Economic and ecological optimization of the London urban logistics system considering infection risk during pandemic periods

Document Type: Research Paper

Author

School of Strategy and Leadership, Coventry University, Coventry, UK

Abstract

Urban delivery, especially the last-mile delivery, has become an increasingly important area in the global supply chain along with the boom of e-commerce. Delivery companies and merchants can introduce some innovative solutions such as the equipment of autonomous vehicles (AVs) to decrease their operating costs, environmental impact, and social risks during the delivery process. This paper mainly develops a mathematical model to get the best allocation of AVs among city logistics centers (CLCs) as a mixed delivery method. The advantage of the presented model stems from considering the equipment cost, the delivery cost, and the CO2 emission, which is measured through social carbon cost (SCC). In addition, this paper establishes a risk model considering the impact of seasonal variations to evaluate the infection risk of delivery during pandemic periods for four potential delivery scenarios: customers going to CLCs, ordering online and picking-up at CLCs, delivering by traditional vehicles (TVs), and delivering by the mixed method with the optimal allocation of AVs. The research finds the optimal allocation for a London case, reveals the relationship between the nominal service capacity (NCpa) of CLCs and the optimal number of CLCs equipped with AVs, concludes that the more CLCs are equipped with AVs, the fewer CO2 emissions and the fewer citizens will be infected, and provides some managerial insights that may help delivery companies and merchants make appropriate decisions about the allocation of AVs.

Keywords


Abbasi, M., and Nilsson, F. (2016). Developing environmentally sustainable logistics: Exploring themes and challenges from a logistics service provider's perspective. Transportation Research Part D: Transport and Environment, Vol. 46, pp.273-283.

Akeb, H., Moncef, B., and Durand, B. (2018). Building a collaborative solution in dense urban city settings to enhance parcel delivery: An effective crowd model in Paris. Transportation Research Part E: Logistics and Transportation Review, Vol. 119, pp.223-233.

Alho, A., Silva, J., Sousa, J., and Blanco, E. (2018). Improving mobility by optimizing the number, location, and usage of loading/unloading bays for urban freight vehicles. Transportation Research Part D: Transport and Environment, Vol. 61, pp.3-18.

Anderhofstadt, B. and Spinler, S. (2020). Preferences for autonomous and alternative fuel-powered heavy-duty trucks in Germany. Transportation Research Part D: Transport and Environment, Vol. 79, p.102232.

Batty, M. (2008). The size, scale, and shape of cities. Science, Vol. 319(5864), pp.769-771.

Bettencourt, L.M. (2013). The origins of scaling in cities. Science, Vol. 340(6139), pp.1438-1441.

Brealey, R.A., Myers, S.C., and Allen, F. (2010). Principles of Corporate finance, 10th ed., USA: McGraw-Hill/Irwin.

Brummelen, J., O’Briena, M., Gruyerb, D., and Najjaran, H. (2018). Autonomous vehicle perception: The technology of today and tomorrow. Transportation Research Part C: Emerging Technologies, Vol. 89, pp.384-406.

Burns, L.D. (2013). Sustainable mobility: a vision of our transport future. Nature, Vol. 497(7448), pp.181-182.

Chabot, T., Bouchard, F., Legault-Michaud, A., Renaud, J., and Coelho, L. (2018). Service level, cost, and environmental optimization of collaborative transportation. Transportation Research Part E: Logistics and Transportation Review, Vol. 110, pp.1-14.

Chanchaichujit, J., Saavedra-Rosas, J., Quaddus, M. and West, M. (2016). The use of an optimization model to design a green supply chain. The International Journal of Logistics Management, Vol. 27(2), pp.595-618.

Chatzitheodoroua, K., Skouloudisb, A., Evangelinosb, K., and Nikolaoua, I. (2019). Exploring socially responsible investment perspectives: A literature mapping and an investor classification. Sustainable Production and Consumption, Vol. 19, pp.117-129.

