Procedures for Cross Asset Management Optimisation

Deix, Stefan; Alten, Karoline; Weninger‐Vycudil, Alfred

doi:10.1016/j.sbspro.2012.06.1175

Cited by 8 publications

(12 citation statements)

References 1 publication

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“…To limit the scope of our paper, we wanted to emphasize on the possibility of interactions among infrastructures in our model and we see the interaction between asset owners and managers as a future direction of our research. These interactions between asset-managers gives rise to the possibility using this model for studying interconnected infrastructure along the lines of cross-asset investment strategies (Deix et al 2012).…”

Section: Discussionmentioning

confidence: 99%

A layered approach to model interconnected infrastructure and its significance for asset management

Bhamidipati

Lei

Herder

2016

European Journal of Transport and Infrastructure Research

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Physical infrastructures facilitate much of societal and economic wellbeing of countries, regions and urban areas. In our increasingly urbanizing world, infrastructures in urban areas are densely located and interconnected. The effects of this interconnectedness are being studied increasingly, particularly in light of climate change effects. In this paper, we develop an agent-based simulation model that allows us to study interconnected infrastructure. We present a layered approach that is analogous to GIS overlay approaches, which allows us to integrate representations of different infrastructures. We explore how this approach can help asset managers to gain insights in interconnected infrastructure by estimating their total damage and repair requirements during a flood event. The results show a difference in these estimates, when compared to non-integrated models, highlighting the need for asset managers to consider interconnectedness in infrastructure.

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Section: Discussionmentioning

confidence: 99%

A layered approach to model interconnected infrastructure and its significance for asset management

Bhamidipati

Lei

Herder

2016

European Journal of Transport and Infrastructure Research

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“…Strategic asset management, therefore, can be expected to involve the long-term direction for an organization to manage its resources in fulfilling stakeholder expectations (Johnson et al, 2008). This incorporates key elements covering such financial resources and natural environment matters as corporate real estate asset management strategies (Haynes and Nunnington, 2010); efficient public sector asset management (Grubiši c et al, 2009); managing government property assets (Kaganova, 2010); road asset optimization (Deix et al, 2012a(Deix et al, , 2012b; improving physical asset performance in the process industry (Schuman and Brent, 2005); optimization of building maintenance, repair and renovation activities (Grussing and Liu, 2014); asset management equipment optimization (Campbell et al, 2016); and facilities managers preparing for climate change-related disasters (Warren, 2010b). Chiang et al (2020) collect data using the Delphi approach to ask 20 experts to determine the first and second level of indicators to be used in their project.…”

Section: Strategic Asset Managementmentioning

confidence: 99%

“…Strategic asset management , therefore, can be expected to involve the long-term direction for an organization to manage its resources in fulfilling stakeholder expectations (Johnson et al , 2008). This incorporates key elements covering such financial resources and natural environment matters as corporate real estate asset management strategies (Haynes and Nunnington, 2010); efficient public sector asset management (Grubišić et al , 2009); managing government property assets (Kaganova, 2010); road asset optimization (Deix et al , 2012a, 2012b); improving physical asset performance in the process industry (Schuman and Brent, 2005); optimization of building maintenance, repair and renovation activities (Grussing and Liu, 2014); asset management equipment optimization (Campbell et al , 2016); and facilities managers preparing for climate change-related disasters (Warren, 2010b).…”

Section: Strategic Asset Management and Optimizationmentioning

confidence: 99%

Toward a public sector asset optimization strategy: the case of Indonesia

Nanang

Susilawati²,

Skitmore³

2022

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Purpose Governments in developing countries manage their considerable state assets for public service delivery directly. In Indonesia, the Directorate of State Asset Management responsible for developing the national strategy for state asset optimization requires the determination of key elements and prioritization tools. The purpose of this paper is to show that a simple calculation using the combination of the balanced scorecard (BCS) and analytical hierarchy process (AHP) will help in the prioritization of strategy development. Design/methodology/approach A questionnaire survey of 131 multistakeholder respondents to identify the most important key elements and the best alternative for asset optimization was done in this study. Findings The respondents agree on the most important key elements, and that the best alternative for asset optimization is the efficient maintenance of assets. Competitive human resources comprise the recommended second key element, and that improvements in asset performance and value will improve public service as the second-highest alternative. This study also shows the importance of the integration of asset optimization in existing government strategic instruments supported by a comprehensive data set related to public assets and their performance. Originality/value This paper provides a new contribution to integrating asset optimization strategies as the core of the organization’s performance and prioritization strategies. Additional BSC perspectives are suggested, with the inclusion of AHP for prioritization. In addition, this study includes the opinions of all the stakeholders, from external users to the central management. The flexibility of the tools to adapt to the existing strategic framework will allow their application by different agencies and in different countries.

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“…TAM has been generally described as a strategic and systematic process for life‐cycle management of physical assets, focusing on engineering practices for resource allocation and utilization, with the objective of better decision‐making based upon quality information and well‐defined objectives (AASHTO, 2011). TAM involves the integration of the different program areas to ensure more efficient decision‐making regarding the allocation of budgets across the program areas (Cambridge Systematics, Inc., PB Consult, Inc., & Texas Transportation Institute, 2006; Deix, Alten, & Weninger‐Vycudil, 2012; ERF, 2015).…”

Section: Introductionmentioning

confidence: 99%

Methodology for analyzing the trade‐offs associated with multi‐objective optimization in transportation asset management under uncertainty

Bai

Miralinaghi

Labi

et al. 2020

Computer aided Civil Eng

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Trade‐off analysis, one of the key tenets of multi‐objective optimization for the project selection problem in Transportation Asset Management (TAM), can help decision‐makers quantify and comprehend the consequences of different resource allocations in terms of the multiple measures of system performance. In analyzing TAM trade‐offs, it is vital to account duly for the uncertainties associated with these system‐wide performance measures. In this paper, we present a methodology that integrates chance‐constraint programming, the Lindeberg Central Limit Theorem, and a hybrid NSGA II method, to address the performance uncertainties associated with the TAM multi‐objective optimization problem. Through analyzing the trade‐offs between expenditure and performance, and between different performance measures, we generate Pareto frontiers at different confidence levels using a hybrid NSGA II method. We demonstrate the proposed methodology using a case study involving real‐life assets and the expected cost and performance benefits of projects associated with these assets. Regarding the trade‐off between cost and performance, we determine the extent to which the strengths of these relationships vary across different confidence levels. We find that, generally, for a given network performance level, a higher expenditure is needed to achieve a high confidence level compared to the expenditure needed to achieve a low confidence level, and more importantly, measures these sensitivities. This is the “Price of Confidence” concept. Regarding the trade‐off between different pairs of performance measures under budgetary constraints, we show how these relationships vary with the confidence level specified for the analysis, and we measure the extent to which higher confidence level requirements translate into lower levels of overall system‐wide performance.

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Procedures for Cross Asset Management Optimisation

Cited by 8 publications

References 1 publication

A layered approach to model interconnected infrastructure and its significance for asset management

A layered approach to model interconnected infrastructure and its significance for asset management

Toward a public sector asset optimization strategy: the case of Indonesia

Methodology for analyzing the trade‐offs associated with multi‐objective optimization in transportation asset management under uncertainty

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