Dynamic Asset Management Research Articles

Digital twin enabled fault detection and diagnosis process for building HVAC systems

The emerging concept of digital twins outlines the pathway towards intelligent buildings. Although abundant building data carries an overwhelming amount of information, if not well exploited, the redundant and irrelevant data dimensions result in the overfitting problem and heavy computational load. Taking the fault detection and diagnosis process for building HVAC systems as the case, this paper adopts a symbolic artificial intelligence technique to identify informative sensory dimensions for building-specific faults by exploring the symbolic representation of labelled time-series. To preserve this ad-hoc temporal knowledge in the digital twin ecosystem, machine-readable fault tags are defined to label corresponding sensor entities. A digital twin data platform is developed to annotate the real-time data with fault tags and produce filtered low-latency data streams associated with a specified tag to automate this process. This paper describes a digital twin-based approach to automatically identify and pick up informative data to support dynamic asset management.

An openBIM Approach to IoT Integration with Incomplete As-Built Data

Digital Twins (DT) are powerful tools to support asset managers in the operation and maintenance of cognitive buildings. Building Information Models (BIM) are critical for Asset Management (AM), especially when used in conjunction with Internet of Things (IoT) and other asset data collected throughout a building’s lifecycle. However, information contained within BIM models is usually outdated, inaccurate, and incomplete as a result of unclear geometric and semantic data modelling procedures during the building life cycle. The aim of this paper is to develop an openBIM methodology to support dynamic AM applications with limited as-built information availability. The workflow is based on the use of the IfcSharedFacilitiesElements schema for processing the geometric and semantic information of both existing and newly created Industry Foundation Classes (IFC) objects, supporting real-time data integration. The methodology is validated using the West Cambridge DT Research Facility data, demonstrating good potential in supporting an asset anomaly detection application. The proposed workflow increases the automation of the digital AM processes, thanks to the adoption of BIM-IoT integration tools and methods within the context of the development of a building DT.

Multi-period maintenance planning for public buildings: A risk based approach for climate conscious operation

Improving the environmental performance of built environments has received wider interest in the recent past. Asset management is an under-utilized strategy to enhance and maintain desired environmental performance of a building at low life cycle costs (LCC). Current asset management decision-making methods are not fully capable of incorporating future technological changes and complex time sensitive environmental demands. There is a knowledge gap in incorporating factors such as environmental performance, risk and time sensitivity to foster dynamic asset management decision making. This paper presents a multi-period asset management method for public buildings to minimize LCC and attain climate action goals. A scenario planning approach is used to incorporate future changes in technologies, costs and organizational policies. Risk based prioritization is used to compare and select maintenance alternatives in a given period. Value at risk is used to identify the lowest risk capital planning strategy for an organization. The proposed approach was demonstrated using an operating aquatic centre building in British Columbia, Canada. Three possible future scenario were considered. Proposed method was used to develop multi-period maintenance plans for future scenarios. Based on the analysis, Great Transitions scenario is expected to achieve the least financial risk. The proposed approach enable capital asset planning by complying with changing climate action targets in a stochastic environment.

Dynamic asset management with risk-sensitive criterion and non-negative factor constraints: a differential game approach

In this paper, we study continuous time portfolio optimization problem where individual securities are directly affected by economic factors. We consider the risk-sensitive criterion function as is familiar in the robust control literature. This is the natural setting for studying the infinite horizon case of the control problem arising in portfolio optimization. Our result extends earlier works by imposing explicitly the non-negativity constraint on the economic factors. This is achieved by using reflected diffusions. The risk-sensitive control problem with reflected diffusion is then converted into a stochastic differential game. The lower value of this game leads immediately to the desired optimal strategy. Also we prove the existence of unique strong solution to reflected diffusions with bounded measurable drift coefficient which is the first result of its kind for higher dimensional reflected diffusions.

