Train-scheduling optimization model for railway networks with multiplatform stations

This paper focuses on optimizing the schedule of trains on railway networks composed of busy complex stations. A mathematical formulation of this problem is provided as a Mixed Integer Linear Program (MILP). However, the creation of an optimal new timetable is an NP-hard problem; therefore, the MILP can be solved for easy cases, computation time being impractical for more complex examples. In these cases, a heuristic approach is provided that makes use of genetic algorithms to find a good solution jointly with heuristic techniques to generate an initial population. The algorithm was applied to a number of problem instances producing feasible, though not optimal, solutions in several seconds on a laptop, and compared to other proposals. Some improvements are suggested to obtain better results and further improve computation time. Rail transport is recognized as a sustainable and energy-efficient means of transport. Moreover, each freight train can take a large number of trucks off the roads, making them safer. Studies in this field can help to make railways more attractive to travelers by reducing operative cost, and increasing the number of services and their punctuality. To improve the transit system and service, it is necessary to build optimal train scheduling. There is an interest from the industry in automating the scheduling process. Fast computerized train scheduling, moreover, can be used to explore the effects of alternative draft timetables, operating policies, station layouts, and random delays or failures.Postprint (published version

Optimization of Locomotive Management and Fuel Consumption in Rail Freight Transport

For the enormous capital investment and high operation expense of locomotives, the locomotive management/assignment and fuel consumption are two of the most important areas for railway industry, especially in freight train transportation. Several algorithms have been developed for the Locomotive Assignment Problem (LAP), including exact mathematics models, approximate dynamic programming and heuristics. These previously published optimization algorithms suffer from scalability or solution accuracy issues. In addition, each of the optimization models lacks part of the constraints that are necessary in real-world train/locomotive operation, e.g., maintenance/shop constraints or consist busting avoidance. Furthermore, there are rarely research works for the reduction of total train energy consumption on the locomotive assignment level. The thesis is organized around our three main contributions. Firstly we propose a “consist travel plan” based LAP optimization model, which covers all the required meaningful constraints and which can efficiently be solved using large scale optimization techniques, namely column generation (CG) decomposition. Our key contribution is that our LAP model can evaluate the occurrence of consist busting using the number of consist travel plans, and allows locomotive status transformation in flow conservation constraints. In addition, a new column generation acceleration architecture is developed, that allows the subproblem, i.e., column generator to create multiple columns in each iteration, that each is an optimal solution for a reduced sub-network. This new CG architecture reduces computational time greatly comparing to our original LAP model. For train fuel consumption, we derive, linearize and integrate a train fuel consumption model into our LAP model. In addition, we establish a conflict-free pre-process for time windows for train rescheduling without touching train-meet time and position. The new LAP-fuel consumption model works fine for the optimization of the train energy exhaustion on the locomotive assignment level. For the optimization models above, the numerical results are conducted on the railway network infrastructure of Canada Pacific Railway (CPR), with up to 1,750 trains and 9 types of locomotives over a two-week time period in the entire CPR railway network

Integration planning of freight deliveries into passenger bus networks: exact and heuristic algorithms

With the increasing population living in cities, a growing number of small daily urban freight deliveries are performed, typically by private companies. Recently, more environmentally friendly urban logistics services have emerged to mitigate the negative effects of such activities. One example is the integration of freight deliveries into bus networks, traditionally dedicated to passenger transportation, to perform urban logistics activities within cities. In this paper, the integration of the freight delivery process into the urban bus passenger network is addressed where freight parcels are dropped by clients at bus hubs located outside the city center, transported by bus services from the hub to bus stops located in the city center, and delivered to the destination address by a last mile operator. Since bus vehicles supporting both passenger and freight flows need to be physically adapted, the aim is to support the decision-maker to select the minimum number of bus services that must be adapted for freight transportation. The optimization problem considers the freight demand uncertainty in terms of number of freight parcels, destination address, delivery time windows and last mile operator constraints which are modelled by a set of demand scenarios. An exact method based on an integer linear programming (ILP) and two heuristic algorithms based on a greedy randomized adaptive search procedure (GRASP) are proposed. The results show that the proposed optimization methods are efficient, giving valuable insights to stakeholders, in the fields of policy and practice, for the strategic decision of selecting the minimum number of buses to be physically adapted for freight transportation. In particular, the results show that all proposed optimization methods are of interest in practice since the type of problem instances for which each method is more efficient is clearly identified in the obtained computational results. Moreover, in the early stages of the integrated passenger and freight flows service, the impact on the required number of adapted bus services is mainly given by the last mile operator capacity of delivering freight from bus hubs to final parcel destinations, while the other factors (delivery time windows and distributions parcel destination addresses) do not have a significant impact on the required number of bus services.publishe

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Modeling and Solving of Railway Optimization Problems

The main aim of this work is to provide decision makers suitable approaches for solving two crucial planning problems in the railway industry: the locomotive assignment problem and the crew scheduling problem with attendance rates. On the one hand, the focus is on practical usability and the necessary integration and consideration of real-life requirements in the planning process. On the other hand, solution approaches are to be developed, which can provide solutions of sufficiently good quality within a reasonable time by taking all these requirements into account

