IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v264y2023ics0925527323002116.html
   My bibliography  Save this article

On the value of operational flexibility in the trailer shipment and assignment problem: Data-driven approaches and reinforcement learning

Author

Listed:
  • Jung, Seung Hwan
  • Yang, Yunsi

Abstract

This paper addresses the trailer shipment problem—the task of managing the optimal weight of products in a trailer, taking into consideration the uncertain weight of tractors provided by Third-Party Logistics (3PL) providers, and abiding by the gross weight regulation. We propose a series of data-analytics methodologies, including Sample Average Approximation (SAA), Empirical Risk Minimization (ERM), and a dynamic trailer assignment methodology using Reinforcement Learning (RL), to optimize the trailer shipment process. The introduction of operational flexibility and the dynamic utilization of tractor weight information upon arrival are pivotal to the effectiveness of the RL-based methodology. To validate our approaches, we apply them to transaction-level shipping data from a real company. The results demonstrate significant cost reductions in the logistics process, driven by the dynamic assignment methodology which allows efficient selection of trailers to suit varying tractor weights. This research proposes an innovative approach to the prevalent trailer shipment problem, applicable to a wide range of industries using 3PL outsourcing. Through this work, we demonstrate the transformative potential of data-analytics methodologies to enhance efficiency and profitability in logistics operations.

