IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v203y2025ics1366554525003849.html

Warehouse-distribution integration routing problem under real-world transport conditions with multiple time windows and variable loading efficiency

Author

Listed:
  • Liu, Yinying
  • Shi, Xin
  • Liu, Jianmeng
  • Qin, Pengjie
  • Tang, Cheng

Abstract

The growing trend of e-commerce enterprises establishing their own warehouses has significantly enhanced operational efficiency in urban delivery. This has given rise to the Warehouse-Distribution Integration Routing Problem (WDIRP), a variant of the Vehicle Routing Problem (VRP). However, this type of problem under real-world transport conditions, especially considering multiple time windows and variable loading efficiency, has been insufficiently studied in the existing literature. This research addresses these characteristics to more accurately reflect real-world urban delivery challenges. To solve the problem, we propose a Mixed Integer Linear Programming (MILP) model for the WDIRP with a novel methodological approach to formulate multiple time windows, which can be efficiently solved using IBM ILOG CPLEX Optimization Studio (CPLEX). Additionally, we develop a two-stage heuristic algorithm that incorporates a multi-greedy method and a Variable Neighborhood Search (VNS) method, featuring new problem-specific neighborhood structures. The computational experiments encompass 20 randomly generated small-scale instances, 20 benchmark instances, and 18 realistic instances derived from diverse geographical areas and periods in Chongqing, China. Eighteen larger-scale extended instances are also developed to further test scalability, involving up to 50 warehouses and 500 customers. The realistic instances incorporate real-world transport conditions, including road topology, traffic performance, node distribution, and customer demand, with their corresponding parameters extracted or predicted using big data technologies. Our results demonstrate that the proposed model outperforms common formulations in computational performance, and the two-stage heuristic algorithm is superior to alternative approaches in solving the WDIRP. In addition to presenting results for various instances, we implement a mountainous city simulation model to achieve the 3D simulation of the delivery scheme. The findings provide valuable insights for logistics enterprises aiming to optimize urban delivery operations.

