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The rake movements in rail freight transportation are executed by dynamically allocating empty rakes to observed demands. This work proposes an alternate cycle planning strategy based on the average demand data. The system is modeled as the Pickup and Delivery Problem with customer specified origin and destination and multiple full truckload demands by each customer. Initially, an Exact Formulation of the problem that gives optimal cycles is presented. A modified Order First Split Second Heuristic (OFSS) is proposed to solve the deterministic version of this problem where a giant tour is constructed by assigning a value to each node based on the greedy path from that location. The splitting algorithm forms multiple cycles satisfying maximum cycle time constraint from the Giant Tour. A heuristic solution obtained by splitting optimal giant tour is also determined to study the performance of proposed heuristic. Considering the possibility of deviation of actual demands from average, an adaptive algorithm that modifies the planned cycles to accommodate the changes in demand is presented. Computations show that the heuristic gives near-optimal solution in terms of net revenue and the number of vehicles required. Testing this algorithm on dynamic demands show that the initial cycle plan can be followed with minor modification if the Degree of Dynamism of the system is small. The proposed algorithm is tested on Indian freight rail data and the improvements in rake utilization are noted. [ABSTRACT FROM AUTHOR]

Rescheduling disrupted railway traffic is computationally hard even for small problem instances. Disruptions may not be known beforehand and can manifest themselves even when trains are en-route, and they are usually resolved by human experts. Wide geographical distribution, a dynamically changing environment, complex interdependencies between multiple components, operational criticality and uncertainty being characteristic of railway transportation, human resolutions are inconsistent, scale-inefficient and potentially infeasible with deadlocks. We present a multi-agent system (MAS) model for dynamic and real-time rescheduling (DRR) of bi-directional railway traffic on a single track in this paper. A computational framework to dynamically dispatch the disrupted trains in real-time, based on instantaneous system parameters and to reschedule conflicting trains with inherent deadlock avoidance is incorporated in the agents’ model. A learning architecture is implemented as a proof-of concept to resolve disruptions quickly and to enhance autonomy. The model is evaluated against integer optimal solutions generated by a Mixed-Integer Linear Programming (MILP) model using realistic data. Detailed discussions on architecture, implementation using JADE (Java Agent DEvelopment) toolkit, experimental results, performance analysis, evaluation of the model, insights and limitations are reported. The numerical performance measures of the model are total weighted delay of all trains at their destination terminals and computational time for resolution. The distinguishing research contributions in this paper are a MAS architecture for railway rescheduling, dynamic dispatch priority assignment using bidding and a learning procedure that enhances autonomy. [ABSTRACT FROM AUTHOR]

Abstract: Revenue management is widely practiced in the transport industry, but the bulk of the published literature deals with the airline industry. We consider the case of passenger services in the premium segment of Indian Railways (IR) and illustrate an application of the principles of revenue management. The strategy of overbooking is interpreted in terms of waitlist management by IR and cancellation action of customers. An attempt is made to derive elasticity estimates between key mode choices internal to the railways and finally, revenue management through differential pricing is suggested as a means to increase revenue on average. [Copyright &y& Elsevier]