A Decomposition-Based Heuristic for Collaborative Scheduling in a Network of Open-Pit Mines

Abstract

W e consider the short-term production scheduling problem for a network of multiple open-pit mines and ports. Ore produced at each mine is transported by rail to a set of ports and blended into signature products for shipping. Consistency in the grade and quality of production over time is critical for customer satisfaction, whereas the maximal production of blended products is required to maximise profit. In practice, short-term schedules are formed independently at each mine, tasked with achieving the grade and quality targets outlined in a medium-term plan. However, because of uncertainty in the data available to a medium-term planner and the dynamics of the mining environment, such targets may not be feasible in the short term. We present a decomposition-based heuristic for this short-term scheduling problem in which the grade and quality goals assigned to each mine are collaboratively adapted-ensuring the satisfaction of blending constraints at each port and exploiting opportunities to maximise production in the network that would otherwise be missed.

A Decomposition-Based Algorithm for the Scheduling of Open-Pit Networks Over Multiple Time Periods

“…The decision of what to mine in each period has a direct influence on what regions of ore will be accessible later in the horizon. These complexities do not arise in the single period problem of Blom et al (2014). Full utilisation of available processing capacity is not enforced in this earlier work.…”

“…We extend existing work by Blom et al (2014) in which a decomposition-based algorithm for the single time period MMPP was developed. In this paper, we consider the significantly more complex multiple time period setting, in which multiple time period schedules must be generated for each mine that are both feasible to enact, and lead to correct blending at the ports.…”

“…In this paper, we consider the significantly more complex multiple time period setting, in which multiple time period schedules must be generated for each mine that are both feasible to enact, and lead to correct blending at the ports. We incorporate additional constraints into our modelling of the MMPP, not present in the single time period model of Blom et al (2014), to develop a higher fidelity representation of operational behaviour at each Blom et al (2014) introduce an algorithm for discovering high quality solutions to a MINLP model of the single period MMPP. In this algorithm, the open-pit production scheduling task is decomposed into multiple subproblems: a set of optimisation problems solved on behalf of a set of mines (generating a set of candidate production schedules for each mine); and an optimisation problem solved on behalf of a network of ports (selecting a candidate schedule to be enacted at each mine, and routing trains of ore between mines and ports).…”

“…In this paper, we adapt this algorithm to find high quality solutions to the MTP-MMPP MINLP. The key differences between the single period algorithm of Blom et al (2014), and the multiple time period algorithm of this paper, lies in the nature and implementation of the mine-and port-side optimisation problems, and the feedback mechanism that exists between these problems in each iteration. A diagram of the main components of the algorithm is shown in Figure 1.…”

“…The algorithm presented in this paper, to the best of our knowledge, is the first work to solve an integrated production scheduling problem, involving multiple open-pit mines and a horizon of multiple periods, where the grade and quality of ore to be produced by each mine is not known a priori, but determined as part of the optimisation. We show that the algorithm of Blom et al (2014) can be successfully adapted to the more complex multiple time period setting. Most crucial in this transition is the existence of a sufficiently fast and reliable method to generate multiple time period schedules for each mine in our network.…”