As technology advances towards new paradigms such as the Internet of Things, there is a desire among business leaders for a reliable method to determine the value of supporting these ventures. Traditional simulation and analysis techniques cannot model the complex systems inherent in ﬁelds such as infrastructure asset management, or suffer from a lack of data on which to build a prediction. Agent-based modelling, through an integration with data science, presents an attractive simulation method to capture these underlying complexities and provide a solution.
The aim of this work is to investigate this integration as a reﬁned process for answering practical business questions. A speciﬁc case study is addressed to assess the return on investment of installing condition monitoring sensors on lift assets in a London Underground station. An agent-based model is developed for this purpose, supported by analysis from historical data. The simulation results demonstrate how returns can be achieved and highlight features induced as a result of stochasticity in the model. Suggestions of future research paths are additionally outlined.
The International Organization for Standardization (ISO) deﬁnes Asset Management as the coordinated activity of an organization to realise value from assets (p. 14). They deﬁne an Asset as an item, thing or entity that has potential or actual value to an organization (p. 2). These are clearly general deﬁnitions and apply to many organizations which may not previously have been considered to involve asset management processes.
Indeed, assets may be ﬁnancial, human or physical in nature. The effective management of assets is a crucial requirement for organizations to realize their strategic objectives and achieve their stakeholders expectations. While the standards can be applied to any form of asset management, this work will focus in particular on their application to the management of infrastructure assets.
PROBLEM SPECIFICATION AND MOTIVATION
The question that this case study aims to address is what is the RoI of installing remote condition monitoring sensors on lift assets in the London Underground? The objective is to utilize ABMS and data science in providing an answer to the problem. As originally outlined in Section 2.1.3, the installation of these sensors would enable predictive maintenance capabilities through continuous remote condition monitoring. It would represent a signiﬁcant step in the direction to realize the IoT in Smart Cities.
Historical data was provided by a client of Amey Strategic Consulting, namely Asset Performance Jubilee Northern Piccadilly (APJNP), who manage all operational aspects of the Jubilee, Northern and Piccadilly lines in London Underground. Some applicable data was also obtained from public sources (i.e., the passenger count data was obtained from TfL Legacy Data Feed, the TfL Business Case Development Manual was obtained from a Freedom of Information request). The quality of the raw data sets meant they required cleaning and preparation before relevant in sight could be gained, as with much data gathered on a large scale.
The model comprises four types of agents: Users, Lifts, Contractors and an Asset Manager. Additionally, there are four key objects that do not represent agents. These exist to provide an abstraction layer in the model for adaptation to future work (which may be based on different types of assets). They are: Components, Behaviours, Tasks and Policies. Figure 1 presents a UML class diagram showing links between agents and objects.
During initial simulations runs, it was found to be computationally expensive to continuously generate and simulate many Users when only the impact on their overall journey times during disruptive events is desired. In order to make it possible to run repeated multi-year simulations with the available computational resources, a solution was designed whereby User agents are only generated around times of disruptions. Figure 3 presents the UML state chart for this agent.
Figure 6 shows the absolute difference between the total lift time OOS output from the ABM and the value from historical data for each combination of the parameters. The plots provide different illustrations of the same surface which was ﬁtted using local regression. The left graph displays red points representing the mean output of multiple runs for each parameter combination to demonstrate the parameter space tested. The right graph uses an additional dimension (colour) to offer further detail of the surface itself.
RESULTS AND DISCUSSION
Figure 9 presents the output at each simulation setting. The left graph shows box plots of the annual time Lift agents have spent out of service (OOS). The centre box plot displays the annual Lost Customer Hours (LCH) value accrued by the User agents as a consequence of disruptions. The right bar chart presents the same LCH values summarised as means, to avoid scaling to outliers.
Figure 11 shows further detail of the estimated mean returns (Cost savings minus Investment) and discounted RoI for the threshold level of 0.006. The grey shaded area in the RoI plot represents the range of outcomes as indicated from the variation in simulation results. A key aspect in both of these plots is the time taken to achieve a positive RoI. After this stage, the initial investment has been reclaimed and true returns start to be realised. The mean savings in both plots suggest this time would occur between 6 and 7 years after the initial installation (albeit with signiﬁcant uncertainty).
This research has investigated the potential of integrating ABMS and data science to answer practical business questions within infrastructure asset management. A speciﬁc application was addressed in the installation of condition monitoring sensors to London Underground lift assets in Covent Garden station. The developed ABM was supported by analysis of historical data to present an authentic view of the real system. Key areas for future work were also outlined.
The results from the case study offer a number of conclusions. The ABM suggests that condition monitoring sensors on lift assets for predictive maintenance could realize a positive RoI approximately 6 to 7 years following the initial installation. However, difﬁculty was noted in obtaining a conclusive result as there was a signiﬁcant range of achievable outcomes owing to the stochastic nature of the model. It was also determined that, to realize a positive RoI, the asset management ﬁrm must ensure that the predictive maintenance strategy is appropriately implemented and adopted within its current maintenance system.
A key objective of this research was to investigate opportunities for combining ABMS and data science in the ﬁeld of infrastructure asset management. Based on insights from this work, it is clear that this integration possesses great capacity for capturing the complexity of the modern world when compared to other forms of simulation and analysis. However, it has yet to fully graduate from its origins in theoretical application to a reﬁned process for answering practical business questions. The hope is that the current work has highlighted the underlying value in this approach and will serve to further this emerging paradigm.
Source: Stanford University
Authors: Charles Houston | Stephen Gooberman-Hill | Richard Mathie | Andrew Kennedy | Yunxi Li | Pedro Baiz