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Growing Science » International Journal of Industrial Engineering Computations » Improving a multi-echelon last mile delivery system by effective solution methods based on ant colony optimization

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International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print)
Quarterly Publication
Volume 15 Issue 4 pp. 977-996 , 2024

Improving a multi-echelon last mile delivery system by effective solution methods based on ant colony optimization Pages 977-996 Right click to download the paper Download PDF

Authors: Sena Kır, Serap Ercan Comer

DOI: 10.5267/j.ijiec.2024.6.001

Keywords: Last Mile Delivery, Dynamic Location Routing Problem, Ant Colony Optimization, Clustering Analysis

Abstract: The Covid-19 pandemic has significantly impacted consumer behavior and commerce, prompting a shift towards online goods and services. The surge in demand has led to inefficiencies and disruptions, especially in the last-mile delivery (LMD) process. Because of the LMD, the final stage of the supply chain, plays a crucial role in transporting goods from businesses to consumers, challenges such as the cost inefficiencies of direct home delivery have underscored the need for innovative solutions. In this study, the collection delivery points (CDPs) approach was adopted instead of direct home delivery. It focuses on addressing these challenges by adopting service points as dynamic CDPs and handling the problem as a dynamic location routing problem (DLRP). Two solutions approaches are proposed, to select candidate depots strategically and determine efficient route configurations, to aim to minimize travel distance. One of them is a two-phased hierarchical method that starts with clustering and continues with an Ant Colony Optimization (ACO) based-hybrid algorithm, and the other one is based solely on an ACO-based hybrid algorithm. The performance of these approaches is evaluated on modified benchmark instances from the literature. It has been observed that the ACO based-hybrid algorithm is more successful in terms of total travel distance, and if an evaluation is made in terms of the number of routes, it is recommended that the results of the two-phased hierarchical method should also be considered. Furthermore, a real word case study was conducted with the proposed methods and the results were compared from different perspectives. The results corroborate the findings regarding benchmark instances, thereby providing additional validation to the results obtained.

How to cite this paper
Kır, S & Comer, S. (2024). Improving a multi-echelon last mile delivery system by effective solution methods based on ant colony optimization.International Journal of Industrial Engineering Computations , 15(4), 977-996.