Suggested Citation

  • Jung, Seung Hwan & Yang, Yunsi, 2023. "On the value of operational flexibility in the trailer shipment and assignment problem: Data-driven approaches and reinforcement learning," International Journal of Production Economics, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:proeco:v:264:y:2023:i:c:s0925527323002116
    DOI: 10.1016/j.ijpe.2023.108979
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527323002116
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2023.108979?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. Sumit Kunnumkal & Kalyan Talluri, 2016. "On a Piecewise-Linear Approximation for Network Revenue Management," Mathematics of Operations Research, INFORMS, vol. 41(1), pages 72-91, February.
    2. Egeblad, Jens & Garavelli, Claudio & Lisi, Stefano & Pisinger, David, 2010. "Heuristics for container loading of furniture," European Journal of Operational Research, Elsevier, vol. 200(3), pages 881-892, February.
    3. Bortfeldt, Andreas & Wäscher, Gerhard, 2013. "Constraints in container loading – A state-of-the-art review," European Journal of Operational Research, Elsevier, vol. 229(1), pages 1-20.
    4. Ali Jamshidi & Shahrzad Faghih‐Roohi & Siamak Hajizadeh & Alfredo Núñez & Robert Babuska & Rolf Dollevoet & Zili Li & Bart De Schutter, 2017. "A Big Data Analysis Approach for Rail Failure Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1495-1507, August.
    5. Manuel Iori & Silvano Martello, 2010. "Routing problems with loading constraints," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 4-27, July.
    6. Retsef Levi & Georgia Perakis & Joline Uichanco, 2015. "The Data-Driven Newsvendor Problem: New Bounds and Insights," Operations Research, INFORMS, vol. 63(6), pages 1294-1306, December.
    7. Ruomeng Cui & Santiago Gallino & Antonio Moreno & Dennis J. Zhang, 2018. "The Operational Value of Social Media Information," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1749-1769, October.
    8. Chen, Yi-Ting & Sun, Edward W. & Chang, Ming-Feng & Lin, Yi-Bing, 2021. "Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0," International Journal of Production Economics, Elsevier, vol. 238(C).
    9. Giannoccaro, Ilaria & Pontrandolfo, Pierpaolo, 2002. "Inventory management in supply chains: a reinforcement learning approach," International Journal of Production Economics, Elsevier, vol. 78(2), pages 153-161, July.
    10. Bodendorf, Frank & Sauter, Maximilian & Franke, Jörg, 2023. "A mixed methods approach to analyze and predict supply disruptions by combining causal inference and deep learning," International Journal of Production Economics, Elsevier, vol. 256(C).
    11. Gah-Yi Ban & Cynthia Rudin, 2019. "The Big Data Newsvendor: Practical Insights from Machine Learning," Operations Research, INFORMS, vol. 67(1), pages 90-108, January.
    12. Yan Shang & David Dunson & Jing-Sheng Song, 2017. "Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics," Operations Research, INFORMS, vol. 65(6), pages 1574-1588, December.
    13. Omar, Haytham & Klibi, Walid & Babai, M. Zied & Ducq, Yves, 2023. "Basket data-driven approach for omnichannel demand forecasting," International Journal of Production Economics, Elsevier, vol. 257(C).
    14. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    15. Ardekani, Zahra Fozouni & Sobhani, Seyed Mohammad Javad & Barbosa, Marcelo Werneck & de Sousa, Paulo Renato, 2023. "Transition to a sustainable food supply chain during disruptions: A study on the Brazilian food companies in the Covid-19 era," International Journal of Production Economics, Elsevier, vol. 257(C).
    16. Islam, Samiul & Amin, Saman Hassanzadeh & Wardley, Leslie J., 2021. "Machine learning and optimization models for supplier selection and order allocation planning," International Journal of Production Economics, Elsevier, vol. 242(C).
    17. Qiu, Huaxin & Wang, Sutong & Yin, Yunqiang & Wang, Dujuan & Wang, Yanzhang, 2022. "A deep reinforcement learning-based approach for the home delivery and installation routing problem," International Journal of Production Economics, Elsevier, vol. 244(C).
    18. Kirac, Emre & Milburn, Ashlea Bennett, 2018. "A general framework for assessing the value of social data for disaster response logistics planning," European Journal of Operational Research, Elsevier, vol. 269(2), pages 486-500.
    19. Xingxing Chen & Jacob Feldman & Seung Hwan Jung & Panos Kouvelis, 2022. "Approximation schemes for the joint inventory selection and online resource allocation problem," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3143-3159, August.
    20. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    21. Velibor V. Mišić & Georgia Perakis, 2020. "Data Analytics in Operations Management: A Review," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 158-169, January.
    22. Raymond Yiu Keung Lau & Wenping Zhang & Wei Xu, 2018. "Parallel Aspect‐Oriented Sentiment Analysis for Sales Forecasting with Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1775-1794, October.
    23. Erjie Ang & Sara Kwasnick & Mohsen Bayati & Erica L. Plambeck & Michael Aratow, 2016. "Accurate Emergency Department Wait Time Prediction," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 141-156, February.
    24. Daniel Adelman, 2007. "Dynamic Bid Prices in Revenue Management," Operations Research, INFORMS, vol. 55(4), pages 647-661, August.
    25. Manuel Iori & Silvano Martello, 2010. "Rejoinder on: Routing problems with loading constraints," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 41-42, July.
