IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v100y2021ics030504831931535x.html
   My bibliography  Save this article

Data-driven decision and analytics of collection and delivery point location problems for online retailers

Author

Listed:
  • Xu, Xianhao
  • Shen, Yaohan
  • (Amanda) Chen, Wanying
  • Gong, Yeming
  • Wang, Hongwei

Abstract

The location of collection and delivery points (CDPs), impacted by online customers’ demand data, plays an important role for online retailers. While previous delivery points optimization researches do not use customer behavior data, we propose new models, integrating with customer behavior data analysis, to optimize collection and delivery points for online retailers. We explore a real customer behavior data and use totally 257,685 users’ records (212,062 records for training set and 45,623 records test set). We first estimate the customer purchase probability by five data mining models. Based on the estimation results, we establish two facility location models to respectively optimize the attended and unattended CDPs locations with the objective of cost minimization. Our numerical experiments make a quantitative analysis of customer service level and location cost. Our results can further help online retailers to decide the suitable CDPs with trading off the consumer service level and the total logistics cost. We make interesting contributions: (i) we analyze real customer behavior data and find that gradient boosting trees algorithm outperform other four algorithms when estimating customers’ purchase probabilities; (ii) We propose a new data-driven method integrating data mining models and facility location models to determine CDP locations for online retailers.

