Optimized Train Dispatching and Rescheduling During a Disruption in a Bottleneck Section

Abstract

Abstract Railway transportation is nowadays becoming one of the most preferred mode of transport due to its safety, capacity and reliability; the capital cost for the construction of the railway infrastructure is however very high and is characterized by high rigidity as the track layout is fixed; therefore there is need to optimally use the available infrastructure. Minor delays arising from a simple disruptions or even a single train failure can have massive impacts in terms of overall delays for subsequent trains using the track facility if not solved amicably. Disruptions can be attributed to power outages, mechanical failures, derailments, accidents or even environmental factors. In a case of multiple uncertain perturbations happening in a busy complex railway network, where there are many trains requesting to use the available track resources concurrently, there will be massive delays which has a lot of negative operational and economic implications as well as passengers’ dissatisfaction. A mathematical model that is; a mixed-integer linear programming formulation is modelled to minimize total time delays in case of a set of multiple disruptions occurring on a busy track section i.e. bottleneck section, the model is formulated with consideration of sets of constraints factoring in feasible routes and safety margins and other operational dynamics such as dwell times to achieve optimal use of the available infrastructure. A number of numerical experiments based on arbitrary data and real network data are carried out to verify the effectiveness and efficiency of the proposed model. Performance of the designed model is evaluated and results are validated, the results obtained shows that the model offers an efficient rescheduled trains operation plan during disruptions, furthermore the performance of Fmincon solver and genetic algorithms (GA) are compared and their robustness evaluated, GA shows better performance during multiple disruption scenario.