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Balancing Risks and Monetary Savings When the Crowd is Involved in Pickups and Deliveries

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Innovative Intelligent Industrial Production and Logistics (IN4PL 2023)

Abstract

The increasing number of requests in the last-mile delivery has led to the introduction of advanced technological solutions to enhance couriers’ services. In addition, innovative strategies like crowd-shipping have been introduced in order to create synergies within the territory and involve ordinary people in the transportation activity with the aim of reducing operational costs and pollution as well as of increasing the service level. We refer to a company that manages a crowd-shipping platform and provides transportation services within time windows through its own fleet of vehicles and occasional drivers. The service requests correspond to pairs of pickups and deliveries. The objective of the company is to maximize the profit by balancing risks and benefits, from an economic perspective, associated with the involvement of ordinary people in fulfilling service requests. We extend the pickup and delivery problem with occasional drivers and regular vehicles, introducing risk and compensation considerations. The computational analysis conducted through an optimization model shows how the use of occasional drivers reduces overall costs. Moreover, a series of managerial insights is provided thanks to a sensitivity analysis on the risk and compensation parameters associated with crowd-shipping service.

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References

  1. Laganá, D., Longo, F., Vocaturo, F.: Vendor-managed inventory practice in the supermarket supply chain. Int. J. Food Eng. 12, 827–834 (2016). https://doi.org/10.1515/ijfe-2016-0067

    Article  Google Scholar 

  2. Coelho, L., De Maio, A., Laganá, D.: A variable MIP neighborhood descent for the multi-attribute inventory routing problem. Transp. Res. Part E: Logist. Transp. Rev. 144, 102137 (2020). https://doi.org/10.1016/j.tre.2020.102137

    Article  Google Scholar 

  3. Crainic, T., Ricciardi, N., Storchi, G.: Models for evaluating and planning city logistics systems. Transp. Sci. 43, 407–548 (2009). https://doi.org/10.1287/trsc.1090.0279

    Article  Google Scholar 

  4. Montoya-Torres, J., Munoz-Villamizar, A., Vega-Mejía, C.: On the impact of collaborative strategies for goods delivery in city logistics. Prod. Plann. Control 27, 443–455 (2016). https://doi.org/10.1080/09537287.2016.1147092

    Article  Google Scholar 

  5. Schwerdfeger, S., Boysen, N.: Optimizing the changing locations of mobile parcel lockers in last-mile distribution. Eur. J. Oper. Res. 285(3), 1077–1094 (2020). https://doi.org/10.1016/j.ejor.2020.02.033

    Article  MathSciNet  Google Scholar 

  6. de Mello Bandeira R.A., Goes G.V., Schmitz Goncalves D.N., de Almeida D’Agosto M., Machado de Oliveira C.: Electric vehicles in the last mile of urban freight transportation: a sustainability assessment of postal deliveries in Rio de Janeiro-Brazil. Transp. Res. Part D: Transp. Environ. 67, 491–502 (2019). https://doi.org/10.1016/j.trd.2018.12.017

  7. Moshref-Javadi, M., Hemmati, A., Winkenbach, M.: A truck and drones model for last-mile delivery: a mathematical model and heuristic approach. Appl. Math. Model. 80, 290–318 (2020). https://doi.org/10.1016/j.apm.2019.11.020

    Article  MathSciNet  Google Scholar 

  8. Laporte, G., Meunier, F., Wolfer, C.R.: Shared mobility systems. 4OR 13(4), 341–360 (2015). https://doi.org/10.1007/s10288-015-0301-z

    Article  MathSciNet  Google Scholar 

  9. Wu, C., Kim, I.: Analyzing the structural properties of bike-sharing networks: evidence from the United States, Canada, and China. Transp. Res. Part A: Policy Pract. 140, 52–71 (2020). https://doi.org/10.1016/j.tra.2020.07.018

    Article  Google Scholar 

  10. Neumann, T.: The impact of carsharing on transport in the city. Case study of tri-city in Poland. Sustainability 13(2), 688 (2021). https://doi.org/10.3390/su13020688

    Article  Google Scholar 

  11. Beraldi, P., De Maio, A., Laganá, D., Violi, A.: A pick-up and delivery problem for logistics e-marketplace services. Optim. Lett. 15, 1565–1577 (2021). https://doi.org/10.1007/s11590-019-01472-3

    Article  MathSciNet  Google Scholar 

  12. Furuhata, M., Dessouky, M., Ordonez, F., Brunet, M., Wang, X., Koenig, S.: Ridesharing: the state-of-the-art and future directions. Transp. Res. Part B: Methodol. 57, 28–46 (2013). https://doi.org/10.1016/j.trb.2013.08.012

