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We study a daily mine planning problem where, given a set of blocks we wish to mine, our task is to generate a mining sequence for the excavators such that blending resource constraints are met at various stages of the sequence. Such time-oriented resource constraints are not traditionally handled well by automated planners. On the other hand, the remaining problem involves finding node-disjoint sequences with state-dependent travel times on the arcs, which are highly challenging for a Mixed-Integer Program (MIP). In this paper, we address the problem of finding feasible sequences using a combined MIP and planning based decomposition approach. The MIP takes care of the resource constraints, and the planner solves the remaining sequence problem. We extend the notion of finding feasible sequences to finding good feasible sequences, by devising a heuristic objective function in the MIP, which improves the resulting search space for the planner. We empirically analyse the scalability of our approach on a benchmark data set, before demonstrating its effectiveness on a real world case study provided by our industry partner. These results demonstrate that by using a heuristic MIP, it is possible to obtain better makespan results with a suboptimal planner than by using an optimal planner with an uninformed MIP.

Association for the Advancement of Artificial Intelligence
Computing and Information Systems, The University of Melbourne, Parkville, Australia
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