Chris Teller

Dr Christopher Turner


Lecturer in Business Analytics
MSc, MSc, PhD
+44 (0)1483 684310
19 MS 02
Online Only (Please Email): Monday 11am -1pm, Thursday 11am - 1pm

Biography

Areas of specialism

Business Analytics; Business Process Management; Mixed Reality Visualisation; Industry 4.0; Data Mining; Cloud Manufacturing; Distributed and Sustainable Manufacturing

Previous roles

2009 - 2017
Dr Christopher Turner was a Senior Research Fellow and Project Manager within the Manufacturing and Materials Department at Cranfield University.
Cranfield University

Research

Research interests

My teaching

My publications

Publications

Chris J Turner, Wolfgang Garn (2022)Next Generation DES Simulation: A Research Agenda for Human Centric Manufacturing Systems, In: Journal of industrial information integration28100354 Elsevier Inc

In this paper we introduce a research agenda to guide the development of the next generation of Discrete Event Simulation (DES) systems. Interfaces to digital twins are projected to go beyond physical representations to become blueprints for the actual “objects” and an active dashboard for their control. The role and importance of real-time interactive animations presented in an Extended Reality (XR) format will be explored. The need for using game engines, particularly their physics engines and AI within interactive simulated Extended Reality is expanded on. Importing and scanning real-world environments is assumed to become more efficient when using AR. Exporting to VR and AR is recommended to be a default feature. A technology framework for the next generation simulators is presented along with a proposed set of implementation guidelines. The need for more human centric technology approaches, nascent in Industry 4.0, are now central to the emerging Industry 5.0 paradigm; an agenda that is discussed in this research as part of a human in the loop future, supported by DES. The potential role of Explainable Artificial Intelligence is also explored along with an audit trail approach to provide a justification of complex and automated decision-making systems with relation to DES. A technology framework is proposed, which brings the above together and can serve as a guide for the next generation of holistic simulators for manufacturing.

C. J Turner, C Emmanouilidis, T Tomiyama, A Tiwari, R Roy (2019)Intelligent decision support for maintenance: an overview and future trends, In: International journal of computer integrated manufacturing32(10)pp. 936-959 Taylor & Francis

The changing nature of manufacturing, in recent years, is evident in industry's willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable 'Human in the loop' interactions.

C Turner, O Okorie, C Emmanouilidis, J Oyekan (2022)Circular production and maintenance of automotive parts: An Internet of Things (IoT) data framework and practice review, In: Computers in industry136 Elsevier B.V

•The paper focuses on the matching of digital technologies to maintenance practice in the automotive sector.•A framework for IOT enabled circular maintenance practice and treatment of automotive parts is proposed.•The framework proposed is capable of processing data from vehicle based sensors.•An Auto-circular simulator concept is outlined, providing visualisation and analytics toolbox capabilities.•A case study focused on a hydrogen car power unit is included, with a supporting ontology. The adoption of the Circular Economy paradigm by industry leads to increased responsibility of manufacturing to ensure a holistic awareness of the environmental impact of its operations. In mitigating negative effects in the environment, current maintenance practice must be considered for its potential contribution to a more sustainable lifecycle for the manufacturing operation, its products and related services. Focusing on the matching of digital technologies to maintenance practice in the automotive sector, this paper outlines a framework for organisations pursuing the integration of environmentally aware solutions in their production systems. This research sets out an agenda and framework for digital maintenance practice within the Circular Economy and the utilisation of Industry 4.0 technologies for this purpose.

G. Gonzalez Lozano, A. Tiwari, C. Turner (2018)A design algorithm to model fibre paths for manufacturing of structurally optimised composite laminates, In: Composite Structures204pp. 882-895 Elsevier

Fibre steering is involved in the development of non-conventional variable stiffness laminates (VSL) with curvilinear paths as well as in the lay-up of conventional laminates with complex shapes. Manufacturability is generally overlooked in design and, as a result, industrial applications do not take advantage of the potential of composite materials. This work develops a design for manufacturing (DFM) tool for the introduction in design of the manufacturing requirements and limitations derived from the fibre placement technology. This tool enables the automatic generation of continuous fibre paths for manufacturing. Results from its application to a plate with a central hole and an aircraft structure – a windshield front fairing – are presented, showing good correlation of resulting manufacturable paths to initial fibre trajectories. The effect of manufacturing constraints is assessed to elucidate the extent to which the structurally optimal design can be reached while conforming to existing manufacturing specifications.

