Risk-Tolerant On-Site Dispatch for Autonomous Mining Truck Fleets With Uncertain Failure Signs

This study proposes a risk-tolerant dispatch approach for a fleet of autonomous mining trucks in an open-pit mine, leveraging early signs to reduce the impact of potential failures that may or may not occur later. Unlike traditional methods that ignore these early signs or wait until a failure has actually happened, our approach proactively plans for both possible outcomes without relying on probabilities. We propose a Y-shaped solution structure composed of a shared trunk that covers the period before it becomes clear if the failure will occur and two separate branches that address the final scenarios. We formulate the dispatch problem as a mixed-integer linear program and solve it via Gurobi. To facilitate the solution process with Gurobi, an evolutionary algorithm is adopted to explore the solution space for a good initial guess. A high-performance discrete-event simulator is embedded in the cost function evaluation module of the evolutionary algorithm for quickly selecting qualified solution candidates. By integrating both the failure and non-failure scenarios into one unified plan, we avoid extreme risk-taking or undue conservatism, ensuring stable operational performance. Simulations and field trials at a real open-pit mine confirm that this risk-tolerant approach effectively manages failure risks when early signs are available.