Outputs (14)

Nature Inspired Optimisation for Delivery Problems: From Theory to the Real World (2022)
Book
Urquhart, N. (2022). Nature Inspired Optimisation for Delivery Problems: From Theory to the Real World. Springer. https://doi.org/10.1007/978-3-030-98108-2

This book explains classic routing and transportation problems and solutions, before offering insights based on successful real-world solutions. The chapters in Part I introduce and explain the traveling salesperson problem (TSP), vehicle routing pro... Read More about Nature Inspired Optimisation for Delivery Problems: From Theory to the Real World.

An empirical evaluation of a novel domain-specific language – modelling vehicle routing problems with Athos (2022)
Journal Article
Hoffmann, B., Urquhart, N., Chalmers, K., & Guckert, M. (2022). An empirical evaluation of a novel domain-specific language – modelling vehicle routing problems with Athos. Empirical Software Engineering, 27(7), Article 180. https://doi.org/10.1007/s10664-022-10210-w

Domain-specific languages (DSLs) are a popular approach among software engineers who demand for a tailored development interface. A DSL-based approach allows to encapsulate the intricacies of the target platform in transformations that turn DSL model... Read More about An empirical evaluation of a novel domain-specific language – modelling vehicle routing problems with Athos.

Modelling the Impact of Individual Preferences on Traffic Policies (2022)
Journal Article
Nguyen, J., Powers, S., Urquhart, N., Farrenkopf, T., & Guckert, M. (2022). Modelling the Impact of Individual Preferences on Traffic Policies. SN Computer Science, 3(5), Article 365. https://doi.org/10.1007/s42979-022-01253-3

Urban traffic is a system always prone to overload, often approaching breakdown during rush hour times. Well adjusted modifications of traffic policies, with appropriate interventions, promise potential improvements by inducing change in both individ... Read More about Modelling the Impact of Individual Preferences on Traffic Policies.

Athos: An Extensible DSL for Model Driven Traffic and Transport Simulation (2020)
Presentation / Conference Contribution
Hoffmann, B., Urquhart, N., Chalmers, K., & Guckert, M. (2020, February). Athos: An Extensible DSL for Model Driven Traffic and Transport Simulation. Presented at Modelling 2020, Vienna

Multi-agent systems may be considered appropriate tools for simulating complex systems such as those based around traffic and transportation networks. Modelling traffic participants as agents can reveal relevant patterns of traffic flow. Upsurging tr... Read More about Athos: An Extensible DSL for Model Driven Traffic and Transport Simulation.

Using MAP-Elites to support policy making around Workforce Scheduling and Routing (2020)
Journal Article
Urquhart, N., Hart, E., & Hutcheson, W. (2020). Using MAP-Elites to support policy making around Workforce Scheduling and Routing. Automatisierungstechnik, 68(2), https://doi.org/10.1515/auto-2019-0107

English abstract: Algorithms such as MAP-Elites provide a means of allowing users to explore a solution space by returning an archive of high-performing solutions. Such an archive, can allow the user an overview of the solution space which may be use... Read More about Using MAP-Elites to support policy making around Workforce Scheduling and Routing.

An Illumination Algorithm Approach to Solving the Micro-Depot Routing Problem (2019)
Presentation / Conference Contribution
Urquhart, N., Hoehl, S., & Hart, E. (2019, July). An Illumination Algorithm Approach to Solving the Micro-Depot Routing Problem. Presented at Genetic and Evolutionary Computation Conference (GECCO '19), Prague, Czech Republic

An increasing emphasis on reducing pollution and congestion in city centres combined with an increase in online shopping is changing the ways in which logistics companies address vehicle routing problems (VRP). We introduce the {\em micro-depot}-VRP,... Read More about An Illumination Algorithm Approach to Solving the Micro-Depot Routing Problem.

Increasing Trust in Meta-Heuristics by Using MAP-Elites (2019)
Presentation / Conference Contribution
Urquhart, N., Guckert, M., & Powers, S. (2019, July). Increasing Trust in Meta-Heuristics by Using MAP-Elites. Presented at Genetic and Evolutionary Computation COnference, Prague, Czech Republic

Intelligent AI systems using approaches containing emergent elements often encounter acceptance problems. Results do not get sufficiently explained and the procedure itself can not be fully retraced because the flow of control is dependent on stochas... Read More about Increasing Trust in Meta-Heuristics by Using MAP-Elites.