N. Madenas, A. Tiwari, C. J. Turner, S. Peachey, S. Broome (2015)Improving root cause analysis through the integration of PLM systems with cross supply chain maintenance data, In: The International Journal of Advanced Manufacturing Technology84(5 - 8)pp. 1679-1695 Springer Verlag

The purpose of this paper is to demonstrate a system architecture for integrating product lifecycle management (PLM) systems with cross supply chain maintenance information to support root cause analysis. By integrating product data from PLM systems with warranty claims, vehicle diagnostics and technical publications, engineers were able to improve the root cause analysis and close the information gaps. Data collection was achieved via in-depth semi-structured interviews and workshops with experts from the automotive sector. Unified Modelling Language (UML) diagrams were used to design the system architecture proposed. A user scenario is also presented to demonstrate the functionality of the system.

N Prajapat, C Turner, A Tiwari, D Tiwari, W Hutabarat (2020)Real-time discrete event simulation: a framework for an intelligent expert system approach utilising decision trees, In: International journal of advanced manufacturing technology110(11-12)pp. 2893-2911 Springer

This paper explores the use of Discrete Event Simulation (DES) for decision making in real time based on the potential for data streamed from production line sensors. Technological innovations for data collection and an increasingly competitive global market have led to an increase in the application of Discrete Event Simulation by manufacturing companies in recent years. Scenario analysis and optimisation methods are often applied to these simulation models to improve objectives such as cost, profit and throughput. The literature review has identified key research gaps as: The lack of example cases where multi-objective optimisation methods have been applied to simulation models; The need for a framework to visualise the relationship between inputs and outputs of simulation models. A framework is presented to enable the optimisation DES simulation models and optimise multiple objectives simultaneously using design of experiments and meta-models to create a Pareto front of solutions. The results show the resource allocation meta-model provides acceptable prediction accuracy whilst the lead time meta-model was not able to provide accurate prediction. Regression trees have been proposed to assist stakeholders with understanding the relationships between input and output variables. The framework uses regression and classification trees with overlaid values for multiple objectives and random forests to improve prediction accuracy for new points. A real-life test case involving a turbine assembly process is presented to illustrate the use and validity of the framework. The generated regression tree expressed a general trend by demonstrating relationships between input variables and two conflicting objectives. Random forests were implemented for creating higher accuracy predictions and they produced a mean square error of ~0.066 on the training data and ~0.081 on test data.

Okechukwu Okorie, Konstantinos Salonitis, Fiona Charnley, Mariale Moreno, Christopher Turner, Ashutosh Tiwari (2018)Digitisation and the Circular Economy: A Review of Current Research and Future Trends, In: Energies (Basel)11(11) MDPI

Since it first appeared in literature in the early nineties, the Circular Economy (CE) has grown in significance amongst academic, policymaking, and industry groups. The latest developments in the CE field have included the interrogation of CE as a paradigm, and its relationship with sustainability and other concepts, including iterative definitions. Research has also identified a significant opportunity to apply circular approaches to our rapidly changing industrial system, including manufacturing processes and Industry 4.0 (I4.0) which, with data, is enabling the latest advances in digital technologies (DT). Research which fuses these two areas has not been extensively explored. This is the first paper to provide a synergistic and integrative CE-DT framework which offers directions for policymakers and guidance for future research through a review of the integrated fields of CE and I4.0. To achieve this, a Systematic Literature Review (SLR; n = 174) of the empirical literature related to digital technologies, I4.0, and circular approaches is conducted. The SLR is based on peer-reviewed articles published between 2000 and early 2018. This paper also summarizes the current trends in CE research related to manufacturing. The findings confirm that while CE research has been on the increase, research on digital technologies to enable a CE is still relatively untouched. While the “interdisciplinarity” of CE research is well-known, the findings reveal that a substantial percentage is engineering-focused. The paper concludes by proposing a synergistic and integrative CE-DT framework for future research developed from the gaps in the current research landscape.

