In the context of two-echelon delivery, trucks are used for supplies the intermediate depots, namely satellites, and small vehicles are used to deliver demands to the end customers. In this research, we introduce the crowdsourcing transportation system into the two-echelon delivery system. The crowdsourcing system is modelled as occasional drivers (ODs) that traveling around the distribution system. Occasional drivers are independent drivers that have their origin and destination in the system and also willingly transport one or more parcels to another individual (customer). We define this problem as the two-echelon vehicle routing problem with occasional drivers (2E-VRPOD). ODs could utilize their available capacity and pick-up the item from meeting points with second echelon vehicles or satellites, which are already supplied by first echelon vehicles. This problem modelled the ODs would serve the demand depend on the cost and the reward. This mode offers greater flexibility by providing more options to the decision-maker. It could lower transportation costs and require less capital investment than a traditional sourcing approach. In this research, we formulate this problem as a mixed-integer programming (MIP) and develop a mathematical model that can solve by GUROBI solver for small instances and an adaptive large neighborhood search algorithm to handle large instances. In general, this study shows that utilizing the OD’s in a two-echelon last-mile delivery distribution system takes advantage of cost-saving and environmental causes where the total transportation route in the system reduced.

Archetti, C., Savelsbergh, M., & Speranza, M. G. (2016). The vehicle routing problem with occasional drivers. European Journal of Operational Research, 254(2), 472–480. https://doi.org/10.1016/j.ejor.2016.03.049
Barr, A., Wohl, J., 2013, Exclusive: Wal-Mart may get customers to deliver packages to online buyers, https://www.reuters.com/article/us-retail-walmart-delivery/exclusive-wal-mart-may-get-customers-to-deliver-packages-to-online-buyers-idUSBRE92R03820130328 (online accessed in January 2020)
Boccia, M., Crainic, T. G., Sforza, A., & Sterle, C. (2011). Location-routing models for two-echelon freight distribution system design location-routing models for two-echelon freight. CIRRELT Working Paper, 28.
Breunig, U., Schmid, V., Hartl, R. F., & Vidal, T. (2016). A large neighbourhood based heuristic for two-echelon routing problems. Computers and Operations Research, 76, 208–225. https://doi.org/10.1016/j.cor.2016.06.014
Clement, J., 2019, Global retail e-commerce sales 2014-2023, https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/ (online accessed in January 2020)
Crainic, T. G. (2008). City Logistics. In State-of-the-Art Decision-Making Tools in the Information-Intensive Age (Issue June 2020). https://doi.org/10.1287/educ.1080.0047
Crainic, T. G., Ricciardi, N., & Storchi, G. (2004). Advanced freight transportation systems for congested urban areas. Transportation Research Part C: Emerging Technologies, 12(2), 119–137. https://doi.org/10.1016/j.trc.2004.07.002
Cuda, R., Guastaroba, G., & Speranza, M. G. (2015). A survey on two-echelon routing problems. Computers and Operations Research, 55, 185–199. https://doi.org/10.1016/j.cor.2014.06.008
Dahle, L., Andersson, H., Christiansen, M., & Speranza, M. G. (2019). The pickup and delivery problem with time windows and occasional drivers. Computers and Operations Research, 109, 122–133. https://doi.org/10.1016/j.cor.2019.04.023
Enthoven, D. L. J. U., Jargalsaikhan, B., Roodbergen, K. J., uit het Broek, M. A. J., & Schrotenboer, A. H. (2020). The two-echelon vehicle routing problem with covering options: City logistics with cargo bikes and parcel lockers. Computers and Operations Research, 118, 104919. https://doi.org/10.1016/j.cor.2020.104919
Hemmelmayr, V. C., Cordeau, J. F., & Crainic, T. G. (2012). An adaptive large neighborhood search heuristic for Two-Echelon Vehicle Routing Problems arising in city logistics. Computers and Operations Research, 39(12), 3215–3228. https://doi.org/10.1016/j.cor.2012.04.007
Huang, K., & Ardiansyah, M. N. (2019). A decision model for last-mile delivery planning with crowdsourcing integration. Computers and Industrial Engineering, 135(June), 898–912. https://doi.org/10.1016/j.cie.2019.06.059
Landa, R., 2015. Thinking creativity in digital age, first ed. Nimble, Blue Ash, Ohio.
Laporte, G., & Nobert, Y. (1988). A Vehicle Flow Model for the Optimal Design of a Two-Echelon Distribution System. 158–173. https://doi.org/10.1007/978-3-642-46629-8_11
Macrina, G., Di Puglia Pugliese, L., Guerriero, F., & Lagana, D. (2017), The vehicle routing problem witg Occasional drivers and time Windows, Sforza, A., & Sterle, C., Optimization and Decision Science: Methodologies and Apllications, Sorrento, Italy, September 4 -7, 2017.
Macrina, G., Di Puglia Pugliese, L., Guerriero, F., & Laporte, G. (2020). Crowd-shipping with time windows and transshipment nodes. Computers and Operations Research, 113, 104806. https://doi.org/10.1016/j.cor.2019.104806
Ropke, S., & Pisinger, D. (2006). An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science, 40(4), 455–472. https://doi.org/10.1287/trsc.1050.0135
Sampaio, A. H., Savelsbergh, M. W. P., Veelenturf, L. P., & Van Woensel, T. (2017). SCL Report Series. SCL Report Series, 17.
Yu, V. F., & Lin, S. Y. (2015). A simulated annealing heuristic for the open location-routing problem. Computers and Operations Research, 62, 184–196. https://doi.org/10.1016/j.cor.2014.10.009