Towards Green Freight Transportation Using Train Design Optimization

It is imperative to re-design the freight transport modal mix to ensure a shift from road to rail to limit energy consumption and global GHG emissions. However, one of the main barriers to the shift is the ability of the railways to handle consignments from customers with less than ‘unit’ train loads. In such cases, railways have to combine consignments from different customers to form ‘unit’ trains. Combining consignments is a train design optimization process involving designing a trip plan with the minimum number of trains formed and satisfying a set of conditions. However, manually optimizing train design for high-density freight traffic is challenging and practically impossible. Hence, it is essential to develop an automated train design optimization methodology that railways can quickly implement. Among several conditions of train formation, the two key constraints are the ‘number of work events’ and ‘number of block swaps’. However, the existing literature only considers either one of these two constraints in a single decision-making model. We have proposed a train design optimization method based on a genetic algorithm with a priority generator to simultaneously consider both the above-mentioned constraints. The train design optimization method developed has also been demonstrated using real-life data.