基于粗糙集和云模型的港口危险化学品物流系统脆弱性评价
Vulnerability evaluation of port hazardous chemicals logistics system based on rough set and cloud modeling
刘翠莲 1秦坤 1姜昊 1李佳璐2
作者信息
- 1. 大连海事大学交通运输工程学院,辽宁大连116026
- 2. 大连公共交通建设投资集团有限公司,辽宁大连116000
- 折叠
摘要
为有效降低港口危险化学品事故发生率,提升危险化学品物流系统稳定性,提出了一种基于粗糙集理论和云模型的港口危险化学品物流系统脆弱性评价模型.首先基于脆弱性三要素——暴露性、敏感性、适应性,构建港口危险化学品物流系统脆弱性初始评价指标体系,利用粗糙集理论进行指标约简以及确定权重;然后建立港口危险化学品物流系统脆弱性评价云模型;最后对我国6个主要危险化学品港口进行危险化学品物流系统脆弱性评价.结果表明,宁波舟山港、青岛港的脆弱性较低,危险化学品物流系统较为稳定;日照港、烟台港的脆弱性较高,存在实际吞吐量大于泊位通过能力、危险化学品专项检查不彻底、危险化学品物流信息化水平较低等薄弱环节,进而提出针对性的发展建议.
Abstract
In order to effectively reduce the incidence rate of hazardous chemicals accidents in ports and improve the stability of hazardous chemicals logistics system,a vulnerability evaluation model of port hazardous chemicals logistics system based on rough set theory and cloud model is proposed.Firstly,based on the three elements of vulnerability-exposure,sensitivity,and adaptability,the initial evaluation index system of vulnerability of port hazardous chemicals logistics system is constructed,and the rough set theory is used to simplify the indexes as well as to determine the weights;then,a cloud model for vulnerability evaluation of port hazardous chemicals logistics system is established;finally,the vulnerability evaluation of hazardous chemicals logistics system is carried out for six major hazardous chemicals ports in China.The results show that:the vulnerability of Ningbo-Zhoushan Port and Qingdao Port is low,and the hazardous chemicals logistics system is relatively stable;the vulnerability of Rizhao Port and Yantai Port is high,and there are weaknesses such as the actual throughput is larger than the berth capacity,the special inspection of hazardous chemicals is not thorough,and the informationization level of hazardous chemicals logistics is low,and then put forward the target-oriented development suggestions.
关键词
港口危险化学品物流系统/脆弱性/评价指标体系/云模型/粗糙集Key words
port hazardous chemicals logistics system/vulnerability/evaluation index system/cloud model/rough set引用本文复制引用
出版年
2024
NETL
NSTL
万方数据