Route Selection and Distribution Cost of Express Delivery: An Urban Metro Network Based Study

  • Junhua Guo East China Jiaotong University, School of Transportation & Logistics
  • Yutao Ye East China Jiaotong University, School of Transportation & Logistics
  • Yafeng Ma East China Jiaotong University, School of Transportation & Logistics
Keywords: express delivery, metro network, improved genetic algorithm, path optimization

Abstract

Route selection and distribution costs of express delivery based on the urban metro network, referred to as metro express delivery (MeD), is addressed in this study. Considering the characteristics of express delivery transportation and the complexity of the urban metro network, three distribution modes of different time periods are proposed and a strict integrated integer linear programming model is developed to minimize total distribution costs. To effectively solve the optimal problem, a standard genetic algorithm was improved and designed. Finally, the Ningbo subway network is used as an example to confirm the practicability and effectiveness of the model and algorithm. The results show that when the distribution number of express delivery packages is 1980, the three different MeD modes can reduce transportation costs by 40.5%, 62.0%, and 59.0%, respectively. The results of the case analysis will help guide express companies to collaborate with the urban metro network and choose the corresponding delivery mode according to the number of express deliveries required.

References

National Bureau of Statistics of China. Express Industry Developme Data. Available from: http://data.stats.gov.cn/easyquery.htm?cn=C01

Goldman T, Gorham R. Sustainable urban transport: Four innovative directions. Technology in Society. 2006;28(1-2): 261-73. DOI: 10.1016/j.techsoc.2005.10.007

Yang J, Guo J, Ma S. Low-carbon city logistics distribution network design with resource deployment. Journal of Cleaner Production. 2016;119: 223-8. DOI: 10.1016/j.jclepro.2013.11.011

European Commission. Towards a new culture for urban mobility. Green paper. European Union, Brussels; 2007.

Trentini A, Mahléné N. Toward a Shared Urban Transport System Ensuring Passengers & Goods Cohabitation. TeMA - Journal of Land Use Mobility Environmental Progress & Sustainable Energy. 2010;3(2). DOI: 10.6092/1970-9870/165

He Y, Yang S, Chan C-Y, Chen L, Wu C. Visualization Analysis of Intelligent Vehicles Research Field Based on Mapping Knowledge Domain. IEEE Transactions on Intelligent Transportation Systems. 2020;PP(99): 1-16. DOI: 10.1109/ TITS.2020.2991642

Zhao PX, Gao WQ, Han X, Luo WH. Bi-Objective Collaborative Scheduling Optimization of Airport Ferry Vehicle and Tractor. International Journal of Simulation Modelling. 2019;18(2): 355-65. DOI: 10.2507/ijsimm18(2)co9

Kikuta J, Tatsuhide I, Tomiyama I, Yamamoto S, Yamada T. New Subway-Integrated City Logistics Szystem. Procedia - Social and Behavioral Sciences. 2012;39: 476-89. DOI: 10.1016/j.sbspro.2012.03.123

Diziain D, Taniguchi E, Dablanc L. Urban Logistics by Rail and Waterways in France and Japan. Procedia - Social and Behavioral Sciences. 2014;125: 159-70. DOI: 10.1016/j.sbspro.2014.01.1464

Metropolitan Transportation Authority. NYCT Trash Can Free Stations Pilot Update. Metropolitan Transportation Authority. Report Presentation, 2014.

Reece D, Marinov M. Modelling the implementation of a baggage transport system in newcastle upon tyne for passengers using mixedmode travel. Transport Problem. 2015;10(4): 149-55. DOI: 10.1016/j.sbspro.2014.01.1464

Brice D, Marinov M, Rüger B. A Newly Designed Baggage Transfer System Implemented Using Event-Based Simulations. Urban Rail Transit. 2015;1(4): 194-214. DOI: 10.1007/s40864-015-0027-4

Ghilas V, Demir E, Woensel TV. A scenario-based planning for the pickup and delivery problem with time windows, scheduled lines and stochastic demands. Transportation Research Part B: Methodological. 2016;91: 34-51. DOI: 10.1016/j.trb. 2016.04.015

Holguín-Veras J, Wang C, Browne M, Hodge SD, Wojtowicz J. The New York City Off-hour Delivery Project: Lessons for City Logistics. Procedia - Social and Behavioral Sciences. 2014;125: 36-48. DOI: 10.1016/j.sbspro.2014.01.1454

Zhang H, Tang L, Yang C, Lan S. Locating electric vehicle charging stations with service capacity using the improved whale optimization algorithm. Advanced Engineering Informatics. 2019;41. DOI: 10.1016/j.aei.2019.02.006

Zhang H, Cui Y. A model combining a Bayesian network with a modified genetic algorithm for green supplier selection. Simulation. 2019;95(12): 1165-83. DOI: 10.1177/0037549719826306

Cheng R, Gen M. Genetic algorithms and engineering design. John Wiley; 1997.

Ningbo Rail Transit. Ningbo Urban Rapid Rail Transit Construction Plan (2013-2020). Available from: http://www.nbmetro.com/about_plan.php?info/72013

Zhou F, Zhang J, Zhou G. Subway-based Distribution Network Routing Optimization Problem with Time Windows. Journal of Transportation Systems Engineering and Information Technology (in Chinese). 2018;18(5): 92-8. DOI: 10.16097 /j.cnki.1009-6744.2018.05.014

The People's Government of Ningbo. 2018 Ningbo Statistical Yearbook. Ningbo Municipal Statistics Bureau. Report number: 12, 2018.

Published
2021-04-01
How to Cite
1.
Guo J, Ye Y, Ma Y. Route Selection and Distribution Cost of Express Delivery: An Urban Metro Network Based Study. Promet - Traffic&Transportation. 2021;33(2):283-96. DOI: 10.7307/ptt.v33i2.3592
Section
Articles