An internet of things-enabled decision support system for freight transportation: A case study of Indian special freight transport operator

Abstract

Railway organizations are spending on digitizing rail freight transportation for better resource utilization and revenue generation. In 2010, the Indian Railway (IR) proposed a “Special Freight Train Operator” scheme to build an IR stake in freight transportation in high-capacity rakes. IR allowed freight train operators (FTOs) and manufacturers to invest in wagons and take benefit of the largest rail network to move selected goods to their end customers. In the absence of an optimized support system, FTOs are often confronted with the decision of rolling stock (rake) scheduling, rake assignment, and rescheduling on a real-time basis. Today, most of the rakes have GPS devices. Thus, railway train management systems readily provide data to create a dynamic optimization model for FTOs using IoT-based real-time information. We first formulate a mixed-integer linear programming (MILP) model that aims at revenue maximization incorporating optimal rake assignment and optimal rake scheduling. Furthermore, to incorporate real-time GPS data, we propose an “IoT enabled real-time rake schedular-reschedular heuristic” for rescheduling. The computational investigations exhibit that the proposed heuristic performs effectively both in terms of run time and the quality of the solution. These models will help FTOs smoothly run day-to-day businesses and lead to better revenue realization in the calendar month by increasing the number of trips.