Integrated Optimization Of Train Rescheduling And Locomotive Assignment During COVID-19 Outbreak

The pandemic suppression measures of COVID-19 outbreak significantly hit the railway transportation mode.This disruption to the railway system is marked by a significant passenger volume reduction and moderately availability of information.Therefore,for railway companies to meet the need of epidemic prevention and control,many operational lines were canceled or adjusted.To investigate this train rescheduling and locomotive assignment problem(TRLA),the thesis developed the MIP models to solve the integrated TRLA problems during the COVID-19 outbreak considering operational cost and locomotive utilization efficiency separately.Our study can be utilized as decision support tools for rail operators to deal with the railway disruption and any disruption kinds with the same characteristics.The thesis consists of two main research contents.The thesis starts with introductory parts and the related studies review as contained in first chapter and second chapter respectively.The first main contents as introduced in the third chapter develops an operational cost-oriented TRLA model,which takes into account a full train service(from train starting to finishing the task at the depot)and fixed train operational constraints such as running time,dwelling time,and minimum headway time interval.In addition,it considers the passenger volume while minimizing the operational cost.The three scenarios(reflecting the different COVID-19 outbreak characteristic to full recovery)tests of the model on the Beijing-Tianjin intercity railway prove its CPU efficiency.The second main content as introduced in forth chapter develops an efficiency-based TRLA model,which considers a flexible time window for each operation line and locomotive traction operations and minimizes the number of locomotives utilized with their total idle time for train rescheduling and locomotive assignment respectively.At the same time,a solution algorithm was proposed that determines the minimum locomotive fleet size based on the optimal train rescheduling results;then afterward,it reduces traction idle times for the locomotives.In response to the uncertainty of COVID-19,we also design two tailored approaches for the recovery and removal of operation lines,which can insert and cut operation lines according to the results of the locomotive assignment.We conduct a case study on the Beijing-Tianjin intercity railway from the start of the COVID-19 outbreak in December 2019 to the recovery of operations.In summary,this study established two MIP models to solve the problems of train rescheduling and locomotive assignment during the COVID-19 outbreak,considering two perspectives,namely,the minimum train operating cost and the locomotive utilization efficiency to meet the passengers’ demand.The models solve the uncertainty problem of pandemic prevention and control by inserting or deleting running lines from the train schedule.In the aspect of inserting and cutting operational lines,this study designed two algorithms for inserting and cutting operational lines.The case study showed that the model considering the operating line and the utilization efficiency of the locomotive has better performance in terms of calculation efficiency and extent of applicability.