Short-term Underground Mine Scheduling : Constraint Programming in an Industrial Application

Abstract: The operational performance of an underground mine depends critically on how the production is scheduled. Increasingly advanced methods are used to create optimized long-term plans, and simultaneously the actual excavation is getting more and more automated. Therefore, the mapping of long-term goals into tasks by manual short-term scheduling is becoming a limiting segment in the optimization chain. In this thesis we study automating the short-term mine scheduling process, and thus contribute to an important missing piece in the pursuit of autonomous mining.First, we clarify the fleet scheduling problem and the surrounding context. Based on this knowledge, we propose a flow shop that models the mine scheduling problem. A flow shop is a general abstract process formulation that captures the key properties of a scheduling problem without going into specific details. We argue that several popular mining methods can be modeled as a rich variant of a k-stage hybrid flow shop, where the flow shop includes a mix of interruptible and uninterruptible tasks, after-lags, machine unavailabilities, and sharing of machines between stages.Then, we propose a Constraint Programming approach to schedule the underground production fleet. We formalize the problem and present a model that can be used to solve it. The model is implemented and evaluated on instances representative of medium-sized underground mines.After that, we introduce travel times of the mobile machines to the scheduling problem. This acknowledges that underground road networks can span several hundreds of kilometers. With this addition, the initially proposed Constraint Programming model struggles with scaling to larger instances. Therefore, we introduce a second model. The second model does not solve the interruptible scheduling problem directly; instead, it solves a related uninterruptible problem and transforms the solution back to the original time domain. This model is significantly faster, and can solve instances representative of large-sized mines even when including travel times.Lastly, we focus on finding high-quality schedules by introducing Large Neighborhood Search. To do this, we present a domain-specific neighborhood definition based on relaxing variables corresponding to certain work areas. Variants of this neighborhood are evaluated in Large Neighborhood Search and compared to using only restarts. All methods and models in this thesis are evaluated on instances generated from an operational underground mine.