Dynamic railway junction rescheduling using population based ant colony optimisation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Efficient rescheduling after a perturbation is an important concern of the railway industry. Extreme delays can result in large fines for the train company as well as dissatisfied customers. The problem is exacerbated by the fact that it is a dynamic one; more timetabled trains may be arriving as the perturbed trains are waiting to be rescheduled. The new trains may have different priorities to the existing trains and thus the rescheduling problem is a dynamic one that changes over time. The aim of this research is to apply a population-based ant colony optimisation algorithm to address this dynamic railway junction rescheduling problem using a simulator modelled on a real-world junction in the UK railway network. The results are promising: the algorithm performs well, particularly when the dynamic changes are of a high magnitude and frequency

How effective is Ant Colony Optimisation at Robot Path Planning

This project involves investigation of the problem robot path planning using ant colony optimisation heuristics to construct the quickest path from the starting point to the end. The project has developed a simulation that successfully simulates as well as demonstrates visually through a graphical user interface, robot path planning using ant colony optimisation. The simulation shows an ability to traverse an unknown environment from a start point to an end and successfully construct a route for others to follow both when the terrain is dynamic and stati

Optimal dynamic operations scheduling for small-scale satellites

A satellite's operations schedule is crafted based on each subsystem/payload operational needs, while taking into account the available resources on-board. A number of operating modes are carefully designed, each one with a different operations plan that can serve emergency cases, reduced functionality cases, the nominal case, the end of mission case and so on. During the mission span, should any operations planning amendments arise, a new schedule needs to be manually developed and uplinked to the satellite during a communications' window. The current operations planning techniques over a reduced number of solutions while approaching operations scheduling in a rigid manner. Given the complexity of a satellite as a system as well as the numerous restrictions and uncertainties imposed by both environmental and technical parameters, optimising the operations scheduling in an automated fashion can over a flexible approach while enhancing the mission robustness. In this paper we present Opt-OS (Optimised Operations Scheduler), a tool loosely based on the Ant Colony System algorithm, which can solve the Dynamic Operations Scheduling Problem (DOSP). The DOSP is treated as a single-objective multiple constraint discrete optimisation problem, where the objective is to maximise the useful operation time per subsystem on-board while respecting a set of constraints such as the feasible operation timeslot per payload or maintaining the power consumption below a specific threshold. Given basic mission inputs such as the Keplerian elements of the satellite's orbit, its launch date as well as the individual subsystems' power consumption and useful operation periods, Opt-OS outputs the optimal ON/OFF state per subsystem per orbital time step, keeping each subsystem's useful operation time to a maximum while ensuring that constraints such as the power availability threshold are never violated. Opt-OS can provide the flexibility needed for designing an optimal operations schedule on the spot throughout any mission phase as well as the ability to automatically schedule operations in case of emergency. Furthermore, Opt-OS can be used in conjunction with multi-objective optimisation tools for performing full system optimisation. Based on the optimal operations schedule, subsystem design parameters are being optimised in order to achieve the maximal usage of the satellite while keeping its mass minimal

Free Search of real value or how to make computers think

This book introduces in detail Free Search - a novel advanced method for search and optimisation. It also deals with some essential questions that have been raised in a strong debate following the publication of this method in journal and conference papers. In the light of this debate, Free Search deserves serious attention, as it appears to be superior to other competitive methods in the context of the experimental results obtained. This superiority is not only quantitative in terms of the actual optimal value found but also qualitative in terms of independence from initial conditions and adaptation capabilities in an unknown environment

Railway platform reallocation after dynamic perturbations using ant colony optimisation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Train delays at stations are a common occurrence in complex, busy railway networks. A delayed train will miss its scheduled time slot on the platform and may have to be reallocated to a new platform to allow it to continue its journey. The problem is a dynamic one because while reallocating a delayed train further unanticipated train delays may occur, changing the nature of the problem over time. Our aim in this study is to apply ant colony optimisation (ACO) to a dynamic platform reallocation problem (DPRP) using a model created from real-world train schedule data. To ensure that trains are not unnecessarily reallocated to new platforms we introduce a novel best-ant-replacement scheme that takes into account not only the objective value but also the physical distance between the original and the new platforms. Results showed that the ACO algorithm outperformed a heuristic that places the delayed train in the first available time-slot and that this improvement was more apparent with high-frequency dynamic changes

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