  Transporting ore from mines to ports is of significant interest in mining
supply chains. These operations are commonly associated with growing costs and
a lack of resources. Large mining companies are interested in optimally
allocating their resources to reduce operational costs. This problem has been
previously investigated in the literature as resource constrained job
scheduling (RCJS). While a number of optimisation methods have been proposed to
tackle the deterministic problem, the uncertainty associated with resource
availability, an inevitable challenge in mining operations, has received less
attention. RCJS with uncertainty is a hard combinatorial optimisation problem
that cannot be solved efficiently with existing optimisation methods. This
study proposes an adaptive population-based simulated annealing algorithm that
can overcome the limitations of existing methods for RCJS with uncertainty
including the premature convergence, the excessive number of hyper-parameters,
and the inefficiency in coping with different uncertainty levels. This new
algorithm is designed to effectively balance exploration and exploitation, by
using a population, modifying the cooling schedule in the Metropolis-Hastings
algorithm, and using an adaptive mechanism to select perturbation operators.
The results show that the proposed algorithm outperforms existing methods
across a wide range of benchmark RCJS instances and uncertainty levels.
Moreover, new best known solutions are discovered for all but one problem
instance across all uncertainty levels.
