Trade-offs in multi-channel delivery network design with perishable and non-perishable goods
DOI:
https://doi.org/10.59490/jscms.2025.8229Keywords:
Last mile delivery, Perishable goods logistics, Vehicle routing problem, Mixed integer linear programming, Best-World MethodAbstract
This study focuses on optimizing last-mile delivery in e-commerce by balancing cost efficiency and customer preferences, particularly for mixed perishable and non-perishable goods distribution. As online grocery shopping grows, ensuring the timely and efficient delivery of perishable products while maintaining quality remains a critical challenge. The problem is modelled as a variant of the multi-objective Vehicle Routing Problem (VRP), where customer utility and operational costs are incorporated as two objectives. Customer utility is computed with parameters estimated using the Best-Worst Method (BWM). The multi-objective model is solved by linearizing the non-linear objectives and using a weighted sum method. The model evaluates home delivery, attended pickup points, and lockers, revealing that cost-driven strategies shift deliveries toward self-pickup, with perishable items primarily assigned to attended pickup points due to temperature control. The findings provide insights for improving delivery network design, enhancing service quality, and optimizing the distribution of both perishable and non-perishable products.
References
Abdulkader, M. M. S., Gajpal, Y., & ElMekkawy, Y. E. (2018). Vehicle routing problem in omni-channel retailing distribution systems. International Journal of Production Economics 196, 43–55. https://doi.org/10.1016/j.ijpe.2017.11.011
Amorim, P., DeHoratius, N., Eng-Larsson, F., & Martins, S. (2024). Customer preferences for delivery service attributes in attended home delivery. Management Science, 70(1). https://doi.org/10.1287/mnsc.2020.01274
Belotti, P., Kirches, C., Leyffer, S., Linderoth, J., Luedtke, J., & Mahajan, A. (2013). Mixed-integer nonlinear optimization. Acta Numerica, 22, 1–131. https://doi.org/10.1017/S0962492913000032
Boysen, N., Fedtke, S. & Schwerdfeger, S. (2021). Last-mile delivery concepts: a survey from an operational research perspective. Or Spectrum, 43, 1–58. https://doi.org/10.1007/s00291-020-00607-8
Cardenas, I. D., Dewulf, W., Vanelslander, T., Smet, C., & Beckers, J. (2017). The e-commerce parcel delivery market and the implications of home B2C deliveries vs pick-up points. International Journal of Transport Economics / Rivista Internazionale Di Economia Dei Trasporti, 44(2), 235–256. http://www.jstor.org/stable/26504078
Chu, H., Zhang, W., Bai, P., & Chen, Y. (2021). Data-driven optimization for last-mile delivery. Complex & Intelligent Systems, 9, 1–14. https://doi.org/10.1007/s40747-021-00293-1
Genius Coca, A. (2020). Implementation of receiver preferences in a parcel locker network for last mile deliveries [Master Thesis]. https://resolver.tudelft.nl/uuid:de08f67e-02aa-489a-a2b2-358eb25aff22
Guerrero-Lorente, J., Gabor, A. F., & Ponce-Cueto, E. (2020). Omnichannel logistics network design with integrated customer preference for deliveries and returns. Computers & Industrial Engineering, 144, 106433. https://doi.org/10.1016/j.cie.2020.106433
Hayel, Y., Quadri, D., Jiménez, T., & Brotcorne, L. (2016). Decentralized optimization of last-mile delivery services with non-cooperative bounded rational customers. Annals of Operations Research ,239(2), 451–469. https://doi.org/10.1007/s10479-014-1647-x
Kurowski, M., Sobolewski, M., & Koszorek, M. (2022). Geometrical parcel locker network design with consideration of users’ preferences as a solution for sustainable last mile delivery. Sustainability, 15(20), 15114. https://doi.org/10.3390/su152015114
Liang, F., Brunelli, M., & Rezaei, J. (2020). Consistency issues in the best worst method: Measurements and thresholds. Omega, 96, 102175. https://doi.org/10.1016/j.omega.2019.102175
Liang, X., Wang, N., Zhang, M., & Jiang, B. (2023). Bi-objective multi-period vehicle routing for perishable goods delivery considering customer satisfaction. Expert Systems With Applications, 220, 119712. https://doi.org/10.1016/j.eswa.2023.119712
Liu, P., Hendalianpour, A., Feylizadeh, M., & Pedrycz, W. (2022). Mathematical modeling of vehicle routing problem in omni-channel retailing. Applied Soft Computing, 131, 109791. https://doi.org/10.1016/j.asoc.2022.109791
Lo, H., Liou, J. J., Wang, H., & Tsai, Y. (2018). An integrated model for solving problems in green supplier selection and order allocation. Journal of Cleaner Production, 190, 339-352. https://doi.org/10.1016/j.jclepro.2018.04.105
Mi, X., Tang, M., Liao, H., Shen, W., & Lev, B. (2019). The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next? Omega, 87, 205-225. https://doi.org/10.1016/j.omega.2019.01.009
Molin, E., Kosicki, M., & van Duin, R. (2022). Consumer preferences for parcel delivery methods: The potential of parcel locker use in the Netherlands. European Journal of Transport and Infrastructure Research, 22(2), 183–200. https://doi.org/10.18757/ejtir.2022.22.2.6427
Guarino Neto, L., & Geraldo Vidal Vieira, J. (2023). An investigation of consumer intention to use pick-up point services for last-mile distribution in a developing country. Journal of Retailing and Consumer Services, 74, 103425. https://doi.org/10.1016/j.jretconser.2023.103425
Punakivi, M., & Tanskanen, K. (2002). Increasing the cost efficiency of e-fulfilment using shared reception boxes. International Journal of Retail & Distribution Management, 30(10), 498–507. https://doi.org/10.1108/09590550210445362
Rabet, R., Sajadi, S.M. & Tootoonchy, M. (2024). A hybrid meta-heuristic and simulation approach towards green project scheduling. Annals of Operations Research, 1–40. https://doi.org/10.1007/s10479-024-06291-z
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130. https://doi.org/10.1016/j.omega.2015.12.001
Saaty, L. S. (2013). Analytic hierarchy process. In Encyclopedia of operations research and management science (pp.52-64). Springer.
