图像模糊的WASPAS方法来选择最后一英里的交付模式:贝尔格莱德的一个案例研究
Vladimir Simić1, Dragan Lazarević1, Momčilo Dobrodolac1
1Department of Postal and Telecommunication Traffic, Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, Belgrade, 11010 Serbia.
European transport research review
|April 16, 2024
概括
该研究引入了一种新的图像模糊的WASPAS方法来选择最佳的最后一英里交付 (LMD) 模式. 在贝尔格莱德,后台机被认为是最佳的LMD解决方案,提供可持续和高效的方法.
科学领域:
- 后勤和运输科学 后勤和运输科学
- 运营研究 运营研究
- 决策科学 决策科学 决策科学
背景情况:
- 增加的城市交通和环境问题需要可持续的最后一英里交付 (LMD) 解决方案.
- 医疗危机凸显了对弹性和高效的LMD系统的需求.
- 选择最佳的LMD模式涉及复杂的,不确定性下的多标准决策.
研究的目的:
- 在图片模糊的环境中,介绍权重总和产品评估 (WASPAS) 方法的扩展.
- 为选择最合适的LMD模式提供强大的决策工具.
- 解决在LMD规划中不准确和不确定的信息所带来的挑战.
主要方法:
- 开发一种使用图片模糊集 (PFS) 的新型多标准决策 (MCDM) 方法.
- 应用扩展的WASPAS方法来处理中立/拒绝信息和不确定性.
- 与现有的基于PFS的MCDM方法进行比较分析,以验证可靠性和稳定性.
主要成果:
- 拟议的图片模糊的WASPAS方法显示出高可靠性,稳定性和一致性.
- 在贝尔格莱德的一项案例研究中,将后自动机确定为最有效的LMD模式.
- 排名LMD模式:自动售货机,货物自行车,无人机,传统配送,自动驾驶汽车和管道运输.
结论:
- 图像模糊的WASPAS方法为全球城市环境中优化LMD操作提供了有价值的工具.
- 该方法可以适应解决各种不确定性特征的复杂MCDM问题.
- 调查结果为物流公司提供了可操作的见解,旨在实现可持续和高效的LMD.
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