Integrated Line Planning and Train Scheduling for an Urban Rail Transit Line

This work investigates an unmanned aerial vehicle (UAV) assisted IoT system, where a UAV flies to each foothold to collect data from IoT devices, and then return to its start point. For such a system, we aim to minimize the energy consumption by jointly optimizing the deployment and flight trajectory of UAV. It is a mixed-integer non-convex and NP-hard problem. In order to address it, a bilevel optimization approach is proposed, where an upper-level method aims to optimize the deployment of UAV and a lower-level one aims to plan UAV flight trajectory. Specifically, the former optimizes the number and locations of footholds of UAV. This work proposes an improved dandelion algorithm with a novel encoding strategy, in which each dandelion represents a foothold of UAV and the entire dandelion population is seen as an entire deployment. Then, two mutation strategies are designed to adjust the number and locations of footholds. Based on the footholds of the UAV provided by the former, the latter transforms flight trajectory planning into a traveling salesman problem (TSP). This work proposes an iterated greedy algorithm to solve it efficiently. The effectiveness of the proposed bilevel optimization approach is verified on ten instances, and the experimental results show that it significantly outperforms other benchmark approaches.

Train scheduling and circulation planning in urban rail transit lines

A new method is proposed for single-track railway freight train timetable with two-stage solution of train scheduling problem. The method describes train scheduling problem in matrix with the "signaling block" concept. The matrix represents entire block occupancy of trains between inter-stations and internal-stations while decomposing individual train occupancy, making optimization method more flexible in design and use. The "two-stage solution" refers to that of train timetable with "conflict solution" and "timetable optimization." At the beginning, related data for trains are entered to build the initial timetable, which is then transferred and saved as the matrix format. All conflicts between interstations and internal-stations are located; solutions are made with manual scheduling to generate a feasible timetable. In the second stage, departure and stop time of freight trains, and passenger trains if necessary, are adjusted with Fuzzy system. Before optimization, storage is made in ascending amount of extra-time percent in freight train schedule. Execution is made subject to pre-ordered results during optimization for computer program acceleration of current manual spotting solution to find reasonable and feasible solution. The two-stage method also takes less time than the regular railway timetable design methods in cost saving of manual labor and providing higher operation performance and efficiency.

Optimisation of train schedules to minimise transit time and maximise reliability : Higgins, Andrew, Ph.D. Queensland University of Technology, 1996. 260 pp. (available from Inter-library loans, Gardens Point Campus, Queensland University of Technology, P.O. Box 2434, Brisbane 4001, Australia)

Efficiency in public and freight transportation systems is of great importance for a society. Railways can other high capacity and relatively low environmental impact, but require that several technical systems are tuned and operate well. Specifically there is a tight interdependency between infrastructure and trains. The consequences are that all subsystems must be maintained and that the coordination of infrastructure activities and train operations is essential. Railway infrastructure maintenance and train services should ideally be planned together, but practice and research about railway scheduling has historically focused mainly on train operations and timetabling while maintenance planning has received less attention | and little research have considered the joint scheduling of both types of activities. Instead the traditional approach has been a sequential and iterative planning procedure, where train timetabling often has precedence over infrastructure maintenance. This thesis studies how maintenance windows, which are regular time windows reserved for maintenance work, can be dimensioned and jointly scheduled with train services in a balanced and efficient way for both maintenance contractors and train operators. Mathematical methods are used, with the aim of advancing the knowledge about quantitative methods for solving such coordination problems. The thesis contributes with new optimization models that jointly schedule maintenance windows and train services, investigates the solving efficiency of these models, and studies crucial extensions of the planning problem | primarily for the consideration of maintenance resources. Furthermore, the models are applied to, verified and validated on a demanding real-life problem instance. The main results are that integrated and optimal scheduling of maintenance windows and train services is viable for problems of practical size and importance, and that substantial maintenance cost savings can be achieved with such an integrated approach as compared to a traditional sequential planning process. The thesis consists of an introduction and overview of the research, followed by six papers which present: (1) A cost benefit model for assessment of competing capacity requests at a single location; (2) An optimization model for integrated scheduling of both maintenance windows and train services; (3) Mathematical reformulations that strengthen the optimization model; (4) Extensions for handling resource considerations and cyclic schedules; (5) A case study for a major single track line in Sweden; and (6) A mathematical study of length-restricted sequences under cyclic conditions.

