IDEAS home Printed from https://ideas.repec.org/a/igg/jcini0/v15y2021i2p139-157.html
   My bibliography  Save this article

Research on IRP of Perishable Products Based on Mobile Data Sharing Environment

Author

Listed:
  • Zelin Wang

    (School of Information Science and Technology, Nantong University, Nantong, China)

  • Xiaoning Wei

    (School of Information Science and Technology, Nantong University, Nantong, China)

  • Jiansheng Pan

    (School of Information Science and Technology, Nantong University, Nantong, China)

Abstract

Inventory routing problem (IRP) has always been a hot issue. Due to its particularity, perishable products have high requirements for inventory and transportation. In order to reduce the losses of perishable goods and improve the storage efficiency of perishable goods, based on the general inventory path problem, this paper further has studied the IRP of perishable goods. In addition, in the process of product distribution and transportation, there are a lot of real-time product information generated dynamically. These real-time mobile data must be shared by the whole distribution network, which will also dynamically affect the efficiency of IRP research. On the basis of some assumptions, the mathematical model has been established with inventory and vehicle as constraints and the total cost of the system as the objective. In view of the particularity of perishable inventory path problem, this paper proposed an improved differential evolution algorithm (IDE) to improve the differential evolution algorithm from two aspects. Firstly, the population has been initialized by gridding and the greedy local optimization algorithm has been used to assist the differential evolution algorithm, with these measures to improve the convergence speed of the algorithm. Then, the accuracy of the algorithm is improved by the adaptive scaling factor, two evolution modes and changing the constraints of the problem. Then the improved algorithm has been used to solve the inventory path problem. The results of numerical experiments show that the algorithm is effective and feasible and can improve the accuracy and speed up the convergence of the algorithm.

Suggested Citation

  • Zelin Wang & Xiaoning Wei & Jiansheng Pan, 2021. "Research on IRP of Perishable Products Based on Mobile Data Sharing Environment," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(2), pages 139-157, April.
  • Handle: RePEc:igg:jcini0:v:15:y:2021:i:2:p:139-157
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCINI.20210401.oa10
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. van Donselaar, K. & van Woensel, T. & Broekmeulen, R. & Fransoo, J., 2006. "Inventory control of perishables in supermarkets," International Journal of Production Economics, Elsevier, vol. 104(2), pages 462-472, December.
    2. Devapriya, Priyantha & Ferrell, William & Geismar, Neil, 2017. "Integrated production and distribution scheduling with a perishable product," European Journal of Operational Research, Elsevier, vol. 259(3), pages 906-916.
    3. Alvarez, Aldair & Cordeau, Jean-François & Jans, Raf & Munari, Pedro & Morabito, Reinaldo, 2020. "Formulations, branch-and-cut and a hybrid heuristic algorithm for an inventory routing problem with perishable products," European Journal of Operational Research, Elsevier, vol. 283(2), pages 511-529.
    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. Zelin Wang & Xiaoning Wei & Jiansheng Pan, 2021. "Research on IRP of Perishable Products Based on Mobile Data Sharing Environment," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(2), pages 5-23, April.
    2. Liu, Wenqian & Ke, Ginger Y. & Chen, Jian & Zhang, Lianmin, 2020. "Scheduling the distribution of blood products: A vendor-managed inventory routing approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    3. Noordhoek, Marije & Dullaert, Wout & Lai, David S.W. & de Leeuw, Sander, 2018. "A simulation–optimization approach for a service-constrained multi-echelon distribution network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 292-311.
    4. Ting, S.L. & Tse, Y.K. & Ho, G.T.S. & Chung, S.H. & Pang, G., 2014. "Mining logistics data to assure the quality in a sustainable food supply chain: A case in the red wine industry," International Journal of Production Economics, Elsevier, vol. 152(C), pages 200-209.
    5. Zhang, Zhe & Song, Xiaoling & Gong, Xue & Yin, Yong & Lev, Benjamin & Zhou, Xiaoyang, 2024. "Coordinated seru scheduling and distribution operation problems with DeJong’s learning effects," European Journal of Operational Research, Elsevier, vol. 313(2), pages 452-464.
    6. Dong Li & Xiaojun Wang, 2017. "Dynamic supply chain decisions based on networked sensor data: an application in the chilled food retail chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5127-5141, September.
    7. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    8. Bachtenkirch, David & Bock, Stefan, 2022. "Finding efficient make-to-order production and batch delivery schedules," European Journal of Operational Research, Elsevier, vol. 297(1), pages 133-152.
    9. Arda, Yasemin & Cattaruzza, Diego & François, Véronique & Ogier, Maxime, 2024. "Home chemotherapy delivery: An integrated production scheduling and multi-trip vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 317(2), pages 468-486.
    10. Wang, Gang, 2024. "Order assignment and two-stage integrated scheduling in fruit and vegetable supply chains," Omega, Elsevier, vol. 124(C).
    11. Alvarez, Aldair & Miranda, Pedro & Rohmer, S.U.K., 2022. "Production routing for perishable products," Omega, Elsevier, vol. 111(C).
    12. Haijema, Rene, 2014. "Optimal ordering, issuance and disposal policies for inventory management of perishable products," International Journal of Production Economics, Elsevier, vol. 157(C), pages 158-169.
    13. Bouchery, Yann & Ghaffari, Asma & Jemai, Zied & Tan, Tarkan, 2017. "Impact of coordination on costs and carbon emissions for a two-echelon serial economic order quantity problem," European Journal of Operational Research, Elsevier, vol. 260(2), pages 520-533.
    14. Buisman, M.E. & Haijema, R. & Bloemhof-Ruwaard, J.M., 2019. "Discounting and dynamic shelf life to reduce fresh food waste at retailers," International Journal of Production Economics, Elsevier, vol. 209(C), pages 274-284.
    15. Anna‐Lena Sachs & Michael Becker‐Peth & Stefan Minner & Ulrich W. Thonemann, 2022. "Empirical newsvendor biases: Are target service levels achieved effectively and efficiently?," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1839-1855, April.
    16. Alexis Robbes & Yannick Kergosien & Virginie André & Jean-Charles Billaut, 2022. "Efficient heuristics to minimize the total tardiness of chemotherapy drug production and delivery," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 785-820, September.
    17. K Katsaliaki & N Mustafee & S J E Taylor & S Brailsford, 2009. "Comparing conventional and distributed approaches to simulation in a complex supply-chain health system," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 43-51, January.
    18. Penn, Jerrod & Matopoulos, Aristides & House, Lisa, 2010. "Response to Out of Stock Produce and its Underlying Economic Considerations," 2010 Annual Meeting, February 6-9, 2010, Orlando, Florida 56487, Southern Agricultural Economics Association.
    19. Wenzhu Liao & Tong Wang, 2019. "A Novel Collaborative Optimization Model for Job Shop Production–Delivery Considering Time Window and Carbon Emission," Sustainability, MDPI, vol. 11(10), pages 1-27, May.
    20. Raut, Rakesh D. & Gardas, Bhaskar B. & Narwane, Vaibhav S. & Narkhede, Balkrishna E., 2019. "Improvement in the food losses in fruits and vegetable supply chain - a perspective of cold third-party logistics approach," Operations Research Perspectives, Elsevier, vol. 6(C).

    More about this item

    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:igg:jcini0:v:15:y:2021:i:2:p:139-157. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.