  The Stockpile blending problem is an important component of mine production
scheduling, where stockpiles are used to store and blend raw material. The goal
of blending material from stockpiles is to create parcels of concentrate which
contain optimal metal grades based on the material available. The volume of
material that each stockpile provides to a given parcel is dependent on a set
of mine schedule conditions and customer demands. Therefore, the problem can be
formulated as a continuous optimization problem. In the real-world application,
there are several constraints required to guarantee parcels that meet the
demand of downstream customers. It is a challenge in solving the stockpile
blending problems since its scale can be very large. We introduce two repaired
operators for the problems to convert the infeasible solutions into the
solutions without violating the two tight constraints. Besides, we introduce a
multi-component fitness function for solving the large-scale stockpile blending
problem which can maximize the volume of metal over the plan and maintain the
balance between stockpiles according to the usage of metal. Furthermore, we
investigate the well-known approach in this paper, which is used to solve
optimization problems over continuous space, namely the differential evolution
(DE) algorithm. The experimental results show that the DE algorithm combined
with two proposed duration repair methods is significantly better in terms of
the values of results than the results on real-world instances for both
one-month problems and large-scale problems.
