Paper Info
Title
Heuristic strategies for solving complex interacting stockpile blending problem with chance constraints
Abstract
ABSTRACTThe stockpile blending problem seeks to determine how many tonnages of ore that stockpiles provide and to which parcels. This scheduling model maximizes the volume of valuable material in the final production subject to resource capacities, constraints for mill machines, and customer requirements. Motivated by the uncertainty in the geologic input date which affects optimization, we consider the stockpile blending problem with uncertainty in material grades. A non-linear continuous optimization model developed here to integrate uncertain variables to optimization. We introduce chance constraints that are used to guarantee the constraint is violated with a small probability to tackle the stochastic material grades. We investigate a well-known approach in this paper, which is used to solve optimization problems over continuous space, namely the differential evolution (DE) algorithm. In the experiment section, we compare the performance of the algorithm with the deterministic model and three chance constraint models by using a synthetic benchmark. We also evaluate the effectiveness of different chance constraints.
Year
DOI
Venue
2021
10.1145/3449639.3459382
Genetic and Evolutionary Computation Conference
Keywords
DocType
Citations 
Stochastic mine planning, stochastic optimization, Differential Evolution, chance-constrained optimization
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Yue Xie102.37
Aneta Neumann21412.79
Frank Neumann304.06