Computer-based decision support for railway traffic scheduling and dispatching: A review of models and algorithms

This paper provides an overview of the research in railway scheduling and dispatching. A distinction is made between tactical scheduling, operational scheduling and re-scheduling. Tactical scheduling refers to master scheduling, whereas operational scheduling concerns scheduling at a later stage. Re-scheduling focuses on the re-planning of an existing timetable when deviations from it have occurred. 48 approaches published between 1973 and 2005 have been reviewed according to a framework that classifies them with respect to problem type, solution mechanism, and type of evaluation. 26 of the approaches support the representation of a railway network rather than a railway line, but the majority has been experimentally evaluated for traffic on a line. 94 % of the approaches have been subject to some kind of experimental evaluation, while approximately 4 % have been implemented. The solutions proposed vary from myopic, priority-based algorithms, to traditional operations research techniques and the application of agent technology.This paper provides an overview of the research in railway scheduling and dispatching. A distinction is made between tactical scheduling, operational scheduling and re-scheduling. Tactical scheduling refers to master scheduling, whereas operational scheduling concerns scheduling at a later stage. Re-scheduling focuses on the re-planning of an existing timetable when deviations from it have occurred. 48 approaches published between 1973 and 2005 have been reviewed according to a framework that classifies them with respect to problem type, solution mechanism, and type of evaluation. 26 of the approaches support the representation of a railway network rather than a railway line, but the majority has been experimentally evaluated for traffic on a line. 94 % of the approaches have been subject to some kind of experimental evaluation, while approximately 4 % have been implemented. The solutions proposed vary from myopic, priority-based algorithms, to traditional operations research techniques and the application of agent technology

SURROGATE SEARCH: A SIMULATION OPTIMIZATION METHODOLOGY FOR LARGE-SCALE SYSTEMS

For certain settings in which system performance cannot be evaluated by analytical methods, simulation models are widely utilized. This is especially for complex systems. To try to optimize these models, simulation optimization techniques have been developed. These attempt to identify the system designs and parameters that result in (near) optimal system performance. Although more realistic results can be provided by simulation, the computational time for simulator execution, and consequently, simulation optimization may be very long. Hence, the major challenge in determining improved system designs by incorporating simulation and search methodologies is to develop more efficient simulation optimization heuristics or algorithms. This dissertation develops a new approach, Surrogate Search, to determine near optimal system designs for large-scale simulation problems that contain combinatorial decision variables. First, surrogate objective functions are identified by analyzing simulation results to observe system behavior. Multiple linear regression is utilized to examine simulation results and construct surrogate objective functions. The identified surrogate objective functions, which can be quickly executed, are then utilized as simulator replacements in the search methodologies. For multiple problems containing different settings of the same simulation model, only one surrogate objective function needs to be identified. The development of surrogate objective functions benefits the optimization process by reducing the number of simulation iterations. Surrogate Search approaches are developed for two combinatorial problems, operator assignment and task sequencing, using a large-scale sortation system simulation model. The experimental results demonstrate that Surrogate Search can be applied to such large-scale simulation problems and outperform recognized simulation optimization methodology, Scatter Search (SS). This dissertation provides a systematic methodology to perform simulation optimization for complex operations research problems and contributes to the simulation optimization field

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown

Optimizing feeder bus network based on access mode shifts

The methodology introduced in this dissertation is to optimally find a feeder bus network in a suburban area for an existing rail system that connects the suburban area with the Central Business District (CBD). The objective is to minimize the total cost, including user and supplier costs. Three major access modes (walk, feeder bus, and auto) for the rail station are considered and the cost for all modes makes up the user cost. The supplier cost comes from the operating cost of the feeder bus network. The decision variables include the structure of the feeder bus network, service frequencies, and bus stop locations. The developed methodology consists of four components, including a Preparation Procedure (PP), Initial Solution Generation Procedure (ISGP), Network Features Determination Procedure (NFDP) and Solution Search Procedure (SSP). PP is used to perform a preliminary processing on the input data set. An initial solution that will be used in SSP is found in ISGP. The NFDP is a module to determine the network related features such as service frequency, mode split, stop selections and locations. A logit-based Multinomial Logit-Proportional Model (MNL-PM) model is proposed to estimate the mode shares of walk, bus and auto. A metaheuristic Tabu Search (TS) method is developed to find the optimal solution for the methodology. In the computational experiments, an Exhaustive Search (ES) method is designed and tested to validate the effectiveness of the proposed methodology. The results of networks of different sizes are presented and sensitivity analyses are performed to investigate the impacts of various model parameters (e.g., fleet size, parking fee, bus fare, etc.)

