A Chance Constrained Approach on Goal Programming Model to Evaluate Transportation Planning

Abstract

Chance Constrained Programming (CCP) as one of the special parts of mathematical programming in which its application in industrial and academic fields is currently needed to cover the problem of uncertainty in making decisions. The CCP technique is an efficient and easy tool to apply for data involved to random parameters by assuming random parameters must follow a stable normal distribution. In selection process, budget goal, making the most benefits, and achieving minimum travel are considered as a random parameter and solved by using CCP. Goal Programming (GP) model, will be used to model the rail system transportation project selection process, because allocating resources into all projects at the same time requires multi-criteria decision making techniques. The use of the CCP in the GP model provides flexibility in determining the level of opportunity to avoid mistakes in making decisions on the objective function. In other words, the reliability of random parameters can be considered more flexible in detail. In addition, the deviation variable in GP can give more information to decision makers to consider project selection. The results obtained indicate a balance between the use of the budget and the costs, including achieving the minimum risk evaluated in the AHP So that in this model the evaluation of transportation planning for social, economic impacts and the effectiveness of the use of the budget can be optimized properly.