Continuous transportation of material in the mining industry is achieved by the dispatch of autonomous haul-trucks with discrete haulage capacities. Recently, Monte Carlo Tree Search (MCTS) was successfully deployed in tackling challenges of long-run optimality, scalability and adaptability in haul-truck dispatch. Typically, operational constraints imposed on the mine site are satisfied by heuristic controllers or human operators independent of the dispatch planning. This article incorporates operational constraint satisfaction into the dispatch planning by utilising the MCTS based dispatch planner Flow-Achieving Scheduling Tree (FAST). Operational constraint violation and satisfaction are modelled as opportunity costs in the combinatorial optimisation problem of dispatch. Explicit cost formulations are avoided by utilising MCTS generator models to derive opportunity costs. Experimental studies with four types of operational constraints demonstrate the success of utilising opportunity costs for constraint satisfaction, and the effectiveness of integrating constraints into dispatch planning.

翻译：采矿行业的物料连续运输通过调度具有离散运输能力的自动驾驶卡车实现。近期，蒙特卡洛树搜索（MCTS）在解决卡车调度中的长期最优性、可扩展性和适应性挑战方面取得了成功。通常，矿区施加的运营约束由独立于调度规划的启发式控制器或人工操作员满足。本文通过利用基于MCTS的调度规划器Flow-Achieving Scheduling Tree（FAST），将运营约束满足纳入调度规划。运营约束违反与满足被建模为调度组合优化问题中的机会成本。通过利用MCTS生成器模型推导机会成本，避免了显式成本公式的构建。针对四类运营约束的实验研究表明，利用机会成本实现约束满足具有良好效果，且将约束集成至调度规划具有显著有效性。