Ant Colony Optimisation for Dynamic and Dynamic Multi-objective Railway Rescheduling Problems

Recovering the timetable after a delay is essential to the smooth and efficient operation of the railways for both passengers and railway operators. Most current railway rescheduling research concentrates on static problems where all delays are known about in advance. However, due to the unpredictable nature of the railway system, it is possible that further unforeseen incidents could occur while the trains are running to the new rescheduled timetable. This will change the problem, making it a dynamic problem that changes over time. The aim of this work is to investigate the application of ant colony optimisation (ACO) to dynamic and dynamic multiobjective railway rescheduling problems. ACO is a promising approach for dynamic combinatorial optimisation problems as its inbuilt mechanisms allow it to adapt to the new environment while retaining potentially useful information from the previous environment. In addition, ACO is able to handle multi-objective problems by the addition of multiple colonies and/or multiple pheromone and heuristic matrices. The contributions of this work are the development of a junction simulator to model unique dynamic and multi-objective railway rescheduling problems and an investigation into the application of ACO algorithms to solve those problems. A further contribution is the development of a unique two-colony ACO framework to solve the separate problems of platform reallocation and train resequencing at a UK railway station in dynamic delay scenarios. Results showed that ACO can be e ectively applied to the rescheduling of trains in both dynamic and dynamic multi-objective rescheduling problems. In the dynamic junction rescheduling problem ACO outperformed First Come First Served (FCFS), while in the dynamic multi-objective rescheduling problem ACO outperformed FCFS and Non-dominated Sorting Genetic Algorithm II (NSGA-II), a stateof- the-art multi-objective algorithm. When considering platform reallocation and rescheduling in dynamic environments, ACO outperformed Variable Neighbourhood Search (VNS), Tabu Search (TS) and running with no rescheduling algorithm. These results suggest that ACO shows promise for the rescheduling of trains in both dynamic and dynamic multi-objective environments.Engineering and Physical Sciences Research Council (EPSRC

Sequence-Based Simulation-Optimization Framework With Application to Port Operations at Multimodal Container Terminals

It is evident in previous works that operations research and mathematical algorithms can provide optimal or near-optimal solutions, whereas simulation models can aid in predicting and studying the behavior of systems over time and monitor performance under stochastic and uncertain circumstances. Given the intensive computational effort that simulation optimization methods impose, especially for large and complex systems like container terminals, a favorable approach is to reduce the search space to decrease the amount of computation. A maritime port can consist of multiple terminals with specific functionalities and specialized equipment. A container terminal is one of several facilities in a port that involves numerous resources and entities. It is also where containers are stored and transported, making the container terminal a complex system. Problems such as berth allocation, quay and yard crane scheduling and assignment, storage yard layout configuration, container re-handling, customs and security, and risk analysis become particularly challenging. Discrete-event simulation (DES) models are typically developed for complex and stochastic systems such as container terminals to study their behavior under different scenarios and circumstances. Simulation-optimization methods have emerged as an approach to find optimal values for input variables that maximize certain output metric(s) of the simulation. Various traditional and nontraditional approaches of simulation-optimization continue to be used to aid in decision making. In this dissertation, a novel framework for simulation-optimization is developed, implemented, and validated to study the influence of using a sequence (ordering) of decision variables (resource levels) for simulation-based optimization in resource allocation problems. This approach aims to reduce the computational effort of optimizing large simulations by breaking the simulation-optimization problem into stages. Since container terminals are complex stochastic systems consisting of different areas with detailed and critical functions that may affect the output, a platform that accurately simulates such a system can be of significant analytical benefit. To implement and validate the developed framework, a large-scale complex container terminal discrete-event simulation model was developed and validated based on a real system and then used as a testing platform for various hypothesized algorithms studied in this work

Green logistic network design : intermodal transportation planning and vehicle routing problems.

