文献类型：期刊文献

英文题名：Equity-aware routing optimization for hazardous chemicals considering time-varying conditions and link disruption risks

作者：Liu, Liping[1];Zhang, Kefei[1];Shao, Xiaofeng[2];Wen, Yuxuan[1]

机构：[1]East China Univ Sci & Technol, Sch Business, Shanghai, Peoples R China;[2]Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Shanghai, Peoples R China

年份：2026

卷号：275

外文期刊名：RELIABILITY ENGINEERING & SYSTEM SAFETY

收录：;EI(收录号：20261720581780);WOS:【SCI-EXPANDED(收录号:WOS:001758163100001)】；

语种：英文

外文关键词：Hazardous chemical transportation; Time-varying conditions; Link disruption; Risk equity; Multi-objective routing optimization

摘要：Hazardous chemical transportation accidents can readily lead to severe casualties, as well as substantial environmental and property damage. Hazardous chemical transportation routing decisions are jointly affected by time-varying conditions, link disruptions, and increasing public concerns about the risk equity. Yet existing studies often treat risk assessment, time-varying conditions, disruption scenarios, and risk equity in isolation, limiting their ability to explain peak-period risk surges and disruption-induced risk shifting. To fill this gap, we first propose a comprehensive HCT risk assessment model that explicitly embeds multiple time periods and scenario-based link disruptions to characterize the evolving accident likelihood and consequence severity. Second, we examine how transportation risk is redistributed across space over multiple time periods when different disruption scenarios occur, and develop a hazardous chemical transportation risk equity metric that reflects time-varying, disruption-induced risk deviation. Third, we formulate an equity-aware multi-objective routing model that jointly minimizes total risk, risk deviation, and transportation cost under multiple time periods and disruption scenarios, while imposing threshold-type constraints on risk, which jointly captures expected risk and risk variability. Then we develop an enhanced Third-generation Non-dominated Sorting Genetic Algorithm with constraint-aware population repair to ensure feasibility and maintain diversity under time-dependent disruptions. A Shanghai case shows that incorporating link disruptions and time-varying conditions improves safety and equity, especially at peak hours.