Capacity Alignment Planning for a Coal Chain: A Case Study

We study a capacity alignment planning problem for a coal chain. Given a set of train operators, a set of train paths, and a terminal comprising of a dump station and a set of routes from the dump station to the stockyard, we seek a feasible assignment of train operators to train paths, to time slots at the dump station, and to routes. The assignment must maximize the number of system paths in the resulting schedule and the schedule should perform well with respect to various performance criteria. We model the problem as a mixed-integer conic programme (MICP) with multiple objectives which we solve using a hierarchical optimization procedure. In each stage of this procedure we solve a single objective MICP. Depending upon whether we evaluate the associated performance criteria under a 2-or 1-norm we reformulate the MICP as either a mixed-integer second-order cone programme or as a mixed-integer linear programme respectively, and can streamline the hierarchical optimization procedure by exploiting properties of the model or observed behaviour on practical instances. We compare the performance of the procedure under the different norms on a real instance of the problem and find that the quality of the solutions found by the faster 1-norm procedure compare well to the solution found under the 2-norm

A new constraint programming approach for optimising a coal rail system

Because of the bottlenecking operations in a complex coal rail system, millions of dollars are costed by mining companies. To handle this issue, this paper investigates a real-world coal rail system and aims to optimise the coal railing operations under constraints of limited resources (e.g., limited number of locomotives and wagons). In the literature, most studies considered the train scheduling problem on a single-track railway network to be strongly NP-hard and thus developed metaheuristics as the main solution methods. In this paper, a new mathematical programming model is formulated and coded by optimization programming language based on a constraint programming (CP) approach. A new depth-first-search technique is developed and embedded inside the CP model to obtain the optimised coal railing timetable efficiently. Computational experiments demonstrate that high-quality solutions are obtainable in industry-scale applications. To provide insightful decisions, sensitivity analysis is conducted in terms of different scenarios and specific criteria