Okechukwu Okorie, Konstantinos Salonitis, Fiona Charnley, Christopher Turner (2018)A Systems Dynamics Enabled Real-Time Efficiency for Fuel Cell Data-Driven Remanufacturing, In: Journal of Manufacturing and Materials Processing2(4) MDPI

Remanufacturing is a viable option to extend the useful life of an end-of-use product or its parts, ensuring sustainable competitive advantages under the current global economic climate. Challenges typical to remanufacturing still persist, despite its many benefits. According to the European Remanufacturing Network, a key challenge is the lack of accurate, timely and consistent product knowledge as highlighted in a 2015 survey of 188 European remanufacturers. With more data being produced by electric and hybrid vehicles, this adds to the information complexity challenge already experienced in remanufacturing. Therefore, it is difficult to implement real-time and accurate remanufacturing for the shop floor; there are no papers that focus on this within an electric and hybrid vehicle environment. To address this problem, this paper attempts to: (1) identify the required parameters/variables needed for fuel cell remanufacturing by means of interviews; (2) rank the variables by Pareto analysis; (3) develop a casual loop diagram for the identified parameters/variables to visualise their impact on remanufacturing; and (4) model a simple stock and flow diagram to simulate and understand data and information-driven schemes in remanufacturing.

J Oyekan, W Hutabarat, C Turner, C Arnoult, A Tiwari (2020)Using Therbligs to embed intelligence in workpieces for digital assistive assembly, In: Journal of ambient intelligence and humanized computing11(6)pp. 2489-2503 Springer

Current OEM (Original Equipment Manufacturer) facilities tend to be highly integrated and are often situated on one site. While providing scale of production such centralisation may create barriers to the achievement of fully flexible, adaptable, and reconfigurable factories. The advent of Industry 4.0 opens up opportunities to address these barriers by decentralising information and decision-making in manufacturing systems through CPS (Cyber Physical Systems) use. This research presents a qualitative study that investigates the possibility of distributing information and decision-making logic into ‘smart workpieces’ which can actively participate in assembly operations. To validate the concept, a use-case demonstrator, corresponding to the assembly of a ‘flat-pack’ table, was explored. Assembly parts in the demonstrator, were equipped with computation, networking, and interaction capabilities. Ten participants were invited to evaluate the smart assembly method and compare its results to the traditional assembly method. The results showed that in its current configuration the smart assembly was slower. However, it made the assembly process more flexible, adaptable and reconfigurable.

J Oyekan, Y Chen, C Turner, A Tiwari (2021)Applying a fusion of wearable sensors and a cognitive inspired architecture to real-time ergonomics analysis of manual assembly tasks, In: Journal of Manufacturing Systems61pp. 391-405 Elsevier

High value manufacturing systems still require ergonomically intensive manual activities. Examples include the aerospace industry where the fitting of pipes and wiring into confined spaces in aircraft wings is still a manual operation. In these environments, workers are subjected to ergonomically awkward forces and postures for long periods of time. This leads to musculoskeletal injuries that severely limit the output of a shopfloor leading to loss of productivity. The use of tools such as wearable sensors could provide a way to track the ergonomics of workers in real time. However, an information processing architecture is required in order to ensure that data is processed in real time and in a manner that meaningful action points are retrieved for use by workers. In this work, based on the Adaptive Control of Thought—Rational (ACT-R) cognitive framework, we propose a Cognitive Architecture for Wearable Sensors (CAWES); a wearable sensor system and cognitive architecture that is capable of taking data streams from multiple wearable sensors on a worker’s body and fusing them to enable digitisation, tracking and analysis of human ergonomics in real time on a shopfloor. Furthermore, through tactile feedback, the architecture is able to inform workers in real time when ergonomics rules are broken. The architecture is validated through the use of an aerospace case study undertaken in laboratory conditions. The results from the validation are encouraging and in the future, further tests will be performed in an actual working environment.

