Abstract
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.
Highlights
Introduction and literature reviewRail transport is a unique production of services that involves several stakeholders at once to respond to customers orders for different products to be transported
The objective of the train planning model (TPM) is to decide where each particular container should be loaded on a given train while minimizing the weighted sum of the number of wagons used in the train loading plan
The paper deals with the train load and transport planning problem
Summary
Rail transport is a unique production of services that involves several stakeholders at once to respond to customers orders for different products to be transported. Zhao et al.[4] propose a mathematical model for the empty cars allocation problem in railway networks with dynamic demands to minimize the total cost incurred by transferring and storing empty cars in different stages of the demand. They propose a genetic algorithm to generate approximate optimal solutions. The integer program proposed seek to minimize operating, handling and yard storage costs while meeting on-time delivery requirements They develop a decomposition procedure that yields near-optimal solutions and a method for providing tight bounds of the objective function. An algorithm suggested provides an optimal solution for the train load problem
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