Assessing the Efficiency of Intermodal Freight Transport Chains Using a Modified Network DEA Approach
Formulating effective policies to promote intermodal freight transport (IFT) market share calls for performance measurement models that help benchmark the efficiency of transport chains, and identify the points for improvements. Despite the importance of efficiency measurement, studies on the performance measurement of IFT chains are quite limited. This paper presents a modified network data envelopment analysis (NDEA) method to measure the performance of different intermodal freight transport chains inside a freight network. NDEA is an extension of traditional data envelopment analysis (DEA) which is used to evaluate the performance of multi-divisional systems. It has been used before in other sectors as well. The application of this method to the IFT chains, what can be seen as a sequence of divisions, involves two main challenges. The first challenge is to identify the number of divisions because, in an IFT network, we may have different IFT chains with different structures, where the number of sequential transshipment and transportation activities varies. The second challenge is defining a relevant intermediate service that connects the various divisions. Both challenges are discussed in the paper and the original formulation is extended to cope with these challenges. We also illustrate the presented model by applying it to a sample of 10 IFT chains in a European IFT network. The results of the model are used to compare different IFT chains and also to analyze the sources of inefficiencies. Based on the observations, a general conclusion would be that in most of the IFT chains, the transshipment activities are less efficient activities in a chain, and therefore, the focus of improvement efforts in the majority of corridors should be on the terminal divisions.
|Keywords||DEA, Freight transport, efficiency|
Saeedi, H, Behdani, B, Wiegmans, B.W, & Zuidwijk, R.A. (2018). Assessing the Efficiency of Intermodal Freight Transport Chains Using a Modified Network DEA Approach. Retrieved from http://hdl.handle.net/1765/109086