Optimization of rail transport is complex because of the industry’s multiple constraints. The transport of containers is very particular since it is characterized in addition to its specificities as a product to be loaded and transported by a strong instability of the demand. So far research in this area has dealt only with the separate treatment of the train load and transport problem. The present study focuses on optimizing resources facing unstable demand for the combined problem. Mathematical models are proposed to assign customers demands to wagons and for railcars allowance per axis depending on the available park, the locomotive capacity and the train length. An algorithm for the train load problem is also suggested. The models have been tested to measure their efficiency by comparing them to an existing train planning model and to manual assignment adopted in the rail industry. Some test results are finally reported to show how a novel formulation can simplify the resolution of a complex problem.
This paper addresses a problem faced by maintenance service providers: performing maintenance activities at the right time on geographically distributed machines subjected to random failures. This problem requires determining for each technician the sequence of maintenance operations to perform to minimize the total expected costs while ensuring a high level of machine availability. To date, research in this area has dealt with routing and maintenance schedules separately. This study aims to determine the optimal maintenance and routing plan simultaneously. A new bi-objective mathematical model that integrates both routing and maintenance considerations is proposed for time-based preventive maintenance. The first objective is to minimize the travel cost related to technicians’ routing. The second objective can either minimize the total preventive and corrective maintenance cost or the failure cost. New general variable neighborhood search (GVNS) and variable neighborhood descent (VND) algorithms based on the Pareto dominance concept are proposed and performed over newly generated instances. The efficiency of our approach is demonstrated through several experiments. Compared to the commercial solver and existing multi-objective VND and GVNS, these new algorithms obtain highly competitive results on both mono-objective and bi-objective variants.
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