    26. Sai Ho Chung & Hoi Lam Ma & Hing Kai Chan, 2017. "Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1443-1458, August.
    27. Qin, Wei & Sun, Yan-Ning & Zhuang, Zi-Long & Lu, Zhi-Yao & Zhou, Yao-Ming, 2021. "Multi-agent reinforcement learning-based dynamic task assignment for vehicles in urban transportation system," International Journal of Production Economics, Elsevier, vol. 240(C).
    28. Preil, Deniz & Krapp, Michael, 2022. "Bandit-based inventory optimisation: Reinforcement learning in multi-echelon supply chains," International Journal of Production Economics, Elsevier, vol. 252(C).
    29. Kris Johnson Ferreira & Bin Hong Alex Lee & David Simchi-Levi, 2016. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 69-88, February.
    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. Silva, Elsa & Ramos, António G. & Oliveira, José F., 2018. "Load balance recovery for multi-drop distribution problems: A mixed integer linear programming approach," Transportation Research Part B: Methodological, Elsevier, vol. 116(C), pages 62-75.
    2. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    3. Julian Senoner & Bernhard Kratzwald & Milan Kuzmanovic & Torbjørn H. Netland & Stefan Feuerriegel, 2023. "Addressing distributional shifts in operations management: The case of order fulfillment in customized production," Production and Operations Management, Production and Operations Management Society, vol. 32(10), pages 3022-3042, October.
    4. Alonso, M.T. & Alvarez-Valdes, R. & Iori, M. & Parreño, F. & Tamarit, J.M., 2017. "Mathematical models for multicontainer loading problems," Omega, Elsevier, vol. 66(PA), pages 106-117.
    5. Erkip, Nesim Kohen, 2023. "Can accessing much data reshape the theory? Inventory theory under the challenge of data-driven systems," European Journal of Operational Research, Elsevier, vol. 308(3), pages 949-959.
    6. Carlos A. Vega-Mejía & Jairo R. Montoya-Torres & Sardar M. N. Islam, 2019. "Consideration of triple bottom line objectives for sustainability in the optimization of vehicle routing and loading operations: a systematic literature review," Annals of Operations Research, Springer, vol. 273(1), pages 311-375, February.
    7. Ramos, António G. & Silva, Elsa & Oliveira, José F., 2018. "A new load balance methodology for container loading problem in road transportation," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1140-1152.
    8. Bortfeldt, Andreas & Wäscher, Gerhard, 2013. "Constraints in container loading – A state-of-the-art review," European Journal of Operational Research, Elsevier, vol. 229(1), pages 1-20.
    9. Xiang Song & Dylan Jones & Nasrin Asgari & Tim Pigden, 2020. "Multi-objective vehicle routing and loading with time window constraints: a real-life application," Annals of Operations Research, Springer, vol. 291(1), pages 799-825, August.
    10. Xuan Bi & Gediminas Adomavicius & William Li & Annie Qu, 2022. "Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1644-1660, May.
    11. Cherkesly, Marilène & Gschwind, Timo, 2022. "The pickup and delivery problem with time windows, multiple stacks, and handling operations," European Journal of Operational Research, Elsevier, vol. 301(2), pages 647-666.
    12. Bortfeldt, Andreas & Yi, Junmin, 2020. "The Split Delivery Vehicle Routing Problem with three-dimensional loading constraints," European Journal of Operational Research, Elsevier, vol. 282(2), pages 545-558.
    13. Xinxue (Shawn) Qu & Aslan Lotfi & Dipak C. Jain & Zhengrui Jiang, 2022. "Predicting upgrade timing for successive product generations: An exponential‐decay proportional hazard model," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2067-2083, May.
    14. Chung, Sai-Ho, 2021. "Applications of smart technologies in logistics and transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    15. Tsan‐Ming Choi & Subodha Kumar & Xiaohang Yue & Hau‐Ling Chan, 2022. "Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 9-31, January.
    16. Bonet Filella, Guillem & Trivella, Alessio & Corman, Francesco, 2023. "Modeling soft unloading constraints in the multi-drop container loading problem," European Journal of Operational Research, Elsevier, vol. 308(1), pages 336-352.
    17. Dazhou Lei & Hao Hu & Dongyang Geng & Jianshen Zhang & Yongzhi Qi & Sheng Liu & Zuo‐Jun Max Shen, 2023. "New product life cycle curve modeling and forecasting with product attributes and promotion: A Bayesian functional approach," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 655-673, February.
    18. Xiangyu Chang & Yinghui Huang & Mei Li & Xin Bo & Subodha Kumar, 2021. "Efficient Detection of Environmental Violators: A Big Data Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1246-1270, May.
    19. Xiaodan Zhu & Anh Ninh & Hui Zhao & Zhenming Liu, 2021. "Demand Forecasting with Supply‐Chain Information and Machine Learning: Evidence in the Pharmaceutical Industry," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3231-3252, September.
    20. Yu, Bin & Guo, Zhen & Asian, Sobhan & Wang, Huaizhu & Chen, Gang, 2019. "Flight delay prediction for commercial air transport: A deep learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 203-221.

    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:proeco:v:264:y:2023:i:c:s0925527323002116. 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/locate/ijpe .

    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.