Development of an Entrepreneurship and Time–Cost–Risk Optimization Framework in EPC Projects Using Hybrid Metaheuristic Algorithms
Keywords:
EPC project management, multi-objective optimization, Genetic Algorithm, Particle Swarm Optimization, project scheduling, project cost, risk analysis, metaheuristic algorithms, uncertainty, intelligent decision-support systemExtended AbstractAbstract
This study aimed to develop and evaluate an integrated optimization framework for the simultaneous management of time, cost, and risk in EPC projects using hybrid metaheuristic algorithms and uncertainty scenario analysis. This applied modeling and simulation-based study was conducted to design an advanced decision-support framework for engineering, procurement, and construction projects. First, the work breakdown structure of an EPC project was modeled, and activity precedence relationships, resource constraints, direct and indirect costs, and risk-related variables were identified. A multi-objective function was then formulated to simultaneously minimize total project duration, final project cost, and overall risk level. Genetic Algorithm, Particle Swarm Optimization, and hybrid metaheuristic configurations were employed to solve the optimization model. To incorporate dynamic uncertainty in activity durations and resource availability, more than 5,000 uncertainty scenarios were simulated and analyzed. The performance of the proposed framework was compared with conventional project scheduling and resource allocation approaches. Inferential results indicated that the proposed hybrid framework outperformed traditional scheduling methods in the simultaneous optimization of time, cost, and risk. Comparative scenario analysis demonstrated that the application of hybrid metaheuristic algorithms reduced total project duration by approximately 20% to 35% compared with conventional approaches. Moreover, delay-related additional costs decreased by approximately 15% to 30%. Sensitivity analysis and comparative assessment of high-risk scenarios further showed that the proposed model substantially reduced the overall project risk level and generated more efficient Pareto-optimal solutions for decision-making under resource constraints and uncertainty. The proposed framework can be used as an intelligent and integrated decision-support tool for EPC project management. By combining multi-objective optimization, hybrid metaheuristic algorithms, and uncertainty analysis, the model enables project managers to identify more efficient implementation scenarios. The framework may contribute to reducing delays, controlling cost overruns, improving risk mitigation, and supporting optimal managerial decision-making in complex industrial EPC projects.
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