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.

铁路组织正在将铁路货运数字化，以更好地利用资源和创收。2010 年，印度铁路 (IR) 提出了一项“特殊货运列车运营商”计划，以建立 IR 在大容量货物运输方面的股份。IR 允许货运列车运营商 (FTO) 和制造商投资货车，并利用最大的铁路网络将选定的货物运送给最终客户。在缺乏优化的支持系统的情况下，FTO 经常面临实时决定机车车辆 (rake) 调度、rake 分配和重新调度的问题。今天，大多数耙子都有 GPS 设备。因此，铁路列车管理系统很容易提供数据，使用基于物联网的实时信息为 FTO 创建动态优化模型。我们首先制定了一个混合整数线性规划 (MILP) 模型，该模型旨在结合最优 rake 分配和最优 rake 调度来实现收入最大化。此外，为了整合实时 GPS 数据，我们提出了一种“支持物联网的实时 rake schedular-reschedular heuristic”用于重新调度。计算研究表明，所提出的启发式算法在运行时间和解决方案质量方面都有效。这些模式将帮助 FTO 顺利开展日常业务，并通过增加出行次数在日历月内实现更好的收入实现。我们提出了一种“支持物联网的实时rake schedular-reschedular heuristic”来重新调度。计算研究表明，所提出的启发式算法在运行时间和解决方案质量方面都有效。这些模式将帮助 FTO 顺利开展日常业务，并通过增加出行次数在日历月内实现更好的收入实现。我们提出了一种“支持物联网的实时rake schedular-reschedular heuristic”来重新调度。计算研究表明，所提出的启发式算法在运行时间和解决方案质量方面都有效。这些模式将帮助 FTO 顺利开展日常业务，并通过增加出行次数在日历月内实现更好的收入实现。