Çolak, S., Lima, A., and González, M.C. (2016). Understanding congested travel in urban areas. Nature communications, Vol. 7(1), pp.1-8.

Duarte, F. and Ratti, C. (2018). The impact of autonomous vehicles on cities: A review. Journal of Urban Technology, Vol. 25(4), pp.3-18.

Elavarasan, R.M. and Pugazhendhi, R. (2020). Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic. Science of The Total Environment, Vol. 725, p.138858.

Fagnant, D.J. and Kockelman, K. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, Vol. 77, pp.167-181.

Feng, X. (2021). Time and Cost Efficiency of Autonomous Vehicles in the Last-Mile Delivery: A UK Case. International Business Research, Vol. 14(3), pp.1-26.

Figliozzi, M.A., Boudart, J.A. and Feng, W. (2011). Economic and environmental optimization of vehicle fleets Transportation Research Record. Journal of the Transportation Research Board, Vol. 2252(1), pp.1-6.

Fraedricha, E., Heinrichs, D., Bahamonde-Birkeb, F., and Cyganskib, R. (2019). Autonomous driving, the built environment, and policy implications. Transportation Research Part A: Policy and Practice, Vol. 122, pp.162-172.

Gilchrist, S. and Himmelberg, C.P. (1995). Evidence on the role of cash flow for investment. Journal of Monetary Economics, Vol. 36(3), pp.541-572.

Govindan, K., Mina, H., and Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review. Vol. 138, p.101967.

Guerrero, S., Manadat, S., and Leachman, R. (2013). The Trucking Sector Optimization Model: A tool for predicting carrier and shipper responses to policies aiming to reduce GHG emissions. Transportation Research Part E: Logistics and Transportation Review, Vol. 59, pp.85-107.

Hänsel, M.C. and Quaas, M.F. (2018). Intertemporal distribution, sufficiency, and the social cost of carbon. Ecological Economics, Vol. 146, pp.520-535.

Hepburn, C. (2017). Climate change economics: Make carbon pricing a priority. Nature Climate Change, Vol. 7(6), pp.389-390.

Hoffmann, T. and Prause, G. (2018). On the Regulatory Framework for Last-Mile Delivery Robots. Machines, Vol. 6 (33), pp.1-16.

James, Q., and Katsuaki, T. (1984). Sample selection and cost underestimation bias in pioneer projects. Working Papers 512, California Institute of Technology, Division of the Humanities and Social Sciences

Joerss, M., Neuhaus, F., and Schröder, J. (2016). How customer demands are reshaping last-mile delivery. Travel, Transport & Logistics October (2016), Mckinsey & Company

Kissler, S.M., Tedijanto, C., Goldstein, E., Grad, Y.H. and Lipsitch, M. (2020). Projecting the transmission dynamics of SARS-CoV-2 through the post pandemic period. Science, Vol. 368(6493), pp.860-868.

Lee, L.H., Chen, Y., Gillai, B., and Rammohan, S. (2016). Technological Distribution and Innovation in Last-mile Delivery. White Paper (2016), Stanford Graduate School of Business

Li, Y., Du, Q., Lu, X., Wu, J., and Han, X. (2019). Relationship between the development and CO2 emissions of the transport sector in China. Transportation Research Part D: Transport and Environment, Vol. 74, pp.1-14.

Lokhandwala, M. and Cai, H. (2020). Siting charging stations for electric vehicle adoption in shared autonomous fleets. Transportation Research Part D: Transport and Environment, Vol. 80, p.102231.

Lopez, N., Soliman, J., Biona, J., and Fulton, L. (2020). Cost-benefit analysis of alternative vehicles in the Philippines using immediate and distant future scenarios. Transportation Research Part D: Transport and Environment, 82, p.102308.

Masoud, N. and Jayakrishnanb, R. (2017). Autonomous or driverless vehicles: Implementation strategies and operational concerns. Transportation Research Part E: Logistics and Transportation Review, Vol. 108, pp.179-194.

