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Payload-based fleet optimization for rail cars

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Published on Tuesday, 25 April 2017

Rail car fleets are significant investments in the chemical industry. Therefore, chemical companies constantly try to optimize the fleets as part of major working capital optimization initiatives.

The Luxembourg Centre for Logistics and Supply Chain Management at the University of Luxembourg has contributed to study a complex rail car fleet optimization problem and to develop a model resulting in substantial cost savings.

In their paper, “Payload-based fleet optimization for rail cars in the chemical industry” published in European Journal of Operational Research, Prof. Steffen Klosterhalfen and his co-authors conclude that BASF has realised cost savings of nearly 2 million euros so far through the fleet redesign.

The authors study a complex rail car fleet optimisation problem observed at BASF and an approach to tackle the problem in a sequential way. They develop a mixed integer non-linear programming model to derive suggestions to the questions:

 - How many different rail car sizes in terms of volume are appropriate and what are their optimal volumes?

 - For each volume, what is the optimal number of rail cars of this size?

The model formulation takes into account various real-world restrictions concerning the volume and weight of the rail cars as well as the tracks that they run on. They propose two solution methods. First, they reformulate the non-linear terms, which results in a mixed integer linear programming (MILP) model. This model can be solved in a reasonable amount of time, if the number of different rail car sizes is small (2 or 3). Second, they develop a two-step approach that uses an enumeration scheme in the first stage to find good initial solutions that are fed into the MILP formulation in the second stage. This approach solves the model for larger numbers of different rail car sizes (4 or 5).

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