Abstract
In Indian Railways, container train operators (CTOs) run intermodal trains. The success of a CTO depends on it providing timely delivery of containers at low haulage cost. The CTO must utilize its rolling stock efficiently, select containers, and assign these containers to wagons optimally considering multiple conflicting requirements. I discuss a mathematical programming–based approach we developed for a major CTO for train-load planning, which reduces the average cost of container haulage and increases the timeliness desired by customers. After being used to plan more than 1,000 trains, my model has been estimated to save about 2% in rail haulage cost, which corresponds to an annual saving of more than 300 million Indian rupees for Indian Railways trains. This study has led to a remarkable turnaround in the operations strategy of the operator, with a shift in emphasis from increasing train utilization to maximizing the contribution to profit.
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