Budgetary allocations of resources are made in all businesses, but their volume and composition vary; and efficient budget allocation is fundamental to flow in businesses. The objective of the allocation problem is to determine the required budget for each department (or section) of a company so as to maximize the sum of the company's benefits. The purpose of this paper is to find a suitable degree of fuzziness for preference rankings and to demonstrate an example of budget allocation using artificial intelligence programming, and fuzzy analytic hierarchy process (FAHP). An efficient budget allocation method using FAHP will be provided for businesses. This method is suitable for use in evaluating proposed policies (including tangible and intangible information). A comparison between FAHP and artificial neural network (ANN) will be also made in this paper. An aerospace company's budget allocation problem is investigated as a case study in this research, which will illustrate how to solve this problem. The case study utilizes a two-stage interview (semi-structured interview and in-depth interview) to select their budget allocations given a number of tangible and intangible criteria. The results from the case study are pertinent to other real-world allocation problems that share many of the characteristics of problems, such as decision makers' subjective opinions.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence