Le 09/06/2022 par unknown :
Context
E-commerce has enjoyed a tremendous growth over the last decades. In 2020 alone, it represents 13% of retail business with 92.6 billion euros in France. This number has grown up to 37% especially after the first outbreak of Covid-19 in the country (Bon-Maury et al., 2021). Heavily involving last-mile delivery services, E-commerce has revolutionized the way businesses manage logistics due to various features it is characterized e.g., on-line orders (orders keep appearing after the deployment
of vehicles used), short delivery times (one-day and same-day deliveries), release dates (distribution process starts while warehouse replenishment is performed), overlapping customer time windows (Archetti et al., 2016). To deal with such challenges, crowshipping has been adopted by Amazon through Amazon Flex since 2013. It employs private people (occasional drivers) to deliver parcels while they are en-route to their destination (Barr and Wohl, 2013; Bensinger, 2015). It is an alternative of dropping-off packages while some packages are still taken care by professional drivers (e.g., FedEx, DHL). Along with its success and challenges, E-commerce has also been criticized due to its environmental, social, and even economic impacts. As it provides superior product variety and availability as well as a fast access to buy and return products (Chopra and Meindl, 2016), E-commerce tends to generate more CO2 emission, increase air pollution and packaging waste, road traffics, etc. In terms of social impact, E-commerce causes the so-called delivery uberization and loss of employment at traditional commerce. Economically, fiscality related to E-commerce is complicated and it is often perceived as incomparable.
To deal with such challenges, it is necessary to develop solutions that allow to improve the operations of E-commerce while reducing its negative impacts on environment and society.
Objectives
The doctoral research aims to provide models and methods to improve the operations of E-commerce while reducing CO2 emissions and responding its social impact. In detail, we intend to:
• Develop multi-objective optimization models of crowdshipping pickup-and-delivery with time windows that involve decisions on routing of delivery vehicles by considering occasional drivers. The models will include (but not limited to) objective functions that minimize pickup and delivery cost, minimize CO2 emission generated, and maximize customer service level.
• Provide solving methods that offer (near) optimal solutions within faster computational time than those of commercial solvers.
• Incorporate orders that arrive during the distribution process (online orders) to the problem as well as propose novel approaches to adjust solutions of prior problems.
Required skills
• Final year of master’s degree (or equivalent) in Operations Research, 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 good level of spoken and written English
Important points
• The successful candidate will be registered as a Ph.D. student at Institut Mines-Télécom (IMT) Atlantique, Nantes. The doctoral research is expected to start in October 2022.
• Contact persons:
o Muhammad Khakim Habibi, Rennes School of Business, muhammad.habibi@rennes-sb.com
o Audrey Derrien, IMT Atlantique, LS2N, audrey.derrien@imt-atlantique.fr
o Alexandre Dolgui, IMT Atlantique, LS2N, alexandre.dolgui@imt-atlantique.fr
