Risk in the construction industry is an important factor which must be considered in every decision. To ensure the smooth operation of any project, construction risk factors need to be investigated and assessed. For this reason, the evaluation of the risk factor is determined using expert opinion. Four main risk categories are used in this study, namely risk on project management, engineering risk, implementation risk, and supplier risk. The purpose of this study is to implement a fuzzy analytical hierarchy process (fuzzy AHP), a method that has been receiving increased interest in decision making situations to determine the relative importance of the criteria used for the decision when calibrating the end of each risk factor stage. A paired comparison was employed for subjective judgment made by experts in order to compute the priority weight vector for each risk item. Results show that the risk on supplier carries the highest weight (0.91), followed by the risk of accidents on site (0.58) and less motivation among the project management team (0.55). The final relative weight at the last level of the hierarchy gives a signal to the decision maker of organization of all the possible impacts of the risks revealed. Hence, the application of fuzzy AHP in ranking risk factors has demonstrated better results and more flexibility in human decision making.

Mamdani fuzzy inference system has been widely used for potential risk modelling and management. The decision-making is usually provided by multiple experts in the field. The conflicting information in sources from different experts become an open issue and has attracted some researchers to investigate further. Various risk factors in a project caused difficulties for decision makers to make reliable decisions on the whole project since it involves ambiguities, vagueness, and fuzziness. The introduction of the fuzzy inference system to the evaluation of construction risk is capable in explaining its reasoning process and, hence, overcoming such problems. Risk factors under the project management risk were identified through literature sources and from the opinion of experts. It is found that the likelihood and severity of risk is somehow interlinked with the concept of fuzzy theory. For model input and output linguistics variables, the triangular membership function was selected. The methodology employs a fuzzy aggregation system in which an appropriate control action can be determined by the acquisition of expert judgment. A total of 23 rules with logical OR operator, truncation implication, and Mean of Maxima (MoM) method for defuzzification were used to create an effective fuzzy model intended for making decisions. The framework determines the relationship between input and output parameters in if-then rules or mathematical functions using an effective fuzzy arithmetic operator. The study addresses the principle issues of multiexpert opinions based on Mamdani-type decision system and the illustrative example taken from one of medium-sized project held in Malaysia’s construction industry. By comparing with other experimental results, we verify the rationality and reliability of the proposed method.

The effectiveness of government financing is a challenge in various industries, including higher education universities. The funding source and the resources' size are the key determinants of quality education. The problems arise in multi-criteria decision-making, where many subjective opinions are needed from the experts. It is, therefore necessary to prioritize the limited budget available for important criteria. On the other hand, multi-criteria evaluation leads to technically rigorous and enlightened university budget decisions. This paper proposes the exploitation of the Analytic Hierarchy Process (AHP) in budget allocation at one of the public universities in Malaysia. This study’s participants were eight top management experts in managing expenditure at the faculty level. The findings showed that the most significant factors in deciding budget allocations are Teaching and Learning (0.30) and Maintenance (0.26). Furthermore, the most dominant sub-criteria were laboratory and equipment devices (S4) and training and conferences (S10), with a weighted mean of 0.682 and 0.664, respectively. The weights were aggregated by the geometric mean and median, as well as the simulated mean and median, which showed deviating results and rank reversals. The geometric mean weights differed significantly. In contrast, the aggregation using measures of the median was similar to the geometric median, with only a few rankings criteria differing. This highlights that the median score is significant in weight calculation.