A mixed integer linear programming model with heuristic improvements for single-track railway rescheduling problem

A rescheduling algorithm for trains on a single-track railway was developed in case of disturbances that would cause conflicts between trains. This algorithm is based on mixed integer linear programming (MILP) with speed-up routines. The model considers station capacities explicitly (i.e., the number of available tracks for meeting and overtaking operations). Because the model is too hard for the solvers (CPLEX in this study) to tackle, three speed-up routines were devised when rescheduling trains. These routines are a greedy heuristic to reduce the solution space, using the lazy constraint attribute of the solver and a multiobjective approach to find a good initial feasible solution that conforms to actual railway operation. The algorithm was tested on a hypothetical rail line for different sizes of timetable instances with disturbed trains in a maximum two-hour time horizon. It managed to solve the hardest instances within a three-minute time limit thus minimizing the total weighted delay of rescheduled trains. The optimality gap metric is used to show the effectiveness and efficiencies of the speed-up heuristics developed

Freight and passenger railway optimization

Das Ziel dieser Arbeit war es, einen Überblick über die aktuellen Beiträge der Literatur in den Bereichen der Eisenbahnlogistik sowohl im Güter- als auch im Personenverkehr zu geben. Während sich der Güterverkehr mit Problemen der Zusammenstellung der Züge und Waggons beziehungsweise der Verteilung der Leerfahrzeuge auseinander setzte, beschäftigte sich die Eisenbahnlogistik im Bereich des Personenverkehrs mit Optimierungsmodellen bezüglich Eisenbahnlinienplanung, Erstellung eines Fahrplanes, Inbetriebnahme von Fahrzeugen und Besatzungs- und Einsatzplanung. Die Bereiche der Eisenbahnlogistik haben in der Literatur eindeutig an Aufmerksamkeit gewonnen. In der Folge war es schwierig eine Auswahl aus dieser Vielfalt an Beiträgen zu treffen. Deshalb versucht diese Arbeit nur einen kurzen Einblick über einige wichtige Beiträge der letzten Jahre im Bereich der Eisenbahnlogistik zu geben. Aufgrund hochentwickelter mathematischer Techniken und deren Lösungsmöglichkeiten, die in den letzten Jahren aufgekommen sind, war es nun möglich die komplizierten Modelle der Eisenbahnlogistik in einer vernünftigen Zeit zu lösen. Darüber hinaus wurde ein Trend zur Entwicklung effizienterer entscheidungsunterstützender Hilfsprogramme für reale Gegebenheiten der Eisenbahnlogistik beobachtet. Im Großen und Ganzen sollten in Zukunft stärker integrierte Modelle der Eisenbahnplanung und Routenplanung entwickelt werden um robuste Lösungen und Methoden zu fördern.The aim of this work was to provide a survey of recent contributions about freight and passenger transportation. Whereas passenger optimization models considered problems such as line planning, train timetabling, platforming, rolling stock circulation, shunting and crew scheduling, freight transportation dealt with issues concerning car blocking, train makeup, routing, and empty car distribution. The field of rail transportation has clearly received attention resulting in a diversity of literature contribution. As it was difficult to handle the large amount of papers, this work is trying to give a short review of some important contributions made in recent years. Due to the increase in more sophisticated mathematical techniques, constant refinements in development of the models were made that were able to deal with larger problems. In addition, a trend towards more efficient transportation support systems was observed taking robustness into account. In addition, solution approaches that can deal with larger disturbances of the rail environment in a considerable speed and time, have received attention. Thus, future research can be done to develop more integrated models of scheduling and routing problems of train and passenger transportation to provide robust solutions and problem solving methods that handle disturbances of rail environment

Dynamic bulk freight train scheduling in an uncongested rail network

Dissertation for the degree of Master of Science University of the Witwatersrand Johannesburg. April 2013Many academic works in the train scheduling environment concentrate on optimizing movements of resources through the physical network. To opti- mize bulk freight lines, algorithms must provide a feasible schedule given the available resources, basic operational constraints and varying demand while ensuring resource allocations that minimise total cost. To be usable the al- gorithm must run within reasonable time limits. This dissertation focuses on the bulk freight train scheduling problem of full loads without track conges- tion but extends to cover operational constraints as well as exible resource allocation and hubs. A problem outline is given wherein the constraints and decision variables are well de ned followed by a review of current literature. An exact formation of the problem is given with benchmarking on small data sets. A genetic algorithm is used to solve for schedules on larger problem data sets. The algorithm was successfully implemented on the 60Mt Coal Line in South Africa which provided notable improvements in e ciencies. Discussion and results are provided

A chance-constrained stochastic approach to intermodal container routing problems

We consider a container routing problem with stochastic time variables in a sea-rail intermodal transportation system. The problem is formulated as a binary integer chance-constrained programming model including stochastic travel times and stochastic transfer time, with the objective of minimising the expected total cost. Two chance constraints are proposed to ensure that the container service satisfies ship fulfilment and cargo on-time delivery with pre-specified probabilities. A hybrid heuristic algorithm is employed to solve the binary integer chance-constrained programming model. Two case studies are conducted to demonstrate the feasibility of the proposed model and to analyse the impact of stochastic variables and chance-constraints on the optimal solution and total cost