nazri engku

For the past few years, government-funded universities in Malaysia faced an uphill battle to strategize their management budget due to significant budget cut by the Malaysian Government. One portion of the budget will be spent towards achieving the key performance indicators (KPIs) set by the universities to achieve their annual targets. Unfortunately, some universities set up their specific strategies to achieve the KPIs without ample consideration to the limited available resources where less attention is channeled to the cost of achieving the KPIs. Setting priorities and making decisions on allocation and reallocation of resources based on the direction of the strategies must be executed with transparency and accountability and will be of great importance. In this paper we illustrate how integer programming was applied to allocate budget based on the KPIs set for one of the government-funded Malaysian universities' (U-XYZ's) research and publication agenda. Two models were developed and successfully solved. The first model was to determine the total budget needed if all the KPIs were to be achieved while the second model was to distribute the allocated budget set by U-XYZ, for all the activities planned for the agenda. The result showed that in order to achieve the target, U-XYZ has to increase its budget allocation by RM2.164 million. Otherwise, U-XYZ can only expect to obtain 1.578 out of 1.593 points that is required to be achieved.

This paper presents a brief review on the current available techniques for credit scoring model, namely the statistical-based models and the artificial intelligence/machine learning-based models. It is then followed by the suggestions on how to revise the credit scoring model that is currently being adopted by any credit risk management, if revision is needed. The revision of the model involves the selection of criteria to be included as well as the weights to be given for the criteria. Some potential techniques in selecting the criteria and determining the weights for the selected criteria are also discussed.

– Reports by various researchers show that ensuring pairwise comparison consistency is one of the main issues when applying analytic hierarchy process (AHP) in a decision making activity. Normally, in the case where inconsistencies are found, the evaluators might have to redo the pairwise comparisons, hence, disturbing the original preference. In this paper, we proposed an evaluation step prior to conducting the pairwise comparison in order to eliminate inconsistent comparisons. The evaluation step involves the rating of importance of criteria using a scale of 1-9 whereby 1 represents least important while 9 represents extremely important. The resulted ratings are then used as a guide to construct the AHP-pairwise comparison matrix by using certain simple algorithm that is also proposed in this paper. Once the pairwise comparison matrix is obtained, the weights of criteria can be calculated using the existing AHP-technique. Seven different experiments involving 1000 simulated pairwise comparisons for cases with three, four, five, six, seven, eight and nine criteria, respectively, were conducted. All results yielded consistent pairwise comparisons with consistency ratio values of less than the benchmarked value which is CR = 0.1. This technique can really be an added value to the standard AHP-technique since the users of AHP can now skip the consistency test and stop worrying about producing inconsistent pairwise comparison matrices as reported by many previous researchers.