Suggested Citation

  • Liu, Yinying & Shi, Xin & Liu, Jianmeng & Qin, Pengjie & Tang, Cheng, 2025. "Warehouse-distribution integration routing problem under real-world transport conditions with multiple time windows and variable loading efficiency," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 203(C).
  • Handle: RePEc:eee:transe:v:203:y:2025:i:c:s1366554525003849
    DOI: 10.1016/j.tre.2025.104343
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554525003849
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tre.2025.104343?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Männel, Dirk & Bortfeldt, Andreas, 2016. "A hybrid algorithm for the vehicle routing problem with pickup and delivery and three-dimensional loading constraints," European Journal of Operational Research, Elsevier, vol. 254(3), pages 840-858.
    2. Dönmez, Sercan & Koç, Çağrı & Altıparmak, Fulya, 2022. "The mixed fleet vehicle routing problem with partial recharging by multiple chargers: Mathematical model and adaptive large neighborhood search," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    3. Qureshi, A.G. & Taniguchi, E. & Yamada, T., 2009. "An exact solution approach for vehicle routing and scheduling problems with soft time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(6), pages 960-977, November.
    4. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    5. Cavaliere, Francesco & Accorsi, Luca & Laganà, Demetrio & Musmanno, Roberto & Vigo, Daniele, 2024. "An efficient heuristic for very large-scale vehicle routing problems with simultaneous pickup and delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    6. Sun, Qiuxia & Sun, Yixin & Sun, Lu & Li, Qing & Zhao, Jianli & Zhang, Yu & He, Hao, 2019. "Research on traffic congestion characteristics of city business circles based on TPI data: The case of Qingdao, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    7. J. Schulze & T. Fahle, 1999. "A parallel algorithm for the vehicle routing problem with time window constraints," Annals of Operations Research, Springer, vol. 86(0), pages 585-607, January.
    8. Sadati, Mir Ehsan Hesam & Çatay, Bülent, 2021. "A hybrid variable neighborhood search approach for the multi-depot green vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    9. Dong, Yucheng & Xu, Yinfeng & Li, Hongyi & Dai, Min, 2008. "A comparative study of the numerical scales and the prioritization methods in AHP," European Journal of Operational Research, Elsevier, vol. 186(1), pages 229-242, April.
    10. Xu, Jiuping & Yan, Fang & Li, Steven, 2011. "Vehicle routing optimization with soft time windows in a fuzzy random environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1075-1091.
    11. Mo, Pengli & Yao, Yu & D’Ariano, Andrea & Liu, Zhiyuan, 2023. "The vehicle routing problem with underground logistics: Formulation and algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    12. Brian Kallehauge & Jesper Larsen & Oli B.G. Madsen & Marius M. Solomon, 2005. "Vehicle Routing Problem with Time Windows," Springer Books, in: Guy Desaulniers & Jacques Desrosiers & Marius M. Solomon (ed.), Column Generation, chapter 0, pages 67-98, Springer.
    13. Yuan, Yuan & Cattaruzza, Diego & Ogier, Maxime & Semet, Frédéric & Vigo, Daniele, 2021. "A column generation based heuristic for the generalized vehicle routing problem with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    14. Gonzalez-Calderon, Carlos A. & Holguín-Veras, José, 2019. "Entropy-based freight tour synthesis and the role of traffic count sampling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 121(C), pages 63-83.
    15. Lai, David S.W. & Caliskan Demirag, Ozgun & Leung, Janny M.Y., 2016. "A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 86(C), pages 32-52.
    16. Yu, Zidong & Wang, Haotian & Liu, Xintao, 2024. "Mobility heterogeneity of urban freight areas: Geospatial evidence from shared logistics dynamics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).
    17. Yanik, Seda & Bozkaya, Burcin & deKervenoael, Ronan, 2014. "A new VRPPD model and a hybrid heuristic solution approach for e-tailing," European Journal of Operational Research, Elsevier, vol. 236(3), pages 879-890.
    18. De La Vega, Diego Soto & Vieira, José Geraldo Vidal & Toso, Eli Angela Vitor & de Faria, Rosane Nunes, 2018. "A decision on the truckload and less-than-truckload problem: An approach based on MCDA," International Journal of Production Economics, Elsevier, vol. 195(C), pages 132-145.
    19. Coelho, Leandro Callegari & De Maio, Annarita & Laganà, Demetrio, 2020. "A variable MIP neighborhood descent for the multi-attribute inventory routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    20. Miller, Seth & Laan, Zachary Vander & Marković, Nikola, 2020. "Scaling GPS trajectories to match point traffic counts: A convex programming approach and Utah case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    21. Hosni, Hadi & Naoum-Sawaya, Joe & Artail, Hassan, 2014. "The shared-taxi problem: Formulation and solution methods," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 303-318.
    22. Veenstra, Marjolein & Roodbergen, Kees Jan & Coelho, Leandro C. & Zhu, Stuart X., 2018. "A simultaneous facility location and vehicle routing problem arising in health care logistics in the Netherlands," European Journal of Operational Research, Elsevier, vol. 268(2), pages 703-715.
    23. Okabe, Atsuyuki & Suzuki, Atsuo, 1997. "Locational optimization problems solved through Voronoi diagrams," European Journal of Operational Research, Elsevier, vol. 98(3), pages 445-456, May.
    24. López-Sánchez, A.D. & Hernández-Díaz, A.G. & Vigo, D. & Caballero, R. & Molina, J., 2014. "A multi-start algorithm for a balanced real-world Open Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 238(1), pages 104-113.
    25. Santos, Luís & Coutinho-Rodrigues, João & Current, John R., 2010. "An improved ant colony optimization based algorithm for the capacitated arc routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 246-266, February.
    26. Li, Guoqi & Sun, Wenjie & Yuan, Quan & Liu, Sijing, 2020. "Planning versus the market: Logistics establishments and logistics parks in Chongqing, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    27. Atefi, Reza & Salari, Majid & C. Coelho, Leandro & Renaud, Jacques, 2018. "The open vehicle routing problem with decoupling points," European Journal of Operational Research, Elsevier, vol. 265(1), pages 316-327.
    28. Gmira, Maha & Gendreau, Michel & Lodi, Andrea & Potvin, Jean-Yves, 2021. "Tabu search for the time-dependent vehicle routing problem with time windows on a road network," European Journal of Operational Research, Elsevier, vol. 288(1), pages 129-140.
    29. Beynon, Malcolm, 2002. "An analysis of distributions of priority values from alternative comparison scales within AHP," European Journal of Operational Research, Elsevier, vol. 140(1), pages 104-117, July.
    30. Billy E. Gillett & Leland R. Miller, 1974. "A Heuristic Algorithm for the Vehicle-Dispatch Problem," Operations Research, INFORMS, vol. 22(2), pages 340-349, April.
    31. Hellsten, Erik Orm & Sacramento, David & Pisinger, David, 2020. "An adaptive large neighbourhood search heuristic for routing and scheduling feeder vessels in multi-terminal ports," European Journal of Operational Research, Elsevier, vol. 287(2), pages 682-698.
    32. F. Errico & G. Desaulniers & M. Gendreau & W. Rei & L.-M. Rousseau, 2018. "The vehicle routing problem with hard time windows and stochastic service times," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(3), pages 223-251, September.
    33. Wang, Mengtong & Miao, Lixin & Zhang, Canrong, 2021. "A branch-and-price algorithm for a green location routing problem with multi-type charging infrastructure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    34. Marius M. Solomon, 1987. "Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints," Operations Research, INFORMS, vol. 35(2), pages 254-265, April.
    35. Cortés-Murcia, David L. & Prodhon, Caroline & Murat Afsar, H., 2019. "The electric vehicle routing problem with time windows, partial recharges and satellite customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 184-206.
    Full references (including those not matched with items on IDEAS)