Suggested Citation

  • Xu, Xianhao & Shen, Yaohan & (Amanda) Chen, Wanying & Gong, Yeming & Wang, Hongwei, 2021. "Data-driven decision and analytics of collection and delivery point location problems for online retailers," Omega, Elsevier, vol. 100(C).
    • Handle: RePEc:eee:jomega:v:100:y:2021:i:c:s030504831931535x
    DOI: 10.1016/j.omega.2020.102280
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030504831931535X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2020.102280?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kınay, Ömer Burak & Saldanha-da-Gama, Francisco & Kara, Bahar Y., 2019. "On multi-criteria chance-constrained capacitated single-source discrete facility location problems," Omega, Elsevier, vol. 83(C), pages 107-122.
    2. Bélanger, V. & Ruiz, A. & Soriano, P., 2019. "Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 1-23.
    3. Liu, Chengxi & Wang, Qian & Susilo, Yusak O., 2019. "Assessing the impacts of collection-delivery points to individual’s activity-travel patterns: A greener last mile alternative?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 121(C), pages 84-99.
    4. Guerriero, Francesca & Miglionico, Giovanna & Olivito, Filomena, 2016. "Location and reorganization problems: The Calabrian health care system case," European Journal of Operational Research, Elsevier, vol. 250(3), pages 939-954.
    5. Melo, M.T. & Nickel, S. & Saldanha-da-Gama, F., 2009. "Facility location and supply chain management - A review," European Journal of Operational Research, Elsevier, vol. 196(2), pages 401-412, July.
    6. Janjevic, Milena & Winkenbach, Matthias & Merchán, Daniel, 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, Elsevier, vol. 131(C), pages 37-67.
    7. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    8. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
    9. Richard Church & Charles R. Velle, 1974. "The Maximal Covering Location Problem," Papers in Regional Science, Wiley Blackwell, vol. 32(1), pages 101-118, January.
    10. Bard, Jonathan F. & Jarrah, Ahmad I., 2013. "Integrating commercial and residential pickup and delivery networks: A case study," Omega, Elsevier, vol. 41(4), pages 706-720.
    11. Yuen, Kum Fai & Wang, Xueqin & Ng, Li Ting Wendy & Wong, Yiik Diew, 2018. "An investigation of customers’ intention to use self-collection services for last-mile delivery," Transport Policy, Elsevier, vol. 66(C), pages 1-8.
    12. Wenxuan Shan & Qianqian Yan & Chao Chen & Mengjie Zhang & Baozhen Yao & Xuemei Fu, 2019. "Optimization of competitive facility location for chain stores," Annals of Operations Research, Springer, vol. 273(1), pages 187-205, February.
    13. Ortiz-Astorquiza, Camilo & Contreras, Ivan & Laporte, Gilbert, 2018. "Multi-level facility location problems," European Journal of Operational Research, Elsevier, vol. 267(3), pages 791-805.
    14. Taherkhani, Gita & Alumur, Sibel A., 2019. "Profit maximizing hub location problems," Omega, Elsevier, vol. 86(C), pages 1-15.
    15. Van Nguyen, Truong & Zhou, Li & Chong, Alain Yee Loong & Li, Boying & Pu, Xiaodie, 2020. "Predicting customer demand for remanufactured products: A data-mining approach," European Journal of Operational Research, Elsevier, vol. 281(3), pages 543-558.
    16. Teodora Dan & Patrice Marcotte, 2019. "Competitive Facility Location with Selfish Users and Queues," Operations Research, INFORMS, vol. 67(2), pages 479-497, March.
    17. Eléonora Morganti & Laetitia Dablanc & François Fortin, 2014. "Final deliveries for online shopping: the deployment of pickup point networks in urban and suburban areas," Post-Print hal-01067223, HAL.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kahr, Michael, 2022. "Determining locations and layouts for parcel lockers to support supply chain viability at the last mile," Omega, Elsevier, vol. 113(C).
    2. Yang, Tiannuo & Chu, Zhongzhu & Wang, Bailin, 2023. "Feasibility on the integration of passenger and freight transportation in rural areas: A service mode and an optimization model," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    3. Alyahya, Mansour & Agag, Gomaa & Aliedan, Meqbel & Abdelmoety, Ziad H., 2023. "A cross-cultural investigation of the relationship between eco-innovation and customers boycott behaviour," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    4. Iacocca, Kathleen & Mahar, Stephen & Daniel Wright, P., 2022. "Strategic horizontal integration for drug cost reduction in the pharmaceutical supply chain," Omega, Elsevier, vol. 108(C).
    5. Wang, Haibo & Alidaee, Bahram, 2023. "White-glove service delivery: A quantitative analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Iacocca, Kathleen & Mahar, Stephen & Daniel Wright, P., 2022. "Strategic horizontal integration for drug cost reduction in the pharmaceutical supply chain," Omega, Elsevier, vol. 108(C).
    2. Ashu Kedia & Diana Kusumastuti & Alan Nicholson, 2019. "Establishing Collection and Delivery Points to Encourage the Use of Active Transport: A Case Study in New Zealand Using a Consumer-Centric Approach," Sustainability, MDPI, vol. 11(22), pages 1-23, November.
    3. Fadda, Edoardo & Manerba, Daniele & Cabodi, Gianpiero & Camurati, Paolo Enrico & Tadei, Roberto, 2021. "Comparative analysis of models and performance indicators for optimal service facility location," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    4. Guarino Neto, Luigi & Geraldo Vidal Vieira, José, 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, Elsevier, vol. 74(C).
    5. Yang, Hye-jeong & Fang, Mingjie & Yao, Jinge & Su, Miao, 2023. "Green cooperation in last-mile logistics and consumer loyalty: An empirical analysis of a theoretical framework," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    6. Zhang, Wenwei & Xu, Min & Wang, Shuaian, 2023. "Joint location and pricing optimization of self-service in urban logistics considering customers’ choice behavior," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 174(C).
    7. Saldanha-da-Gama, Francisco, 2022. "Facility Location in Logistics and Transportation: An enduring relationship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    8. Farahani, Reza Zanjirani & Fallah, Samira & Ruiz, Rubén & Hosseini, Sara & Asgari, Nasrin, 2019. "OR models in urban service facility location: A critical review of applications and future developments," European Journal of Operational Research, Elsevier, vol. 276(1), pages 1-27.
    9. Milioti, Christina & Pramatari, Katerina & Kelepouri, Ioanna, 2020. "Modelling consumers’ acceptance for the click and collect service," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    10. Lin, Yun Hui & Wang, Yuan & He, Dongdong & Lee, Loo Hay, 2020. "Last-mile delivery: Optimal locker location under multinomial logit choice model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    11. Mommens, Koen & Buldeo Rai, Heleen & van Lier, Tom & Macharis, Cathy, 2021. "Delivery to homes or collection points? A sustainability analysis for urban, urbanised and rural areas in Belgium," Journal of Transport Geography, Elsevier, vol. 94(C).
    12. Matthias Winkenbach & Alain Roset & Stefan Spinler, 2016. "Strategic Redesign of Urban Mail and Parcel Networks at La Poste," Interfaces, INFORMS, vol. 46(5), pages 445-458, October.
    13. Eliş, Haluk & Tansel, Barbaros & Oğuz, Osman & Güney, Mesut & Kian, Ramez, 2021. "On guarding real terrains: The terrain guarding and the blocking path problems," Omega, Elsevier, vol. 102(C).
    14. Hamid Mousavi & Soroush Avakh Darestani & Parham Azimi, 2021. "An artificial neural network based mathematical model for a stochastic health care facility location problem," Health Care Management Science, Springer, vol. 24(3), pages 499-514, September.
    15. Junming Liu & Weiwei Chen & Jingyuan Yang & Hui Xiong & Can Chen, 2022. "Iterative Prediction-and-Optimization for E-Logistics Distribution Network Design," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 769-789, March.
    16. Camilo Ortiz-Astorquiza & Ivan Contreras & Gilbert Laporte, 2019. "An Exact Algorithm for Multilevel Uncapacitated Facility Location," Transportation Science, INFORMS, vol. 53(4), pages 1085-1106, July.
    17. Huizhu Wang & Jianqin Zhou & Ling Zhou, 2024. "A Lattice Boltzmann Method-like Algorithm for the Maximal Covering Location Problem on the Complex Network: Application to Location of Railway Emergency-Rescue Spot," Mathematics, MDPI, vol. 12(2), pages 1-20, January.
    18. Leung, Abraham & Lachapelle, Ugo & Burke, Matthew, 2023. "Spatio-temporal analysis of Australia Post parcel locker use during the initial system growth phase in Queensland (2013–2017)," Journal of Transport Geography, Elsevier, vol. 110(C).
    19. Conrado V. Plaza & Vanessa de A. Guimarães & Glaydston Ribeiro & Laura Bahiense, 2020. "Economic and environmental location of logistics integration centers: the Brazilian soybean transportation case," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 749-771, October.
    20. Pallant, Jason I. & Danaher, Peter J. & Sands, Sean J. & Danaher, Tracey S., 2017. "An empirical analysis of factors that influence retail website visit types," Journal of Retailing and Consumer Services, Elsevier, vol. 39(C), pages 62-70.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:100:y:2021:i:c:s030504831931535x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.