Windo Hutabarat, John Oyekan, Christopher Turner, Ashutosh Tiwari, Neha Prajapat, Xiao-Peng Gan, Anthony Waller (2017)Combining virtual reality enabled simulation with 3D scanning technologies towards smart manufacturing, In: 2016 Winter Simulation Conference IEEE

Recent introduction of low-cost 3D sensing and affordable immersive virtual reality have lowered the barriers for creating and maintaining 3D virtual worlds. In this paper, we propose a way to combine these technologies with discrete-event simulation to improve the use of simulation in decision making in manufacturing. This work will describe how feedback is possible from real world systems directly into a simulation model to guide smart behaviors. Technologies included in the research include feedback from RGBD images of shop floor motion and human interaction within full immersive virtual reality that includes the latest headset technologies.

Isidro Durazo-Cardenas, Andrew Starr, Christopher J Turner, Ashutosh Tiwari, Leigh Kirkwood, Maurizio Bevilacqua, Antonios Tsourdos, Essam Shehab, Paul Baguley, Yuchun Xu, Christos Emmanouilidis (2018)An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways ’ condition, planning and cost, In: Transportation Research Part C: Emerging Technologies89pp. 234-253 Elsevier

National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment. Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain. Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel in- tegrated system for automatic job scheduling is presented; from concept formulation to the ex- amination of the data to information transitional level interface, and at the decision making level. The underlying architecture con fi gures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value. A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise de- gradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum sche- duling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account speci fi c cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The de- monstrator structure was found fi t for purpose with logical component relationships, o ff ering further scope for research and commercial exploitation

Shahwar Rehman, Ashutosh Tiwari, Christopher Turner, Leon Williams (2018)A framework for innovation outsourcing, In: International Journal of Business Innovation and Research16(1) Inderscience

This paper proposes a framework for the facilitation of organisational capability for outsourcing innovation, enabling firms to take advantage of its many benefits (e.g., reduced costs, increased flexibility, access to better expertise and increased business focus), whilst mitigating its risks. In this framework a generic holistic model is developed to aid firms to successfully outsource innovation. The model is realised in two stages using a qualitative theory-building research design. The initial stage develops a preliminary model which is subsequently validated and refined during the second stage. The propositions which form the preliminary model are deductively explored to identify whether they also exist in a second data set. A semi-structured interview survey is executed with the aid of a rich picture survey instrument to gather data for this purpose. The model developed by this study describes innovation outsourcing as an open system of interrelated activities that takes established company strategy (in terms of people, organisational structures, environment, and technology), and transforms it into improved firm performance through innovation. The model achieves this through a three-stage process which enables the alignment of capability to outsourced innovation activity, and makes actual performance outcomes, rather than expected benefits, the focus of innovation outsourcing aims.

S Quintana - Amate, P Bermell - Garcia, A Tiwari, C J Turner (2017)A new knowledge sourcing framework for knowledge-based engineering: An aerospace industry case study, In: Computers & Industrial Engineering104pp. 35-50 Elsevier

New trends in Knowledge-Based Engineering (KBE) highlight the need for decoupling the automation aspect from the knowledge management side of KBE. In this direction, some authors argue that KBE is capable of effectively capturing, retaining and reusing engineering knowledge. However, there are some limitations associated with some aspects of KBE that present a barrier to deliver the knowledge sourcing process requested by industry. To overcome some of these limitations this research proposes a new methodology for efficient knowledge capture and effective management of the complete knowledge life cycle. The methodology proposed in this research is validated through the development and implementation of a case study involving the optimisation of wing design concepts at an Aerospace manufacturer. The results obtained proved the extended KBE capability for fast and effective knowledge sourcing. This evidence was provided by the experts working in the development of the case study through the implementation of structured quantitative and qualitative analyses.