Refrences
Azarmand, Z., & Neishabouri, E. (2009). Location Allocation Problem, in: Zanjirani Farahani, R., Hekmatfar, M. (Eds.), Facility location: concepts, models, algorithms and case studies. Physica-Verlag, pp. 93-109.
Baker, B. M., & Ayechew, M. A. (2003). A genetic algorithm for the vehicle routing problem. Computers & Operations Research, 30(5), 787-800.
Bhatti, A., Akram, H., Basit, H. M., Khan, A. U., Raza, S. M., & Naqvi, M. B. (2020). E-commerce trends during COVID-19 Pandemic. International Journal of Future Generation Communication and Networking, 13(2), 1449-1452.
Boysen, N., Fedtke, S., & Schwerdfeger, S. (2021). Last-mile delivery concepts: a survey from an operational research perspective. OR Spectrum, 43, 1-58.
Bozorgi-Amiri, A., & Khorsi, M. (2016). A dynamic multi-objective location–routing model for relief logistic planning under uncertainty on demand, travel time, and cost parameters. The International Journal of Advanced Manufacturing Technology, 85(5), 1633-1648.
Charisis, A., & Kaisar, E. (2019). Multiobjective Capacitated Location-Allocation Model for Urban Logistics Delivery Facilities. In Transportation Research Board (TRB) Annual Meeting.
Comert, S. E., & Yazgan, H. R. (2023). A new approach based on hybrid ant colony optimization-artificial bee colony algorithm for multi-objective electric vehicle routing problems. Engineering Applications of Artificial Intelligence, 123, 106375.
Deutsch, Y., & Golany, B. (2018). A parcel locker network as a solution to the logistics last mile problem. International Journal of Production Research, 56(1-2), 251-261.
Dorigo, M., Maniezzo, V., & Colorni, A. (1991). Ant System: An Autocatalytic Optimizing Process. Technical Report TR91-016, Politecnico di Milano.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29-41.
Ferdi, I., & Layeb, A. (2018). A GRASP algorithm based new heuristic for the capacitated location routing problem. Journal of Experimental & Theoretical Artificial Intelligence, 30(3), 369-387.
Gao, S., Wang, Y., Cheng, J., Inazumi, Y., & Tang, Z. (2016). Ant colony optimization with clustering for solving the dynamic location routing problem. Applied Mathematics and Computation, 285, 149-173.
Gdowska, K., Viana, A., & Pedroso, J. P. (2018). Stochastic last-mile delivery with crowdshipping. Transportation research procedia. 30, 90-100.
Gendrau, M., & Tarantilis, C. D. (2010). Solving Large-Scale Vehicle Routing Problems with Time Windows: The State of the Art. CIRRELT.
Gevaers, R., Van de Voorde, E., & Vanelslander, T. (2011). Characteristics and typology of last-mile logistics from an innovation perspective in an urban context, in: City distribution and urban freight transport. Edward Elgar Publishing, pp. 56-71.
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.
Guthrie, C., Fosso-Wamba, S., & Arnaud, J. B. (2021). Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. Journal of Retailing and Consumer Services, 61, 102570.
Hakimi, S.L. (1964). Optimum Location of Switching Centers and the Absolute Centers and Medians of a Graph. Operations Research, 12(3), 450–459.
Hakimi, S.L. (1965). Optimum distribution of switching centers in a communication network and some related graph theoretic problems. Operations Research, 13(3), 462–475.
Han, J., & Kamber, M. (2001). Data Mining and Concepts Techniques. San Francisco: Morgan Kaufmann Publishers Inc.
Hassanzadeh, A., Mohseninezhad, L., Tirdad, A., Dadgostari, F., & Zolfagharinia, H. (2009). Location-routing problem, in: Facility location: concepts, models, algorithms and case studies. Physica-Verlag, pp. 395-417.
Janjevic, M., Merchán, D., & Winkenbach, M. (2021). Designing multi-tier, multi-service-level, and multi-modal last-mile distribution networks for omni-channel operations. European Journal of Operational Research, 294(3), 1059-1077.
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.
Kedia, A., Kusumastuti, D., & Nicholson, A. (2020). Locating collection and delivery points for goods’ last-mile travel: A case study in New Zealand. Transportation Research Procedia, 46, 85-92.
Kiba-Janiak, M., Marcinkowski, J., Jagoda, A., & Skowrońska, A. (2021). Sustainable last mile delivery on e-commerce market in cities from the perspective of various stakeholders. Literature review. Sustainable Cities and Society, 71, 102984.
Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining number of Cluster in K-Means Clustering. International Journal, 1(6), 90-95.
Laporte, G., & Dejax, P. J. (1989). Dynamic location-routeing problems. Journal of the Operational Research Society, 40(5), 471-482 (1989).
Li, S. R., & Keskin, B. B. (2014). Bi-criteria dynamic location-routing problem for patrol coverage. Journal of the Operational Research Society, 65(11), 1711-1725.
Lin, Y. H., Wang, Y., He, D., & Lee, L. H. (2020). Last-mile delivery: Optimal locker location under multinomial logit choice model. Transportation Research Part E: Logistics and Transportation Review, 142, 102059.
Liu, D., Deng, Z., Zhang, W., Wang, Y., & Kaisar, E. I. (2021). Design of sustainable urban electronic grocery distribution network. Alexandria Engineering Journal, 60(1), 145-157.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1(14), 281–297.
Mangiaracina, R., Perego, A., Seghezzi, A., & Tumino, A. (2019). Innovative solutions to increase last-mile delivery efficiency in B2C e-commerce: a literature review. International Journal of Physical Distribution & Logistics Management, 49(9), 901-920.
Memari, P., Tavakkoli-Moghaddam, R., Navazi, F., & Jolai, F. (2020). Air and ground ambulance location-allocation-routing problem for designing a temporary emergency management system after a disaster. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of engineering in medicine, 234(8), 812-828.
Min, H., Jayaraman, V., & Srivastava, R. (1998). Combined location-routing problems: A synthesis and future research directions. European Journal of Operational Research, 108(1), 1-15.
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.
Nadizadeh, A., & Nasab, H. H. (2014). Solving the dynamic capacitated location-routing problem with fuzzy demands by hybrid heuristic algorithm. European Journal of Operational Research, 238(2), 458-470.
Nagy, G., & Salhi, S. (2007). Location-routing: Issues, models and methods. European Journal of Operational Research, 177(2), 649–672.
Orenstein, I., Raviv, T., & Sadan, E. (2019). Flexible parcel delivery to automated parcel lockers: models, solution methods and analysis. EURO Journal on Transportation and Logistics, 8(5), 683-711.
Paul, S. K., Chowdhury, P., Moktadir, M. A., & Lau, K. H. (2021). Supply chain recovery challenges in the wake of COVID-19 pandemic. Journal of business research, 136, 316-329.
Prodhon, C., & Prins, C. (2014). A survey of recent research on location-routing problems. European Journal of Operational Research, 238(1), 1-17.
ReVelle, C., & Swain, R. (1970). Central Facilities Location. Geographical Analysis, 2(1), 30–42.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65.
Sarkis, J. (2020). Supply chain sustainability: learning from the COVID-19 pandemic. International Journal of Operations & Production Management, 41(1), 63-73.
Schwerdfeger, S., & Boysen, N. (2020). Optimizing the changing locations of mobile parcel lockers in last-mile distribution. European Journal of Operational Research, 285(3), 1077-1094.
Taguchi, G., & Wu, Y. (1979). Introduction to Off-Line Quality Control. Central Japan Quality Control Association, Magaya, Japan.
Wang, Y., Xu, R., Schwartz, M., Ghosh, D., & Chen, X. (2020). COVID-19 and retail grocery management: insights from a broad-based consumer survey. IEEE Engineering Management Review, 48(3), 202-211.
Weltevreden, J. W. (2008). B2c e‐commerce logistics: the rise of collection‐and‐delivery points in The Netherlands. International journal of retail & distribution management. 36(8), 638-660.
Xu, R., & Wunsch, D. (2005). Survey of Clustering Algorithms. IEEE Transactions on Neural Networks, 16(3), 645-678.
Zhou, L., Baldacci, R., Vigo, D., & Wang, X., (2018). A Multi-Depot Two-Echelon Vehicle Routing Problem with Delivery Options Arising in the Last Mile Distribution. European Journal of Operational Research, 265 (2), 765–778.
Zhou, L., Lin, Y., Wang, X., & Zhou, F. (2019). Model and algorithm for bilevel multisized terminal location‐routing problem for the last mile delivery. International Transactions in Operational Research, 26(1), 131-156.
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Journal: International Journal of Industrial Engineering Computations | Year: 2024 | Volume: 15 | Issue: 4 | Views: 761 | Reviews: 0

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