A mixed integer linear programming approach for last-mile e-commerce optimization through micro-fulfillment centers
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Abstract
The rapid growth of e-commerce increases the complexity of last-mile delivery due to high distribution costs, urban congestion, and increasingly tight delivery time demands. This study proposes a Mixed Integer Linear Programming (MILP) approach to optimize e-commerce last-mile distribution through the determination of Micro-Fulfillment Centers (MFCs). The model simultaneously determines (i) the locations of candidate MFCs to be opened and (ii) the allocation of demand zones to selected facilities, with the objective of minimizing the total network cost consisting of fixed facility costs and variable last-mile service costs. Service quality is enforced through a hard service level agreement (SLA) mechanism by limiting allocation to only pairs of facility zones that meet a certain travel time threshold, while operational feasibility is guaranteed through capacity constraints at each MFC. The model outputs are implementable in the form of selected MFC locations, zone allocation maps, and performance indicators for evaluation, including total cost decomposition, weighted travel time metrics, and facility capacity utilization to identify potential bottlenecks. Numerical illustrations show that the MILP formulation yields feasible location–allocation decisions with respect to SLA and capacity, while avoiding the “closest/fastest” heuristic that can potentially lead to facility overload. This framework supports decision-makers in designing efficient, responsive, and scalable last-mile networks, and can be extended to incorporate demand uncertainty, SLA penalties (soft-SLAs), multi-echelon structures, and sustainability objectives.
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Allen, J., Piecyk, M., Piotrowska, M., McLeod, F., Cherrett, T., Ghali, K., Nguyen, T., Bektas, T., Bates, O., & Friday, A. (2018). Understanding the impact of e-commerce on last-mile light goods vehicle activity in urban areas: The case of London. Transportation Research Part D: Transport and Environment, 61, 325–338. https://doi.org/10.1016/j.trd.2017.12.020
An, M. J., Jung, S. H., & Lee, D. H. (2025). Demand forecasting in micro-fulfillment centers using association rule-based machine learning. International Journal of Production Economics, 290, 109789. https://doi.org/10.1016/j.ijpe.2025.109789
Bergmann, F. M., Wagner, S. M., & Winkenbach, M. (2020). Integrating first-mile pickup and last-mile delivery on shared vehicle routes for efficient urban e-commerce distribution. Transportation Research Part B: Methodological, 131, 26–62. https://doi.org/10.1016/j.trb.2019.09.013
Bosona, T. (2020). Urban freight last mile logistics—Challenges and opportunities to improve sustainability: A literature review. Sustainability, 12(21), 8769.
Boysen, N., Fedtke, S., & Schwerdfeger, S. (2021). Last-mile delivery concepts: a survey from an operational research perspective. Or Spectrum, 43(1), 1–58. https://doi.org/10.1007/s00291-020-00607-8
Devari, A., Nikolaev, A. G., & He, Q. (2017). Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers. Transportation Research Part E: Logistics and Transportation Review, 105, 105–122. https://doi.org/10.1016/j.tre.2017.06.011
Dixit, A., Shah, B., & Sonwaney, V. (2020). Picking improvement of an FMCG warehouse: a lean perspective. International Journal of Logistics Economics and Globalisation, 8(3), 243–271.
Janjevic, M., Winkenbach, M., & Merchán, D. (2019). Integrating collection-and-delivery points in the strategic design of urban last-mile e-commerce distribution networks. Transportation Research Part E: Logistics and Transportation Review, 131, 37–67. https://doi.org/10.1016/j.tre.2019.09.001
Kahalimoghadam, M., Thompson, R. G., & Rajabifard, A. (2024). Determining the number and location of micro-consolidation centres as a solution to growing e-commerce demand. Journal of Transport Geography, 117, 103875. https://doi.org/10.1016/j.jtrangeo.2024.103875
Karaoulanis, A. (2024). The role of micro fulfilment centers in alleviating, in a sustainable way, the urban last mile logistics problem: A systematic literature review. Sustainability, 16(20), 8774. https://doi.org/10.3390/su16208774
Lim, S. F. W. T., Jin, X., & Srai, J. S. (2018). A literature review, design framework, and research agenda on last-mile logistics models. International Journal of Physical Distribution & Logistics, 48(3), 308–332. https://doi.org/10.1108/IJPDLM-02-2017-0081
Melo, M. T., Nickel, S., & Saldanha-Da-Gama, F. (2009). Facility location and supply chain management–A review. European Journal of Operational Research, 196(2), 401–412. https://doi.org/10.1016/j.ejor.2008.05.007
Millstein, M. A., Bilir, C., & Campbell, J. F. (2022). The effect of optimizing warehouse locations on omnichannel designs. European Journal of Operational Research, 301(2), 576–590. https://doi.org/10.1016/j.ejor.2021.10.061
Olsson, J., Hellström, D., & Pålsson, H. (2019). Framework of Last Mile Logistics Research : A Systematic Review of the Literature. Sustainability, 11, 1–25. https://doi.org/10.3390/su11247131
Özarık, S. S., Veelenturf, L. P., Van Woensel, T., & Laporte, G. (2021). Optimizing e-commerce last-mile vehicle routing and scheduling under uncertain customer presence. Transportation Research Part E: Logistics and Transportation Review, 148, 102263. https://doi.org/10.1016/j.tre.2021.102263
Perboli, G., Tadei, R., & Vigo, D. (2011). The two-echelon capacitated vehicle routing problem: Models and math-based heuristics. Transportation Science, 45(3), 364–380. https://doi.org/10.1287/trsc.1110.0368
Raj, G., Roy, D., de Koster, R., & Bansal, V. (2024). Stochastic modeling of integrated order fulfillment processes with delivery time promise: Order picking, batching, and last-mile delivery. European Journal of Operational Research, 316(3), 1114–1128. https://doi.org/10.1016/j.ejor.2024.03.003
Riandari, F., Dalimunthe, Y. A., Ginting, R., Afifa, R. M., & Afrisawati, A. (2025). Hungarian maximization model approach for optimizing human resource assignment in multi-site projects. Jurnal Teknik Informatika CIT Medicom, 17(2), 50–58.
Riandari, F., & Sihotang, H. T. (2025). Distribution cost optimization: Comparison of NWC, MODI, and Stepping Stone methods in transportation problems. International Journal of Basic and Applied Science, 14(2), 85–96. https://doi.org/10.35335/ijobas.v14i2.688
Sluijk, N., Florio, A. M., Kinable, J., Dellaert, N., & Van Woensel, T. (2023). Two-echelon vehicle routing problems: A literature review. European Journal of Operational Research, 304(3), 865–886. https://doi.org/10.1016/j.ejor.2022.02.022
Stokkink, P., & Geroliminis, N. (2025). On the optimal micro-hub locations in a multi-modal last-mile delivery system. Transportation Research Part E: Logistics and Transportation Review, 203, 104344. https://doi.org/10.1016/j.tre.2025.104344
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
Vazquez-Noguerol, M., Comesaña-Benavides, J. A., Riveiro-Sanroman, S., & Prado-Prado, J. C. (2022). A mixed integer linear programming model to support e-fulfillment strategies in warehouse-based supermarket chains. Central European Journal of Operations Research, 30(4), 1369–1402. https://doi.org/10.1007/s10100-021-00778-x
Wang, Y., Zhang, D., Liu, Q., Shen, F., & Lee, L. H. (2016). Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions. Transportation Research Part E: Logistics and Transportation Review, 93, 279–293. https://doi.org/10.1016/j.tre.2016.06.002
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