ENHANCING THE EFFICIENCY OF PARCEL LOCKER NETWORKS IN POSTAL AND COURIER SERVICES
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
This article aims to enhance the efficiency of automated parcel pickup networks operated by postal and courier services by developing recommendations for optimizing parcel locker compartment sizes based on statistical analysis. Methodology. Statistical analysis methods were applied to determine optimal locker sizes. System analysis was used to structure and evaluate the postal service’s shipment database. Additionally, graphical and tabular methods were employed to visualize the results and facilitate their interpretation. Results. Using Nova Poshta as a case study, the shipment structure was analyzed, identifying two categories of parcels suitable for delivery via parcel lockers. The consistency between the nationwide shipment structure and a control sample from Kyiv was assessed. Based on this analysis, an algorithm was developed to determine the share of parcels for which the existing locker sizes are optimal. Practical implications. The statistical analysis of the occurrence rate of parcels with varying dimensions facilitated the formulation of hypotheses regarding the most efficient locker compartment sizes. The implementation of the proposed algorithm allowed for the identification of the share of parcels for which the compartment sizes of both indoor and outdoor parcel lockers are optimal. Value / originality. The findings on optimal parcel locker compartment sizes provide a foundation for the efficient utilization of locker volume. This leads to improved throughput capacity of the parcel locker network, yielding a positive economic effect by reducing investment costs for network development.
How to Cite
##plugins.themes.bootstrap3.article.details##
postal and courier services, efficiency, automation of parcel pickup, operational business processes, three-dimensional bin packing problem, statistical analysis
Buko, J., Bulsa, M., & Makowski, A. (2022) Spatial Premises and Key Conditions for the Use of UAVs for Delivery of Items on the Example of the Polish Courier and Postal Services Market. Energies. 15(4). DOI: https://doi.org/10.3390/en15041403
Aurambout, J.-Ph., Gkoumas, K., & Ciuffo, B. (2019) Last mile delivery by drones: an estimation of viable market potential and access to citizens across European cities. European Transport Research Review. DOI: https://doi.org/10.1186/s12544-019-0368-2
Martello, S., Pisinger, D., & Vigo, D. (2000) The Three-Dimensional Bin Packing Problem. Operations Research, 48(2), 256–267. DOI: https://doi.org/10.1287/opre.48.2.256.12386
Stoian, Y. E., Chugay, A. M., Pankratov, A. V., & Romanova, T. E. (2018) Two Approaches to Modeling and Solving the Packing Problem for Convex Polytopes. Cybernetics and Systems Analysis, 54(4), 585–593. DOI: https://doi.org/10.1007/s10559-018-0059-3
Elhedhli, S., Gzara, F., & Yildiz, B. (2019) Three-dimensional bin packing and mixed-case palletization. Informs J. Optim., 1, 323–352. DOI: https://doi.org/10.1287/ijoo.2019.0013
Tian, T., Zhu, W., Lim, A., & Wei, L. (2016) The multiple container loading problem with preference. European Journal of Operational Research, Volume 248, Issue 1, 2016, P. 84–94. DOI: https://doi.org/10.1016/j.ejor.2015.07.002
Romero, V., Osaba, S., Villar-Rodriguez, E., Oregi, I., & Ban Y. (2023) Hybrid approach for solving real-world bin packing problem instances using quantum annealers. Sci Rep 13, 11777. DOI: https://doi.org/10.1038/s41598-023-39013-9
De Andoin, M. G., Oregi, I., Villar-Rodriguez, E., Osaba, E. & Sanz, M. (2022) Comparative benchmark of a quantum algorithm for the bin packing problem. In 2022 IEEE Symposium Series on Computational Intelligence (SSCI), 930–937. DOI: https://doi.org/10.48550/arXiv.2207.07460
Oliveira, Ó., Gamboa, D., & Silva, E. (2023) An introduction to the two-dimensional rectangular cutting and packing problem. International transactions in operational research. 30. 3238–3266. DOI: https://doi.org/10.1111/itor.13236
Chernyak, O. I., & Chornous, G. O. (2020) Data Mining in Business Using IBM SPSS Modeler. Kyiv University.
This work is licensed under a Creative Commons Attribution 4.0 International License.