A multi-objective optimization model for project scheduling and material procurement incorporating Monte Carlo cost estimation and ESG-oriented supplier selection

This study presents an integrated multi-objective optimization framework combining project scheduling and material procurement in construction management. The model jointly determines activity start times, material ordering and purchasing, supplier selection, inventory levels, and transportation planning using heterogeneous vehicles over a discrete horizon. Three conflicting objectives are considered: minimizing total project cost and project make-span, and maximizing suppliers’ Environmental, Social, and Governance (ESG) performance. Cost-related parameters are expressed as triangular fuzzy numbers, enabling robust planning under uncertainty. The model is solved using the Augmented Epsilon Constraint method to obtain Pareto-optimal trade-offs among objectives. A comprehensive two-span bridge construction case study demonstrates practical insights into balancing cost, time, and sustainability. Results indicate that increasing the total cost improves both make-span and ESG outcomes but with diminishing marginal benefits. Monte Carlo simulation confirms the optimal fuzzy cost as the median of its probabilistic distribution with roughly 50% confidence. The LP-Metric approach further examines how varying objective weights and supplier capacity constraints affect selection patterns. When capacity limits are absent, suppliers with higher ESG scores and shorter lead times are favored; under capacity restrictions, selection becomes stability-driven, focusing on suppliers capable of meeting required volumes. Sensitivity analyses highlight that lead time strongly influences make-span, whereas warehouse capacity affects ordering cost only up to an economic threshold, and vehicle-type diversification enhances transport efficiency. The proposed fuzzy multi-objective framework provides a comprehensive and realistic basis for sustainable, time-efficient, and cost-aware decision-making in construction projects.