Robust truck dispatching in open-pit mining under epistemic uncertainty: A fuzzy optimization and agent-based simulation framework

Efficient truck dispatching is critical to the productivity and cost-effectiveness of open-pit mining operations. However, dispatch decisions are complicated by discrete allocation constraints and significant epistemic uncertainty arising from fluctuating equipment availability and operational conditions. This study proposes an integrated framework for robust truck dispatching under uncertainty by combining deterministic optimization, fuzzy modeling, and Agent-Based Simulation (ABS). The dispatching problem is first formulated as a mixed-integer linear program that maximizes the total transported material, subject to operational and network constraints. To account for imprecision in haul truck performance, the utilization coefficient is modeled as a triangular fuzzy number, yielding a fuzzy mixed-integer formulation. The fuzzy model is converted into a solvable crisp form using centroid defuzzification, enabling practical implementation without altering system constraints. An agent-based simulation model based on real operational data from an open-pit mine is developed to evaluate dynamic performance under varying conditions of equipment availability and congestion. Results indicate that system productivity is highly sensitive to truck effectiveness, with production losses of up to ۱۵% observed under reduced availability. The proposed framework provides a quantitative tool for assessing production risk and supports robust dispatching decisions in uncertain operating environments.

کلیدواژه ها:

Truck dispatching ، fuzzy optimization ، Mixed-Integer Linear Programming ، Agent-Based Simulation ، Epistemic uncertainty ، Open-pit mining