42 research outputs found

    Infrastructure information management of bridges at local authorities in the UK

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    Behind the largest infrastructure construction projects currently underway is a system of managing information known as Building Information Modelling (BIM). This represents a collaborative approach to civil engineering and makes use of advances in computer technology to link seamlessly many information repositories together across organisational boundaries. Alongside the developments in BIM, the world of asset management has also seen a major leap forward with the release of ISO 5500x – the family of international standards for asset management. This is now being adopted by many industries – particularly those in the infrastructure sectors – to maximise the value which is returned from their assets. In addition, the Highways Maintenance Efficiency Programme has released a guidance for highway authorities wishing to improve their asset management systems. However, infrastructure managers in local authorities such as county councils are significantly less engaged in both of these developments than their counterparts in strategic infrastructure networks. This paper presents the findings of a study of the ‘information system landscape’ at local authorities from across England, UK. The study reveals a number of recurring information management challenges that are frequently present. The paper finally provides a number of recommendations with specific reference to information management and encourages councils to consider adopting the standards. EPSRC/Innovate U

    Exploitation of material consolidation trade-offs in multi-tier complex supply networks

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    While consolidation strategies form the backbone of many supply chain optimisation problems, exploitation of multi-tier material relationships through consolidation remains an understudied area, despite being a prominent feature of industries that produce complex made-to-order products. In this paper, we propose an optimisation framework for exploiting multi-to-multi relationship between tiers of a supply chain. The resulting formulation is flexible such that quantity discounts, inventory holding, and transport costs can be included. The framework introduces a new trade-off between tiers, leading to cost reductions in one tier but increased costs in the other, which helps to reduce the overall procurement cost in the supply chain. A mixed integer linear programming model is developed and tested with a range of small to large-scale test problems from aerospace manufacturing. Our comparison to benchmark results shows that there is indeed a cost trade-off between two tiers, and that its reduction can be achieved using a holistic approach to reconfiguration. Costs are decreased when second tier fixed ordering costs and the number of machining options increase. Consolidation results in reduced inventory holding costs in all scenarios. Several secondary effects such as simplified supplier selection may also be observed.Comment: accepted to Supply Chain Analytic

    Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

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    Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant

    A vulnerability-based approach to human-mobility reduction for countering COVID-19 transmission in London while considering local air quality

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    An ecologic analysis was conducted to explore the correlation between air pollution, and COVID-19 cases and fatality rates in London. The analysis demonstrated a strong correlation (R2>0.7) between increment in air pollution and an increase in the risk of COVID-19 transmission within London boroughs. Particularly, strong correlations (R2>0.72) between the risk of COVID-19 fatality and NO2 and PM2.5 pollution concentrations were also found. Although this study assumed the same level of air pollution across a particular London borough, it demonstrates the possibility to employ air pollution as an indicator to rapidly identify the vulnerable regions within a city. Such an approach can inform the decisions to suspend or reduce the operation of different public transport modes within a city. The methodology and learnings from the study can thus aid public transport to respond to the COVID-19 outbreak by adopting different levels of human-mobility reduction strategies based on the vulnerability of a given region

    Multi-agent learning of asset maintenance plans through localised subnetworks

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    Maintenance planning of networked multi-asset systems is a complex problem due to the inherent individual and collective asset constraints and dynamics as well as the size of the system and interdependencies among assets. Although multi-asset systems have been studied numerous times in the past decades, maintenance planning implications of the system's network characteristics have been barely analysed. Likewise, solutions that consider the network perspective suffer from scalability issues as a network-wide observability is assumed. This paper proposes a network maintenance planning approach based on the decomposition of the multi-asset network into fixed-size localised subnetworks. The overall network maintenance plan is produced by aggregating the subnetwork maintenance plans, which are computed independently via a multi-agent deep reinforcement learning (MARL) algorithm. The results are evaluated against a network-wide approach as well as the commonly-used individual approach. The paper also introduces a systematic approach to integrate the MARL resulting policy in a multi-asset agent-based model. Simulation results of several random asset networks and a large nationwide network infrastructure show that, although a network-wide approach outperforms, on average, other approaches considered, the localised subnetworks approach, provides an acceptable alternative in networks with small-world properties, without the need of a network-wide view
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