Monte Carlo Mathematical Model Simulation: An Evaluation of The Probability of Construction Project Acceleration - Pratama R, Alfandi Rizki; Djakfar, Ludfi; Negara, Kartika Puspa (Journal of Multidisciplinary Applied Natural Science, 2025)
Complex projects such as road infrastructure require reliability and risk analysis for safety and economic sustainability. This study divides the concept of comprehensive risk identification into several variables with several indicators. Indicators refer to findings that occur in the field during acceleration. The simulation was conducted with the help of the @Risk program in Microsoft Excel using standard settings, and the distribution used was triangular. The population in this study is all project stakeholders related to determining the implementation time of the Djuanda FO construction, which is currently underway in the 46th week. The population consisted of 22 experts who were involved in scheduling the FO Djuanda construction project. Since the population size is limited in the study, all population members were sampled. The sampling technique is census or saturated sampling, in which all population members are used as samples. The respondents asked to complete a questionnaire and answer questions about implementing the FO Djuanda development project. Sampling is limited to top management with expertise in decision-making to determine the duration of project implementation in scheduling. This study obtained interval data, with interval data in the form of a Likert scale (scale 1–5). Based on the research results, risk evaluation can be adopted well through Monte Carlo mathematical model simulation. The risks in the acceleration of the FO Djuanda development project, based on the order of risk levels from the largest to the smallest, are direct costs, work calendar schedule, logistics, external, field constraints, health and safety, indirect costs, community relations, environment, construction contracts, traffic, and construction.