    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. Aderemi Oluyinka Adewumi & Olawale Joshua Adeleke, 2018. "A survey of recent advances in vehicle routing problems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 155-172, February.
    2. Kerscher, Christoph & Minner, Stefan, 2025. "Decompose-route-improve framework for solving large-scale vehicle routing problems with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 204(C).
    3. Diana Puspita Sari & Nur Aini Masruroh & Anna Maria Sri Asih, 2021. "Extended Maximal Covering Location and Vehicle Routing Problems in Designing Smartphone Waste Collection Channels: A Case Study of Yogyakarta Province, Indonesia," Sustainability, MDPI, vol. 13(16), pages 1-23, August.
    4. Babagolzadeh, Mahla & Zhang, Yahua & Abbasi, Babak & Shrestha, Anup & Zhang, Anming, 2022. "Promoting Australian regional airports with subsidy schemes: Optimised downstream logistics using vehicle routing problem," Transport Policy, Elsevier, vol. 128(C), pages 38-51.
    5. Wang, Yong & Wei, Zikai & Luo, Siyu & Zhou, Jingxin & Zhen, Lu, 2024. "Collaboration and resource sharing in the multidepot time-dependent vehicle routing problem with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
    6. Qi, Mingyao & Lin, Wei-Hua & Li, Nan & Miao, Lixin, 2012. "A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 248-257.
    7. Liu, Yiming & Yu, Yang & Baldacci, Roberto & Tang, Jiafu & Sun, Wei, 2025. "Optimizing carbon emissions in green logistics for time-dependent routing," Transportation Research Part B: Methodological, Elsevier, vol. 192(C).
    8. Olli Bräysy & Michel Gendreau, 2005. "Vehicle Routing Problem with Time Windows, Part II: Metaheuristics," Transportation Science, INFORMS, vol. 39(1), pages 119-139, February.
    9. Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2013. "Heuristics for multi-attribute vehicle routing problems: A survey and synthesis," European Journal of Operational Research, Elsevier, vol. 231(1), pages 1-21.
    10. Tan Yu & Yongpei Guan & Xiang Zhong, 2024. "Visiting nurses assignment and routing for decentralized telehealth service networks," Annals of Operations Research, Springer, vol. 341(2), pages 1191-1221, October.
    11. Lijun Fan, 2023. "A hybrid adaptive large neighborhood search for time-dependent open electric vehicle routing problem with hybrid energy replenishment strategies," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-38, September.
    12. Zhang, Li & Liu, Zhongshan & Yu, Lan & Fang, Ke & Yao, Baozhen & Yu, Bin, 2022. "Routing optimization of shared autonomous electric vehicles under uncertain travel time and uncertain service time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    13. Md. Anisul Islam & Yuvraj Gajpal, 2021. "Optimization of Conventional and Green Vehicles Composition under Carbon Emission Cap," Sustainability, MDPI, vol. 13(12), pages 1-20, June.
    14. Sana Jawarneh & Salwani Abdullah, 2015. "Sequential Insertion Heuristic with Adaptive Bee Colony Optimisation Algorithm for Vehicle Routing Problem with Time Windows," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-23, July.
    15. Dönmez, Sercan & Koç, Çağrı & Altıparmak, Fulya, 2022. "The mixed fleet vehicle routing problem with partial recharging by multiple chargers: Mathematical model and adaptive large neighborhood search," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    16. Sebastián Dávila & Miguel Alfaro & Guillermo Fuertes & Manuel Vargas & Mauricio Camargo, 2021. "Vehicle Routing Problem with Deadline and Stochastic Service Times: Case of the Ice Cream Industry in Santiago City of Chile," Mathematics, MDPI, vol. 9(21), pages 1-18, October.
    17. Yu Zhang & Zhenzhen Zhang & Andrew Lim & Melvyn Sim, 2021. "Robust Data-Driven Vehicle Routing with Time Windows," Operations Research, INFORMS, vol. 69(2), pages 469-485, March.
    18. Uchoa, Eduardo & Pecin, Diego & Pessoa, Artur & Poggi, Marcus & Vidal, Thibaut & Subramanian, Anand, 2017. "New benchmark instances for the Capacitated Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 257(3), pages 845-858.
    19. Samuel Reong & Hui-Ming Wee & Yu-Lin Hsiao, 2022. "20 Years of Particle Swarm Optimization Strategies for the Vehicle Routing Problem: A Bibliometric Analysis," Mathematics, MDPI, vol. 10(19), pages 1-19, October.
    20. M. Alinaghian & M. Ghazanfari & N. Norouzi & H. Nouralizadeh, 2017. "A Novel Model for the Time Dependent Competitive Vehicle Routing Problem: Modified Random Topology Particle Swarm Optimization," Networks and Spatial Economics, Springer, vol. 17(4), pages 1185-1211, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:transe:v:203:y:2025:i:c:s1366554525003849. 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/600244/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.