Mohammed, F., Selim, S.Z., Hassan, A. and Syed, M.N. (2017). Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transportation Research Part D: Transport and Environment, Vol. 51, pp.146-172.

Nakamura, H. (2020). Evaluating the value of an entrepreneurial city with a spatial hedonic approach: A case study of London. Socio-Economic Planning Sciences, p.100820.

Palak, G., Eksioglu, S., and Geunes, J. (2014). Analyzing the impacts of carbon regulatory mechanisms on supplier and mode selection decisions: An application to a biofuel supply chain. International Journal of Production Economics, Vol. 154, pp.198-216.

Price, C. (2018). Declining discount rate and the social cost of carbon: forestry consequences. Journal of Forest Economics, Vol. 31, pp.39-45.

Rao, C., Goh, M., Zhao, Y., and Zheng, J. (2015). Location selection of city logistics centers under sustainability. Transportation Research Part D: Transport and Environment, Vol. 36, pp.29-44.

Ricke, K., Drouet, L., Caldeira, K. and Tavoni, M. (2018). The country-level social cost of carbon. Nature Climate Change, Vol. 8(10), pp.895-900.

Savelsbergh, M. and Van Woensel, T. (2016). 50th anniversary invited article—city logistics: Challenges and opportunities. Transportation Science, Vol. 50(2), pp.579-590.

Scherr, Y., Saavedra, B., Hewitt, M., and Mattfeld, D. (2019). Service network design with mixed autonomous fleets. Transportation Research Part E: Logistics and Transportation Review, Vol. 124, pp.40-55.

Self, P. (2002). The Evolution of the Greater London Plan, 1944-1970. Progress in Planning, Vol. 3(57), pp.145-175.

Sherafati, M., Bashiri, M., Tavakkoli-Moghaddamc, R., and Pishvaee, M. (2020). Achieving sustainable development of the supply chain by incorporating various carbon regulatory mechanisms. Transportation Research Part D: Transport and Environment, Vol. 81, p.102253.

Stasko, T.H. and Gao, H.O. (2010). Reducing transit fleet emissions through vehicle retrofits, replacements, and usage changes over multiple time periods. Transportation Research Part D: Transport and Environment, Vol. 15(5), pp.254-262.

Stoiber, T., Schubert, I., Hoerler, R., and Burger, P. (2019). Will consumers prefer shared and pooled-use autonomous vehicles? A stated choice experiment with Swiss households. Transportation Research Part D: Transport and Environment, Vol. 71, pp.265-282.

Tsao, Y. and Thanh, V. (2019). A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment. Transportation Research Part E: Logistics and Transportation Review, Vol. 124, pp.13-39.

Vahidia, A. and Sciarretta, A. (2018). Energy-saving potentials of connected and automated vehicles. Transportation Research Part C: Emerging Technologies, Vol. 95, pp.822-843.

Wang, S. and Zhao, J. (2019). Risk preference and adoption of autonomous vehicles. Transportation Research Part A: Policy and Practice, Vol. 126, pp.215-229.

Wong, E., Tai, A., and Zhou, E. (2018). Optimizing truckload operations in third-party logistics: A carbon footprint perspective in the volatile supply chain. Transportation Research Part D: Transport and Environment, Vol. 63, pp.649-661.

Wu, W., Zhang, F., Liu, W., and Lodewijks, G. (2020). Modeling the traffic in a mixed network with autonomous-driving expressways and non-autonomous local streets. Transportation Research Part E: Logistics and Transportation Review. 134, p.101855.

Xia, H. and Yang, H. (2018). Is Last-Mile Delivery a ‘Killer App’ for Self-Driving Vehicles?. Communications of the ACM, Vol. 61(11), pp.70-75

Zhu, F. and Ukkusuri, S.V. (2015). A linear programming formulation for autonomous intersection control within a dynamic traffic assignment and connected vehicle environment. Transportation Research Part C: Emerging Technologies, Vol. 55, pp.363-378.