Bin Xue;Kexin Chang;Yufeng Fan;Xingbin Chen;Tae Wan Kim;Bingsheng Liu
{"title":"An Integrated Framework of Multidisciplinary Decision Making Under Uncertainty for Sustainable Infrastructure Development","authors":"Bin Xue;Kexin Chang;Yufeng Fan;Xingbin Chen;Tae Wan Kim;Bingsheng Liu","doi":"10.1109/TEM.2025.3540270","DOIUrl":null,"url":null,"abstract":"Uncertainty management in multidisciplinary decision making (MDM) involving stakeholders with discipline-specific expertise is imperative for the operations of developing urban infrastructure projects with multidimensional sustainability goals. Preference uncertainty and outcome uncertainty must be addressed simultaneously for theorizing and modeling in such MDM processes. Thus, in this article, we formalize an integrated MDM (iMDM) system to consistently mitigate preference uncertainty in decision alternative evaluation and expeditiously manage outcome uncertainty in decision alternative selection. Unlike the existing decision-making methods that often overlook different uncertainty characteristics in multidisciplinary operations management, the proposed system accounts for both uncertainties by specified information representation and integrated information optimization to enlarge decision spaces. Empirical evaluations in three real-world scenarios indicate that the iMDM system can mitigate and manage uncertainty to derive distinguishable alternative rankings and to generate optimized Pareto alternative sets. We further validate the effectiveness of the system using Charrette tests by quantifying the consistency and expeditiousness of both managing uncertainty and deriving desirable decision alternatives. Our contributions build upon the theoretical foundation of MDM under uncertainty and extend sustainable operations management science by clarifying decision information rationales from an uncertainty management perspective. Practically, findings benefit infrastructure operations’ managers and urban planners in making sustainability decisions in visualized, integrated, and automated manners.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"751-767"},"PeriodicalIF":4.6000,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Engineering Management","FirstCategoryId":"91","ListUrlMain":"https://ieeexplore.ieee.org/document/10878794/","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
Uncertainty management in multidisciplinary decision making (MDM) involving stakeholders with discipline-specific expertise is imperative for the operations of developing urban infrastructure projects with multidimensional sustainability goals. Preference uncertainty and outcome uncertainty must be addressed simultaneously for theorizing and modeling in such MDM processes. Thus, in this article, we formalize an integrated MDM (iMDM) system to consistently mitigate preference uncertainty in decision alternative evaluation and expeditiously manage outcome uncertainty in decision alternative selection. Unlike the existing decision-making methods that often overlook different uncertainty characteristics in multidisciplinary operations management, the proposed system accounts for both uncertainties by specified information representation and integrated information optimization to enlarge decision spaces. Empirical evaluations in three real-world scenarios indicate that the iMDM system can mitigate and manage uncertainty to derive distinguishable alternative rankings and to generate optimized Pareto alternative sets. We further validate the effectiveness of the system using Charrette tests by quantifying the consistency and expeditiousness of both managing uncertainty and deriving desirable decision alternatives. Our contributions build upon the theoretical foundation of MDM under uncertainty and extend sustainable operations management science by clarifying decision information rationales from an uncertainty management perspective. Practically, findings benefit infrastructure operations’ managers and urban planners in making sustainability decisions in visualized, integrated, and automated manners.
期刊介绍:
Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.