Route Selection and Distribution Cost of Express Delivery: An Urban Metro Network Based Study
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.
Copyright (c) 2021 Junhua Guo, Yutao Ye, Yafeng Ma
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
