journal article·2019-04-01Open access

Optimization in Decision Making in Infrastructure Asset Management: A Review

Lin Chen, Qiang Bai

Applied Sciences· Volume 9· Issue 7

Abstract

Infrastructure assets, serving everyone’s daily life, are an essential foundation of any society. Their management faces a wide range of challenges. Hence optimization methods are increasingly applied to assist making management decisions in infrastructure asset management (IAM). A large number of articles apply a broad range of optimization methods in their decision making (DM) and achieve great results. However, they mainly focus on individual methods and a comprehensive knowledge, given the broad range of optimization methods, is hardly discussed. Hence it is valuable to analyze and graphically present the existing knowledge on this subject. This paper, based on a total of 337 articles, provides an overall review of the applications of optimization when making management decisions in IAM, with the intension of enhancing the optimization application and method selection and guiding the future research in this field. More specifically, this paper introduces the application process of optimization when assisting DM in IAM, summarizes the previous application research, and discusses the popular optimization methods applied in DM in IAM. According to the literature review, this paper confirms optimization can effectively assist DM in IAM and a wide range of optimization methods are applicable to assist a variety of DM problems. The recommendations on the applications and selection of optimization methods in the context of IAM are also made to facilitate the applications.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Infrastructure Maintenance and Monitoring · Water Systems and Optimization · Underground infrastructure and sustainability

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Highway, Chang’an University, Xi’an 710064, China

Funding

Fundamental Research Funds for the Central Universities

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