Chris Turner, John Oyekan, Lampros K. Stergioulas (2021)Distributed Manufacturing: A New Digital Framework for Sustainable Modular Construction, In: Sustainability (Basel, Switzerland)131515 MDPI AG

This paper explores the notion of the modular building construction site as an applied instance of redistributed manufacturing; in so doing, this research seeks to reduce the environmental footprint of building sites, treating them as small digitally connected subunits. In seeking to provide a whole lifecycle appreciation of a construction project, it is noted that the presence of a framework to provide guidance on the consideration of Internet of Things (IoT) data streams and connected construction objects is currently lacking. This paper proposes use of embedded IoT enabled sensing technology within all stages of a modular building lifecycle. An expanded four-phase model of intelligent assets use in construction is proposed along with an outline of the required data flows between the stages of a given building’s entire lifecycle that need to be facilitated for a BIM (Buildings Information Modelling) representation to begin to describe a building project as a sustainable asset within the circular economy. This paper also describes the use of concrete as a modular sensing structure; proposing that health monitoring of the material in situ along with the recoding of environmental factors over time could help to extend the longevity of such structures.

Christopher J. Turner, John Oyekan, Lampros Stergioulas, David Griffin (2020)Utilizing Industry 4.0 on the Construction Site Challenges and opportunities, In: IEEE Transactions on Industrial Informatics Institute of Electrical and Electronics Engineers (IEEE)

In recent years a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organizations alike. This paper discusses the current state of the art in the adoption of industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of industry 4.0 technologies. This paper discusses the relevance of the following key industry 4.0 technologies to construction: data analytics and artificial intelligence; robotics and automation; buildings information management; sensors and wearables; digital twin and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This paper also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector; a three-phase use of intelligent assets from the point of manufacture up to after build and a four staged R&D process for the implementation of smart wearables in a digital enhanced construction site.

Chris Turner, Mariale Moreno, Luigi Mondini, Konstantinos Salonitis, Fiona Charnley, Ashutosh Tiwari, Windo Hutabarat (2019)Sustainable Production in a Circular Economy: A Business Model for Re-Distributed Manufacturing, In: Sustainability11(16)4291pp. 1-19 MDPI

The emergence of new technologies such as the Internet of Things, big data, and advanced robotics, together with risks such as climate change, rising labour costs, and a fluctuating economy, are challenging the current UK manufacturing model. In this paper, business models for re-distributed manufacture (RdM) are developed using anIDEF (Icam DEFinition for Function Modelling) description to serve as a guide for the implementation of the RdM concept in the consumer goods industry. This paper explores the viability of a re-distributed business model for manufacturers employing new manufacturing technologies such as additive manufacturing or three-dimensional (3D) printing, as part of a sustainable and circular production and consumption system. An As-Is value chain model is presented alongside the proposed new business model for a sustainable re-distributed manufacturing system. Both are illustrated via a case study drawn from the shoe manufacturing industry. The case study shows that there is a need for robust facilities in close proximity to the customer. These facilities are store fronts which can also manufacture, remanufacture, and provide services. The reduction in transportation and increase in customer involvement throughout the process are the main benefits that would accrue if a re-distributed model is implemented in the given industry.

Kostas Vergidis, Christopher Turner, Alex Alechnovic, Ashutosh Tiwari (2015)An automated optimisation framework for the development of re-configurable business processes: a web services approach, In: International Journal of Computer Integrated Manufacturing28(1)pp. 41-58 Taylor & Francis

The practice of optimising business processes has, until recently, been undertaken mainly as a manual task. This paper provides insight s into a n automated business process optimisation framework by using web services for the development of re - configurable business processes. The research presented here extends the framework of Vergidis (2008) by introducing web services as a mechanism for facilitating business process interactions, identifying enhancements to support business processes and undertaking three case studies to evaluate the proposed enhancements. The featured case studies demonstrate that an increase in the amount of available web services gives rise to improvements in the business processes generated. This research highlights an increase in the efficiency of the algorithm and the quality of the business proc ess designs that result from the enhancements . Future research directio ns are proposed for the further improvement of the framework

E Simon, J Oyekan, W Hutabarat, A Tiwari, C J Turner (2018)Adapting Petri Nets to Discrete Event Simulation for the Stochastic Modelling of Manufacturing Systems, In: International Journal of Simulation Modelling17(1)pp. 5-17 DAAAM Int. Vienna