An Agent Based Technique for Improving Multi-Stakeholder Optimisation Problems (2019)
Presentation / Conference Contribution
Urquhart, N., & Powers, S. T. (2019, June). An Agent Based Technique for Improving Multi-Stakeholder Optimisation Problems. Presented at PAAMS 2019: International Conference on Practical Applications of Agents and Multi-Agent Systems, Avila, Spain

We present an agent based framework for improving multi-stakeholder optimisation problems, which we define as optimisation problems where the solution is utilised by a number of stakeholders who have their own local preferences. We explore our ideas... Read More about An Agent Based Technique for Improving Multi-Stakeholder Optimisation Problems.

Quantifying the effects of increasing user choice in MAP-Elites applied to a Workforce Scheduling and Routing Problem. (2019)
Presentation / Conference Contribution
Urquhart, N., Hart, E., & Hutcheson, W. (2019, April). Quantifying the effects of increasing user choice in MAP-Elites applied to a Workforce Scheduling and Routing Problem. Presented at EvoStar2019: International Conference on the Applications of Evolutionary Computation, Leipzig

Quality-diversity algorithms such as MAP-Elites provide a means of supporting the users when finding and choosing solutions to a problem by returning a set of solutions which are diverse according to set of user-defined features. The number of soluti... Read More about Quantifying the effects of increasing user choice in MAP-Elites applied to a Workforce Scheduling and Routing Problem..

Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems (2018)
Journal Article
Andras, P., Esterle, L., Guckert, M., Anh Han, T., Lewis, P. R., Milanovic, K., Payne, T., Perret, C., Pitt, J., Powers, S. T., Urquhart, N., & Wells, S. (2018). Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems. IEEE technology & society magazine, 37(4), 76-83. https://doi.org/10.1109/MTS.2018.2876107

Intelligent machines have reached capabilities that go beyond a level that a human being can fully comprehend without sufficiently detailed understanding of the underlying mechanisms. The choice of moves in the game Go (generated by Deep Mind?s Alpha... Read More about Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems.

Creating optimised employee travel plans (2018)
Presentation / Conference Contribution
Urquhart, N., & Hart, E. (2015, September). Creating optimised employee travel plans. Presented at EuroGen 2015

The routing of employees who provide services such as home health or social care is a complex problem. When sending an employee between two addresses , there may exist more than one travel option, e.g. public transport or car. In this paper we examin... Read More about Creating optimised employee travel plans.

Evaluating the Performance of an Evolutionary Tool for Exploring Solution Fronts (2018)
Presentation / Conference Contribution
Urquhart, N. (2018, April). Evaluating the Performance of an Evolutionary Tool for Exploring Solution Fronts. Presented at EvoApplications 2018

EvoFilter is an evolutionary algorithm based tool for searching through large non-dominated fronts in order to find a subset of solutions that are of interest to the user. EvoFilter is designed to take the output of existing Multi Objective Evolutio... Read More about Evaluating the Performance of an Evolutionary Tool for Exploring Solution Fronts.

Combining parallel coordinates with multi-objective evolutionary algorithms in a real-world optimisation problem (2017)
Presentation / Conference Contribution
Urquhart, N. (2017, June). Combining parallel coordinates with multi-objective evolutionary algorithms in a real-world optimisation problem. Presented at Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '17

Optimisation problems based upon real-world instances often contain many objectives. Many existing Multi-Objective Evolutionary Algorithm techniques return a set of solutions from which the user must make a final selection; typically such a set of so... Read More about Combining parallel coordinates with multi-objective evolutionary algorithms in a real-world optimisation problem.

Evolving solution choice and decision support for a real-world optimisation problem (2017)
Presentation / Conference Contribution
Urquhart, N., & Fonzone, A. (2017, July). Evolving solution choice and decision support for a real-world optimisation problem. Presented at Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '17, Berlin, Germany

Agencies who provide social care services typically have to optimise staff allocations and the travel whilst attempting to satisfy conflicting objectives. In such cases it is desirable to have a range of solutions to choose from, allowing the agenc... Read More about Evolving solution choice and decision support for a real-world optimisation problem.