    Article  Google Scholar 

  13. Ulmer, M.: Dynamic pricing and routing for same-day delivery. Transp. Sci. 54(4), 1016–1033 (2020). https://doi.org/10.1287/trsc.2019.0958

    Article  Google Scholar 

  14. Laganá, D., Laporte, G., Vocaturo, F.: A dynamic multi-period general routing problem arising in postal service and parcel delivery systems. Comput. Oper. Res. 129, 105195 (2021). https://doi.org/10.1016/j.cor.2020.105195

    Article  MathSciNet  Google Scholar 

  15. Escudero-Santana, A., Muñuzuri, J., Lorenzo-Espejo, A., Muñoz-Díaz, M.-L.: Improving e-commerce distribution through last-mile logistics with multiple possibilities of deliveries based on time and location. J. Theor. Appl. Electron. Commer. Res. 17, 507–521 (2022). https://doi.org/10.3390/jtaer17020027

    Article  Google Scholar 

  16. Punel, A., Stathopoulos, A.: Modeling the acceptability of crowdsourced goods deliveries: role of context and experience effects. Transp. Res. Part E: Logist. Transp. Rev. 105, 18–38 (2017). https://doi.org/10.1016/j.tre.2017.06.007

    Article  Google Scholar 

  17. Ulmer, M., Savelsbergh, M.: Workforce scheduling in the era of crowdsourced delivery. Transp. Sci. 54(4), 1113–1133 (2020). https://doi.org/10.1287/trsc.2020.0977

    Article  Google Scholar 

  18. Le, T., Stathopoulos, A., Van Woensel, T., Ukkusuri, S.: Supply, demand, operations, and management of crowd-shipping services: a review and empirical evidence. Transp. Res. Part C: Emerg. Technol. 103, 83–103 (2019). https://doi.org/10.1016/j.trc.2019.03.023

    Article  Google Scholar 

  19. Arslan, A.M., Agatz, N., Kroon, L., Zuidwijk, R.: Crowdsourced delivery: a dynamic pickup and delivery problem with ad-hoc drivers. Transp. Sci. 53(1), 222–235 (2019). https://doi.org/10.1287/trsc.2017.0803

    Article  Google Scholar 

  20. Savelsbergh, M., Van Woensel, T.: 50th anniversary invited article - city logistics: challenges and opportunities. Transp. Sci. 50, 579–590 (2016). https://doi.org/10.1287/trsc.2016.0675

    Article  Google Scholar 

  21. Bensinger G.: Amazon’s next delivery drone: you. Wall Street J. (2015)

    Google Scholar 

  22. Paloheimo, H., Lettenmeier, M., Waris, H.: Transport reduction by crowdsourced deliveries - a library case in Finland. J. Clean. Prod. 132, 240–251 (2016). https://doi.org/10.1016/j.jclepro.2015.04.103

    Article  Google Scholar 

  23. Archetti, C., Savelsbergh, M., Speranza, M.: The vehicle routing problem with occasional drivers. Eur. J. Oper. Res. 254, 472–480 (2016). https://doi.org/10.1016/j.ejor.2016.03.049

    Article  MathSciNet  Google Scholar 

  24. Dahle, L., Andersson, H., Christiansen, M., Speranza, M.: The pickup and delivery problem with time windows and occasional drivers. Comput. Oper. Res. 109, 122–133 (2019). https://doi.org/10.1016/j.cor.2019.04.023

    Article  MathSciNet  Google Scholar 

  25. Macrina, G., Di Puglia Pugliese, L., Guerriero, F., Laporte, G.: Crowd-shipping with time windows and transshipment nodes. Comput. Oper. Res. 113, 104806 (2020). https://doi.org/10.1016/j.cor.2019.104806

    Article  MathSciNet  Google Scholar 

  26. Dahle, L., Andersson, H., Christiansen, M.: The vehicle routing problem with dynamic occasional drivers. In: Bektaş, T., Coniglio, S., Martinez-Sykora, A., Voß, S. (eds.) ICCL 2017. LNCS, vol. 10572, pp. 49–63. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68496-3_4

    Chapter  Google Scholar 

  27. Archetti, C., Guerriero, G., Macrina, G.: The online vehicle routing problem with occasional driver. Comput. Oper. Res. 127, 105144 (2021). https://doi.org/10.1016/j.cor.2020.105144

