makers like their goals to be achieved by a good balance, a better alternative needs to be used. The Chebyshev goal Programming (CGP) is what this paper proposes as the most appropriate method when the decision makers like to observe a good balance among their achieved goals. Thus, supported by a real world example, this paper compares GP models in terms of balancing principle and shows their potentials to achieve a good balance