Due to earth\u27s climate change and global warming, environmental consideration in the design of logistic systems is accelerating in recent years. In this research we aim to design an efficient and environmentally friendly logistical system to satisfy both government and carriers. In particular, we considered three problems in this dissertation: intermodal network design, deterministic green vehicle routing problem and stochastic green vehicle routing problem. The first problem aims to design an economic and efficient intermodal network including three transportation modes: railway, highway and inland waterway. The intent of this problem is to increase the utilization percentage of waterway system in the intermodal transportation network without increasing the cost to the consumer. In particular, we develop a real world coal transportation intermodal network across 15 states in the United States including highway, railway and inland waterway. The demand data were obtained from the Bureau of Transportation Statistics (BTS) under the US Department of Transportation (DOT). Four boundary models are built to evaluate the potential improvement of the network. The first boundary model is a typical minimum cost problem, where the total transportation cost is minimized while the flow balance and capacity restrictions are satisfied. An additional constraint that help obtain an upper bound on carbon emission is added in the second boundary model. Boundary model 3 minimizes the total emission with flow balance and capacity restrictions the same as boundary model 1. Boundary model 4 minimizes the total emission with an additional current cost restriction to achieve a less-aggressive lower bound for carbon emission. With a motivation to minimize the transportation and environmental costs simultaneously, we propose multi-objective optimization models to analyze intermodal transportation with economic, time performance and environmental considerations. Using data from fifteen selected states, the model determines the tonnage of coal to be transported on roadways, railways and waterways across these states. A time penalty parameter is introduced so that a penalty is incurred for not using the fastest transportation mode. Our analysis provides authorities with a potential carbon emission tax policy while minimizing the total transportation cost. In addition, sensitivity analysis allows authorities to vary waterway, railway and highway capacities, respectively, and study their impact on the total transportation cost. Furthermore, the sensitivity analysis demonstrates that an intermodal transportation policy that uses all the three modes can reduce the total transportation cost when compared to one that uses just two modes. In contrast with traditional vehicle routing problems, the second problem intends to find the most energy efficient vehicle route with minimum pollution by optimization of travel speed. A mixed integer nonlinear programming model is introduced and a heuristic algorithm based on a savings heuristic and Tabu Search is developed to solve the large case for this problem. Numerical experiments are conducted through comparison with a solution obtained by BONMIN in GAMS on randomly generated small problem instances to evaluate the performance of the proposed heuristic algorithm. To illustrate the impact of a time window constraint, travel speed and travel speed limit on total carbon emission, sensitivity analysis is conducted based on several scenarios. In the end, real world instances are examined to further investigate the impact of these parameters. Based on the analysis from the second problem, travel speed is an important decision factor in green vehicle routing problems to minimize the fuel cost. However, the actual speed limit on a road may have variance due to congestion. To further investigate the impact of congestion on carbon emission in the real world, we proposed a stochastic green vehicle routing problem as our third problem. We consider a green vehicle problem with stochastic speed limits, which aims to find the robust route with the minimum expected fuel cost. A two-stage heuristic with sample average approximation is developed to obtain the solution of the stochastic model. Computational study compares the solutions of robust and traditional mean-value green vehicle routing problems with various settings

Hybrid model for proactive dispatching of railway operation under the consideration of random disturbances in dynamic circumstances

With the increasing traffic demand and limited infrastructure expansion, railway networks are often operated close to the full capacity, especially in heavily used areas. As a result, the basic timetable is quite susceptible to the operational disturbances, and thereby the propagation and accumulation of delays significantly degrade the service level for customers. To solve this problem, extensive researches have been conducted by focusing on the predefined robust timetables and the real time dispatching algorithm development. However, it has been widely recognized that excessive robust timetables may deteriorate the operating capacity of the railway network and the addition of recovery time and buffer time can be hardly implemented in the congested area. Moreover, most of the conventional dispatching algorithms ignore the further potential random disturbances during the dispatching process, which yield non-implementable dispatching solutions and, as consequences, inferior punctuality and repetitive dispatching actions. To this end, this project aims to develop a new algorithm for real-world dispatching process with the consideration of risk-oriented random disturbances in dynamic circumstances. In the procedure of this project, an operational risk map will be firstly produced: by simulating considerable amount of disturbed timetables with random disturbances generated in a Monte-Carlo scheme and calculating the corresponding expected negative impacts (average total weighted waiting time among all the disturbances scenarios), different levels of operational risk will be assigned to each block section in the studied railway network. Within a rolling time horizon framework, conflicts are detected with the inclusion of risk-oriented random disturbances in each block section, and the near-optimal dispatching solutions are calculated by using Tabu search algorithm. Finally, three indicators including total weighted waiting time, the number of relative reordering and average absolute retiming, are chosen to testify the effectiveness and advantages of the proposed dispatching algorithm. The sensitivity analysis of dispatching-related parameters is conducted afterwards to investigate the most appropriate relevant parameters for the specific studied area. The proposed algorithms are expected to be capable of automatically producing near-optimal and robust dispatching solutions with sufficient punctuality achieved