考虑模糊不确定的地铁货运系统成网布局规划  

Layout Planning of Metro-based Underground Logistics System Network Considering Fuzzy Uncertainties

作　　者：胡万杰 董建军 任睿 陈志龙 Hu Wanjie;Dong Jianjun;Ren Rui;Chen Zhilong(Faculty of Architecture,Civil and Transportation Engineering,Beijing University of Technology,Beijing 100124,China;College of Civil Engineering,Nanjing Tech University,Nanjing 211800,China;College of Defense Engineering,Army Engineering University of PLA,Nanjing 210007,China)

机构地区：[1]北京工业大学城市建设学部,北京100124 [2]南京工业大学土木工程学院,江苏南京211800 [3]陆军工程大学国防工程学院,江苏南京210007

出　　处：《系统仿真学报》2022年第8期1725-1740,共16页Journal of System Simulation

基　　金：国家自然科学基金(71631007,71971214)。

摘　　要：为解决不确定条件下依托地铁开展城市地下物流的网络设计与优化问题,提出双层地铁-货运系统(metro-based underground logistics system,M-ULS)设施构成,建立地下货运环境效益期望下的M-ULS网络流量配置模型,以综合成本和系统利用率为目标,构建M-ULS网络选址-分配-路径模糊随机规划模型并提出其线性精确化方法,设计离散二进制混沌遗传粒子群算法和精确算法进行组合寻优。结果表明:与确定性情景相比,模糊随机情景下的M-ULS最佳布局方案更加保守,地下物流的环境外部效益可以抵消10%~12%的网络建设运营成本。Aming at the network design and optimization of metro-based urban underground logistics under uncertainties,the facility components of two-tier metro-based underground logistics system(MULS)are proposed.Focus on the comprehensive costs and system utilization rate,a M-ULS network flow assignment model is established based on the expectation of environmental benefits of underground freight transport.a M-ULS network location-allocation-routing fuzzy random programming model is established,and a crisp linearization method is presented.A solution portfolio combining discrete binary chaos particle swarm optimization-genetic algorithm and exact algorithms is designed for combinatorial optimization.Effectiveness of the presented models and algorithms is verified in real-life cases.Results show that the best M-ULS layout schemes in fuzzy random scenarios are more conservative than those in deterministic scenarios.The external environment benefits of underground logistics can offset 10%~12%of costs in network construction and operations.

关 键 词：城市地下物流系统 地铁 选址与布局 粒子群算法 需求不确定 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]