Discrete - Event Simulation (DES) is commonly used for the simulation of manufacturing systems. In many practical cases, DES practitioners ha ve to make simplifications or to use the software in an unconventional or convoluted fashion to meet their needs. Petri nets enable the development of transparent models which allow increased flexibility and control for designers. Furthermore, Petri nets t ake advantage of a solid mathematical ground and constitute a simple language. However, Petri nets lack the software capabilities to realise their full potential. This study investigates the suitability and relevance of Discrete - Event Simulation (DES) soft ware for Petri net modelling in the context of manufacturing systems. A framework is developed for the modelling of different classes of Petri nets on DES. Analytical models of asynchronous flow lines are developed. Initial results show that the analytical models are without closed - form solution and the explosion of the state space is observed, justifying the use of computational methods and simulation for the analysis of manufacturing systems. This study shows that the gain in flexibility provided by Petri nets provides a new insight into the effects of stochasticity on setup and failure times in manufacturing systems.

Christopher J Turner, Ruidong Ma, Jingyu Chen, John Oyekan (2021)Human in the Loop: Industry 4.0 Technologies and Scenarios for Worker Mediation of Automated Manufacturing, In: IEEE access9pp. 103950-103966 Institute of Electrical and Electronics Engineers (IEEE)

Industry 4.0 derived technologies have the potential to enable a new wave of digital manufacturing solutions for semi and fully automated production. In addition, this paradigm encompasses the use of communication technologies to transmit data to processing stations as well as the utilization of cloud based computational resources for data mining. Despite the rise in automation, future manufacturing systems will initially still require humans in the loop to provide supervisory level mediation for even the most autonomous production scenarios. Through a structured review, this paper details a number of key technologies that are most likely to shape this future and describes a range of scenarios for their use in delivering human mediated automated and autonomous production. This paper argues that in all cases of future manufacturing management it is key that the human has oversight of critical information flows and remains an active participant in the delivery of the next generation of production systems.

Christopher J Turner, Windo Hutabarat, John Oyekan, Ashutosh Tiwari (2016)Discrete Event Simulation and Virtual Reality use in Industry: New opportunities and future trends, In: IEEE Transactions on Human-Machine Systems46(6)pp. 882-894 IEEE

This paper reviews the area of combined discrete event simulation (DES) and virtual reality (VR) use within in- dustry. While establishing a state of the art for progress in this area, this paper makes the case for VR DES as the vehicle of choice for complex data analysis through interactive simulation models, highlighting both its advantages and current limitations. This pa- per reviews active research topics such as VR and DES real-time integration, communication protocols, system design considera- tions, model validation, and applications of VR and DES. While summarizing future research directions for this technology combi- nation, the case is made for smart factory adoption of VR DES as a new platform for scenario testing and decision making. It is put that in order for VR DES to fully meet the visualization require- ments of both Industry 4.0 and Industrial Internet visions of digital manufacturing, further research is required in the areas of lower latency image processing, DES delivery as a service, gesture recog- nition for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets.

Christopher Turner, Ashutosh Tiwari, Andrew Starr, Kevin Blacktop (2015)A review of key planning and scheduling in the rail industry in Europe and UK, In: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit230(3)pp. 984-998 SAGE Publications

Planning and scheduling activities within the rail industry have benefited from developments in computer-based simulation and modelling techniques over the last 25 years. Increasingly, the use of computational intelligence in such tasks is featuring more heavily in research publications. This paper examines a number of common rail-based planning and scheduling activities and how they benefit from five broad technology approaches. Summary tables of papers are provided relating to rail planning and scheduling activities and to the use of expert and decision systems in the rail industry.

Wolfgang Garn, Christopher Turner, George Kireulishvili, Vasiliki Panagi (2019)The impact of catchment areas in predicting bus journeys

The catchment area along a bus route is key in predicting bus journeys. In particular, the aggregated number of households within the catchment area are used in the prediction model. The model uses other factors, such as head-way, day-of-week and others. The focus of this study was to classify types of catchment areas and analyse the impact of varying their sizes on the quality of predicting the number of bus passengers. Machine Learning techniques: Random Forest, Neural Networks and C5.0 Decision Trees, were compared regarding solution quality of predictions. The study discusses the sensitivity of catchment area size variations. Bus routes in the county Surrey in the United Kingdom were used to test the quality of the methods. The findings show that the quality of predicting bus journeys depends on the size of the catchment area.

Additional publications