Capacity planning in supply chains of mineral resources
The paper addresses the existing gap in the literature on the optimisation in capacity planning for mineral supply chains. The presented optimisation procedure aims at minimising the cost of infrastructure expansion for any given scenario of future demand. The optimisation procedure is designed as a matheuristic  a hybridisation of mixed integer linear programming (MILP), and a simulated annealing based scheduler. The optimisation procedure is iterative in nature and has the following distinctive features. Each iteration starts with generating a MILP model and finding a minimal cost infrastructure expansion for this model. Then, the scheduler analyses the MILP solution by constructing a schedule. In constructing this schedule, the scheduler reduces its search space using the MILP solution. The scheduler identifies bottlenecks in the infrastructure, which are used for generating a new MILP model at the next iteration. The MILP and the scheduler use different levels of data aggregation and their interaction mechanism is designed as a solution process of a bi-criteria optimisation problem. The computational experiments on data, originating from the worlds largest coal exporter, shows the ability of the developed matheuristic to solve industrial-scaled instances of the problem.
capacity planning matheuristic mixed integer linear programming simulated annealing hybrid heuristic