  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.