    Article  MathSciNet  Google Scholar 

  28. Trentini, A., Mahléné, N.: Toward a shared urban transport system ensuring passengers and goods cohabitation. TeMA - J. Land Use Mob. Environ. 44, 3–37 (2010). https://doi.org/10.6092/1970-9870/165

    Article  Google Scholar 

  29. Serafini, S., Nigro, M., Gatta, V., Marcucci, E.: Sustainable crowdshipping using public transport: a case study evaluation in Rome. Transp. Res. Procedia 30, 101–110 (2018). https://doi.org/10.1016/j.trpro.2018.09.012

    Article  Google Scholar 

  30. Chen W., Mes M., Schutten M.: Multi-hop driver-parcel matching problem with time windows. Flexible Serv. Manuf. J. 1–37 (2017). https://doi.org/10.1007/s10696-016-9273-3

  31. Behrend, M., Meisel, F.: The integration of item-sharing and crowdshipping: can collaborative consumption be pushed by delivering through the crowd? Transp. Res. Part B: Methodol. 111, 227–243 (2018). https://doi.org/10.1016/j.trb.2018.02.017

    Article  Google Scholar 

  32. dos Santos, A.G., Viana, A., Pedroso, J.P.: 2-echelon lastmile delivery with lockers and occasional couriers. Transp. Res. Part E: Logist. Transp. Rev. 162, 102714 (2022). https://doi.org/10.1016/j.tre.2022.102714

    Article  Google Scholar 

  33. Ghaderi, H., Zhang, L., Tsai, P.W., Woo, J.: Crowdsourced last-mile delivery with parcel lockers. Int. J. Prod. Econ. 251, 108549 (2022). https://doi.org/10.1016/j.ijpe.2022.108549

    Article  Google Scholar 

  34. Lyons, T., McDonald, N.C.: Last-mile strategies for urban freight delivery: a systematic review. Transp. Res. Rec. 2677(1), 1141–1156 (2023). https://doi.org/10.1177/03611981221103596

    Article  Google Scholar 

  35. Cebeci, M.S., Tapia, R.J., Kroesen, M., de Bok, M., Tavasszy, L.: The effect of trust on the choice for crowdshipping services. Transp. Res. Part A: Policy Pract. 170, 103622 (2023). https://doi.org/10.1016/j.tra.2023.103622

    Article  Google Scholar 

  36. Pourrahmani, E., Jaller, M.: Crowdshipping in last mile deliveries: operational challenges and research opportunities. Socioecon. Plann. Sci. 78, 101063 (2021). https://doi.org/10.1016/j.seps.2021.101063

    Article  Google Scholar 

  37. Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp. Sci. 40(4), 455–472 (2006). https://doi.org/10.1287/trsc.1050.0135

    Article  Google Scholar 

  38. Beraldi, P., Ghiani, G., Musmanno, R., Vocaturo, F.: Efficient neighborhood search for the probabilistic multi-vehicle pickup and delivery problem. Asia-Pac. J. Oper. Res. 27(3), 301–314 (2010). https://doi.org/10.1142/S0217595910002715

    Article  MathSciNet  Google Scholar 

  39. Buldeo, R.H., Verlinde, S., Merckx, J., Macharis, C.: Crowd logistics: an opportunity for more sustainable urban freight transport? Eur. Transp. Res. Rev. 9, 39 (2017). https://doi.org/10.1007/s12544-017-0256-6

    Article  Google Scholar 

  40. Yildiz, B., Salvelsbergh, M.: Service and capacity planning in crowd-sourced delivery. Transp. Res. Part C: Emerg. Technol. 100, 177–199 (2019). https://doi.org/10.1016/j.trc.2019.01.021

    Article  Google Scholar 

  41. De Maio, A., Laganá, D., Musmanno, R., Vocaturo, F.: Arc routing under uncertainty: introduction and literature review. Comput. Oper. Res. 135, 105442 (2021). https://doi.org/10.1016/j.cor.2021.105442

    Article  MathSciNet  Google Scholar 

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Acknowledgement

The work of Annarita De Maio is partially supported by MUR (Italian Minister of University and Research) under the grant H25F21001230004. This support is gratefully acknowledged.

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De Maio, A., Musmanno, R., Vocaturo, F. (2023). Balancing Risks and Monetary Savings When the Crowd is Involved in Pickups and Deliveries. In: Terzi, S., Madani, K., Gusikhin, O., Panetto, H. (eds) Innovative Intelligent Industrial Production and Logistics. IN4PL 2023. Communications in Computer and Information Science, vol 1886. Springer, Cham. https://doi.org/10.1007/978-3-031-49339-3_7

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  • DOI: https://doi.org/10.1007/978-3-031-49339-3_7

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