This paper presents a train scheduling and a rescheduling models based on the generation of an alternative graph of the railway network. In the first model, the train scheduling problem has been solved to define the optimal timetable for a set of trains with the objective to minimize the travel times of passengers to reach their destination, outlining the related sequence of nodes and arcs of the graph to be covered by each group of passengers. Subsequently, a train rescheduling model is applied on an optimal timetable became infeasible due to unpredicted failures or disturbances introduced on the network. In this second approach, the objective is restore the railway service quality by reducing the delays for trains and passengers. The main contribution of this paper is to implement railway scheduling and rescheduling problem by linear programming formulations introducing in the second approach the innovative concept to prioritize the train that can access on the block section in case of conflicts on the network. The performances of the solution methods have been tested on real train timetable data related to a portion of railway in Liguria Region, in Italy.

A new solution framework for the limited-stop bus service design problem

Inventory control in supply chains: Alternative approaches to a two-stage lot-sizing problem

Distribution expansion planning in the presence of plug-in electric vehicle: A bilevel optimization approach

Risk management model for simultaneous participation of a distribution company in Day-ahead and Real-time markets

PV assisted electric vehicle charging station considering the integration of stationary first- or second-life battery storage

Modeling the first train timetabling problem with minimal missed trains and synchronization time differences in subway networks

We propose a network flow model to study problems in commuting traffic in the railway network of the Tokyo Metropolitan area. Through this network, about seven million passengers commute, and about 7,500 trains transport them in morning rush hours. Trains are usually crowded and subject to delay caused by overcrowded passengers. In addition to the huge traffic volume, such facts intensify the situation that the commuters concentrate in commuting during short time period, traveling radial lines directing to the central district, and choosing a “better (higher speed)” train service. In order to relieve such conditions, not only investments in hardware but also soft approach are important to fully utilize the existing capacity of the network to average the number of passengers across the trains. Our model consists of a time–space network (or schedule-based network) and detailed traffic data on route choice of commuters. The former has been intensively studied to consider traffic problems where each vehicle service is operated according to a timetable. Using this network, every train services and actions of passengers can be represented by nodes and edges of the network. The latter is the questionnaire survey of commuting traffic in Metropolitan area being done in every five years. We use the survey conducted in 2005, which has about 140,000 respondents. Another important point of our model is that it is able to consider the whole railway network universally in one system.

Heavy-duty commercial electric vehicle (HDEV) charging stations, such as for freight trucks, must handle large peak power demands. Installing on-site energy storage can reduce the peak charging demand to avoid expensive and oversized utility-managed distribution equipment. To ensure optimal design of charging infrastructure, the trade-off between energy storage size and grid equipment ratings should be considered. This paper presents a bi-level multi-objective optimization framework to discover Pareto optimal designs, under the constraint of optimally sized power electronic converters and realistic power loss models. Under these considerations, the bi-level approach can greatly simplify the design process by breaking up charging station optimization into a system-level problem and multiple converter-level problems. Using industry-based HDEV arrival times and charging conditions, this bi-level approach is demonstrated for a 9port charging station. The resulting Pareto front showcases equipment sizing trade-offs that are necessary for informed charging infrastructure development decisions. The bi-level optimization Pareto front is compared the Pareto fronts of traditional, fixed efficiency converter models.

Planning a public transportation system is a multi-objective problem which includes among others line planning, timetabling, and vehicle scheduling. For each of these planning stages, models are known and advanced solution techniques exist. Some of the models focus on costs, others on passengers’ convenience. Setting up a transportation system is usually done by optimizing each of these stages sequentially.In this paper we argue that instead of optimizing each single step further and further it would be more beneficial to consider the whole process in an integrated way. To this end, we develop and discuss a generic, bi-objective model for integrating line planning, timetabling, and vehicle scheduling. We furthermore propose an eigenmodel which we apply for these three planning stages and show how it can be used for the design of iterative algorithms as heuristics for the integrated problem. The convergence of the resulting iterative approaches is analyzed from a theoretical point of view. Moreover, we propose an agenda for further research in this field.