Optimal Portfolio Choice with Dynamic Asymmetric Correlations and Transaction Constraints

This paper develops a framework for constructing portfolios with superior out-of-sample performance in the presence of estimation errors. Our framework relies on solving the classical mean-variance problem with dynamic portfolio rebalancing at a comparatively-high frequency level. With the employment of A-DCC GARCH model, we found that the usage of turnover constraints will tend to enhance the performance of the portfolios sufficiently high to overcome transaction costs in practice. For a long-only optimal portfolio based on a linear combination of two different strategies we find a return exceeding 51% per annual with annual volatility equal to 35% over the 1998-2007 period. We argue that the advantage of our framework comes from the mean-reverting nature of the stock market and the impact of the estimation errors in high frequency level. Our works indicate that one can successfully move from ordinary monthly or weekly adjusting strategies to high frequency and dynamic asset management without the significant increase of transaction costs.

A new choice of dynamic asset management: the variable proportion portfolio insurance

The constant proportion portfolio insurance (CPPI) achieves the advantage of simplicity due to its constant multiple. However, a dynamic multiple could improve the effectiveness of portfolio management. In this article, we provide a complete and detailed examination of the mechanism of variable proportion portfolio insurance (VPPI) strategy. The multiple of the VPPI states that when the stock price goes up, the multiple gets larger accordingly and when the stock price goes down, the multiple gets smaller. A portfolio insurance strategy with this discipline could yield better performance. Based on this principle, we recommend an exponential proportion portfolio insurance (EPPI). In addition, we also propose a new performance measure for portfolio insurance. Compared with the CPPI, simulation and empirical evidence support that the EPPI works better in both upside capture and downside protection, implying that the EPPI could be an effective tool for asset management.

Risk‐based Decisions on the Asset Structure of a Bank under Partial Economic Information

We present a model of a bank's dynamic asset management problem in the case of partially observed future economic conditions and with regulatory requirements governing the level of risk taken. The result is an optimal control problem with a random terminal condition arising from the partial observation of a parameter of a maximized functional. The Stochastic Maximum Principle reduces the problem to finding a solution to a Forward Backward Stochastic Differential Equation (FBSDE). As optimization usually implies the dependence of the forward equation on solutions of the backward equation we allow the drift and diffusion of the forward part to be functions of the solution of the backward equation. The necessary conditions for the existence of solutions of FBSDE in such a form are derived. A numerical scheme is then implemented to solve a particular case.

Global intermodal tank container management for the chemical industry

The scale of the global chemical industry is enormous: in 2003, the total value of global production exceeded US$1.7 trillion. International logistics is especially crucial to the high-value chemicals industry, since raw materials sources, production facilities, and consumer markets are distributed globally. Fluctuating demand, imbalanced trade flows, and expensive transportation equipment necessitate dynamic asset management. This paper focuses on asset management problems faced by tank container operators, and formulates an operational tank container management problem as a large-scale multi-commodity flow problem on a time-discretized network. By integrating container routing and repositioning decisions in a single model, total operating costs and fleet sizes can be reduced. A computational study verifies this hypothesis.

Risk-sensitive portfolio optimization on infinite time horizon

We consider a continuous time portfolio optimization problems on an infinite time horizon for a factor model, recently treated by Bielecki and Pliska ["Risk-sensitive dynamic asset management", Appl. Math. Optim. , 39 (1990) 337-360], where the mean returns of individual securities or asset categories are explicitly affected by economic factors. The factors are assumed to be Gaussian processes. We see new features in constructing optimal strategies for risk-sensitive criteria of the portfolio optimization on an infinite time horizon, which are obtained from the solutions of matrix Riccati equations.

Risk-Sensitive Dynamic Asset Management

This paper develops a continuous time portfolio optimization model where the mean returns of individual securities or asset categories are explicitly affected by underlying economic factors such as dividend yields, a firm's return on equity, interest rates, and unemployment rates. In particular, the factors are Gaussian processes, and the drift coefficients for the securities are affine functions of these factors. We employ methods of risk-sensitive control theory, thereby using an infinite horizon objective that is natural and features the long run expected growth rate, the asymptotic variance, and a single risk-aversion parameter. Even with constraints on the admissible trading strategies, it is shown that the optimal trading strategy has a simple characterization in terms of the factor levels. For particular factor levels, the optimal trading positions can be obtained as the solution of a quadratic program. The optimal objective value, as a function of the risk-aversion parameter, is shown to be the solution of a partial differential equation. A simple asset allocation example, featuring a Vasicek-type interest rate which affects a stock index and also serves as a second investment opportunity, provides some additional insight about the risk-sensitive criterion in the context of dynamic asset management.