Keywords: Crowdshipping, Sustainability, Routing, E-commerce
Context
E-commercehas enjoyed a tremendous growth over the last decades. In 2020 alone, itrepresents 13% of retail business with 92.6 billion euros in France. Thisnumber has grown up to 37% especially after the first outbreak of Covid-19 inthe country (Bon-Maury et al., 2021).
Heavilyinvolving last-mile delivery services, E-commerce has revolutionized the waybusinesses manage logistics due to various features it is characterized e.g., on-lineorders (orders keep appearing after the deployment of vehicles used), shortdelivery times (one-day and same-day deliveries), release dates (distributionprocess starts while warehouse replenishment is performed), overlappingcustomer time windows (Archetti et al., 2016).
Todeal with such challenges, crowshipping has been adopted by Amazon through AmazonFlex since 2013. It employs private people (occasional drivers) to deliverparcels while they are en-route to their destination (Barr and Wohl, 2013;Bensinger, 2015). It is an alternative of dropping-off packages while somepackages are still taken care by professional drivers (e.g., FedEx, DHL).Alongwith its success and challenges, E-commerce has also been criticized due to itsenvironmental, social, and even economic impacts. As it provides superiorproduct variety and availability as well as a fast access to buy and returnproducts (Chopra and Meindl, 2016), E-commerce tends to generate more CO2emission, increase air pollution and packaging waste, road traffics, etc. Interms of social impact, E-commerce causes the so-called delivery uberizationand loss of employment at traditional commerce. Economically, fiscality relatedto E-commerce is complicated and it is often perceived as incomparablecompetitors to physical merchants.To deal with such challenges, it is necessary to develop solutions that allow to improve the operations of E-commerce while reducing its negative impcats on environment and society.
Objectives
Thedoctoral research aims to provide models and methods to improve the operations ofE-commerce while reducing CO2 emissions and responding its social impact. Indetail, we intend to:
- Develop multi-objective optimization models of crowdshippingpickup-and-delivery with time windows that involve decisions on routing ofdelivery vehicles by considering occasional drivers. The models will include(but not limited to) objective functions that minimize pickup and deliverycost, minimize CO2 emission generated, and maximize customer service level.
- Provide solving methods that offer (near) optimalsolutions within faster computational time than those of commercial solvers.
- Incorporate orders that arrive during thedistribution process (online orders) to the problem as well as propose novel approachesto adjust solutions of prior problems.
Required skills
- Final year of master's degree (or equivalent) in OperationsResearch, Industrial Engineering, Computer Science or related fields
- Programming skills (Java, Python, C++, …)
- Knowledge of optimization suites (IBM CPLEX,Gurobi, Fico Xpress, …) and Machine Learning techniques will be a plus
- Very goof level of spoken and written English
Important points
- The successful candidate will be registered as aPh.D. student at Institut Mines-Télécom (IMT) Atlantique, Nantes. The doctoral researchis expected to start in October 2022.
- Contact persons:
- MuhammadKhakim Habibi, Rennes School of Business, muhammad.habibi@rennes-sb.com
- Audrey Derrien, IMTAtlantique, LS2N, audrey.derrien@imt-atlantique.fr
- Alexandre Dolgui, IMTAtlantique, LS2N, alexandre.dolgui@imt-atlantique.fr
- Interested candidates are invited to send thefollowing documents in a single file to the three contact persons:
- Detailed CV,
- Grades of masterstudy (or equivalent),
- Cover letter,and
- Recommendationletters.
- Eachapplication will be treated once received and an interview will be conducted assoon as possible or before June 30, 2022.
References
- Archetti,C., Guerriero, F., & Macrina, G. (2021). The online vehicle routing problemwith occasional drivers. Computers & Operations Research, 127,105144.
- Archetti,C., Savelsbergh, M., & Speranza, M. G. (2016). The vehicle routing problemwith occasional drivers. European Journal of Operational Research, 254(2),472-480.
- Barr,A., & Wohl, J. (2013). Exclusive: Walmart may get customers to deliverpackages to online buyers. REUTERS–Business Week, (March).
- Bensinger,G. (2015). Amazon's next delivery drone: You. Wall street journal, 108.
- Bon-Maury, G., Fosse,J., Deketelaere-Hanna, M.,Lambert, P., Vinçon, P., Constanso, V., Verzat, V., Guérin, V. Pour undéveloppement durable du commerce en ligne (Report No. 013543-01). Conseilgénéral de l'environnement et du développement durable. https://www.strategie.gouv.fr/sites/strategie.gouv.fr/files/atoms/files/fs-rapport-pour_un_developpement_durable_du_commerce_en_ligne-11-03-2021.pdf
- Chopra, S. and Meindl, P. (2016). Supply ChainManagement: Strategy, Planning, and Operations (6 th edition). Pearson.
- Dahle,L., Andersson, H., Christiansen, M., & Speranza, M. G. (2019). The pickupand delivery problem with time windows and occasional drivers. Computers& Operations Research, 109, 122-133.
- Habibi,M. K., Hammami, R, Battaïa, O., Dolgui, A. Production-Routing Problemwith Pickup and Delivery with Disassembly Equipment Consideration. Upcoming
- Liu,X., Chu, F., Dolgui, A., Zheng, F., & Liu, M. (2021).Service-oriented bi-objective robust collection-disassembly problem withequipment selection. International Journal of Production Research, 59(6),1676-1690.
- Macrina,G., Pugliese, L. D. P., & Guerriero, F. (2020). A Variable NeighborhoodSearch for the Vehicle Routing Problem with Occasional Drivers and TimeWindows. In ICORES (pp. 270-277).
- Macrina, G., Pugliese, L.D. P., Guerriero, F., & Laporte, G. (2020). Crowd-shipping with timewindows and transshipment nodes. Computers & Operations Research, 113,104806.
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