The success of construction projects is always exposed to risk, such as delay and Project Loss Costs. Based on empirical studies in manual and computational methods, delay and overrun costs were defined as single output with different analyses that require two different processes. This approach has been considered as less effective and less practical. Thus, this research attempted to allocate delay and Project Loss Cost simultaneously as double outputs through the implementation of the Sugeno fuzzy logic system with input double output (MIDO). The objectives of this research are to manually calculate the application of Sugeno fuzzy to minimize Time Delay (TD) and Project Loss Cost (LC), to computationally calculate the application of Sugeno fuzzy to minimize TD and LC, to validate both approaches, and to present the simulation results of Sugeno computational method for minimizing TD and LC through using a fuzzy software, namely the Mamdani MATLAB R2019a. It could be concluded that manual application of Sugeno fuzzy logic for the prediction of double output involved a four-step process, which is fuzzification of variable input, application of an implication function, composition of rules, and applying the weighted average defuzzification method. Meanwhile, the computationally applied Sugeno fuzzy method went through six stages, namely implementation of MIDO system, the categorization of two linguistics sets which are minimum and maximum, application of trapezoid membership function, establishment of 81 fuzzy rules, application of the MIN implication function, and the process of defuzzification. The validation results indicated that the outputs of manual method equal or are similar to the computational method, i.e., TD = 2.44 days and LC=0.813%. Meanwhile, the linearity tests for the TD value (R2=0.9711> 0.95) and LC value (R2=0.9799> 0.95) were declared as valid. The simulation results showed that Time Delays and Project Loss Costs cannot be minimized if the allocation of funds to the input variable is not balanced with the maximum penalty costs and vice versa.

Topics
MATLAB, Cognitive science, Computational methods, Fuzzy logic, Funding
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