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Optimizing a mineral value chain with market uncertainty using benders decomposition

Jian Zhang, Barrie R. Nault and Roussos G. Dimitrakopoulos

European Journal of Operational Research, 2019, vol. 274, issue 1, 227-239

Abstract: A Benders decomposition-based method is developed to simultaneously optimize upstream and downstream operations of a mineral value chain. In each iteration of the proposed method, the mineral value chain optimization model is decomposed to a master problem that only includes the variables that determine the upstream mine production schedule, and a subproblem that includes all other variables that define the downstream material flow and processing plan. In order to reduce the master problem in each iteration, mining blocks representing mineral deposits are dynamically aggregated based on the dual solution of the subproblem. The production schedule obtained based on the aggregated scheduling units is then improved through a moving-window amelioration method. By observing the results of a series of numerical tests, we show that the proposed method efficiently optimizes a mineral value chain by synchronizing the upstream mine production scheduling as well as the downstream material flow and process planning. The numerical tests also show that ignoring market uncertainty results in profits being underestimated because of the underestimated value of low-grade material. To adapt to market uncertainty, the stochastic optimizer suggests greater investment to increase capacity in the processing plant and a different long-term mine production schedule.

Keywords: OR in natural resources; Benders decomposition; Mineral value chain; Market uncertainty (search for similar items in EconPapers)
Date: 2019
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Handle: RePEc:eee:ejores:v:274:y:2019:i:1:p:227-239