Shashi, M. (2023). Leveraging digitalization in last-mile logistics. International Journal of Emerging Technologies and Innovative Research, 10 (2), f204-f208. https://www.jetir.org/papers/JETIR2302524.pdf
Smeets, B. M. M. (2017). Customer choice behavior in the delivery phase of online grocery shopping: A stated choice experiment on customer’ preferences for home delivery and pick-up points [Master Thesis]. https://research.tue.nl/en/studentTheses/customer-choice-behavior-in-the-delivery-phase-of-online-grocery-/
Song, B. D., & Ko, Y. D. (2015). A vehicle routing problem of both refrigerated- and general-type vehicles for perishable food products delivery. Journal of Food Engineering, 169, 61-71. https://doi.org/10.1016/j.jfoodeng.2015.08.027
Song, L., Cherrett, T., McLeod, F., & Guan, W. (2009). Addressing the last mile problem: transport impacts of collection and delivery points. Transportation Research Record, 2097(2097), 9–18. https://doi.org/10.3141/2097-02
Song, L., Mao, B., Wu, Z., & Wang, J. (2019). Investigation of home delivery models and logistics services in China. Transportation Research Record. https://doi.org/10.1177/0361198119844453
Stiglitz, J. E. (1981). Pareto optimality and competition. The Journal of Finance, 36(2), 235-251. https://doi.org/10.1111/j.1540-6261.1981.tb00437.x
Tilk, C., Olkis, K. & Irnich, S. (2021). The last-mile vehicle routing problem with delivery options. OR Spectrum, 43, 877–904. https://doi.org/10.1007/s00291-021-00633-0
Tu, Y., Zhao, Y., Liu, L., & Nie, L. (2022). Travel route planning of core scenic spots based on best-worst method and genetic algorithm: a case study. Management System Engineering, 1. https://doi.org/10.1007/s44176-022-00004-1
Vakulenko, Y., Hellström, D., & Hjort, K. (2018). What's in the parcel locker? Exploring customer value in e-commerce last mile delivery. Journal of Business Research, 88, 421-427. https://doi.org/10.1016/j.jbusres.2017.11.033
Wang, X., Sun, X., Dong, J., Wang, M., & Ruan, J. (2017). Optimizing terminal delivery of perishable products considering customer satisfaction. Mathematical Problems in Engineering, 2017(1), 8696910. https://doi.org/10.1155/2017/8696910
Wang, X. P., Wang, M., Ruan,J. H.,& Li, Y. (2018). Multi-objective optimization for delivering perishable products with mixed time windows. Advances in Production Engineering & Management 13(3), 321–332. https://doi.org/10.14743/apem2018.3.293
Wang, X., Wong, Y. D., Shi, W. & Yuen, K. F. (2023). An investigation on consumers' preferences for parcel deliveries: applying consumer logistics in omni-channel shopping. International Journal of Logistics Management, 35(2), 557-576. https://doi.org/10.1108/IJLM-07-2022-0288
Wang, X., Zhan, L., Ruan, J., & Zhang, J. (2014). How to choose “last mile” delivery modes for e-fulfillment. Mathematical Problems in Engineering, 2014(1), 417129. https://doi.org/10.1155/2014/417129
Zhang, J.-Y., & Li, J. (2011). A heuristic algorithm to vehicle routing problem with the consideration of customers’ service preference. ICSSSM11, 1–6. https://doi.org/10.1109/ICSSSM.2011.5959335
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Supply Chain Management Science
This work is licensed under a Creative Commons Attribution 4.0 International License.
JSCMS is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. The license means that anyone is free to share (to copy, distribute, and transmit the work), to remix (to adapt the work) under the following conditions:
- The original authors must be given credit
- For any reuse or distribution, it must be made clear to others what the license terms of this work are
- Any of these conditions can be waived if the copyright holders give permission
- Nothing in this license impairs or restricts the author's moral rights
