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A smart and agile dry port-seaport logistic network considering industry 5.0: A multi-stage data-driven approach

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  • Rekabi, Shabnam
  • Sazvar, Zeinab

Abstract

The advent of Industry 5.0 (I5.0) has precipitated a demand for sophisticated technological solutions across diverse sectors, with a particular emphasis on logistics. Dry Port-Seaports (DPSs), playing a pivotal role in facilitating international trade, are poised to derive substantial advantages from this revolution, while limited attention has been dedicated to this subject matter. This work is among the first studies that present a multi-stage, multi-objective scenario-based programming model to design a logistics network of DPSs, considering a combination of an Internet of Things (IoT) based infrastructure and I5.0 principles. Several real-world characteristics, including tardiness, staff fatigue, Multi-Modal Transportation (MMT), interrupted capacity, and export/import forecasting, are integrated into the proposed model, as well. For this purpose, the potential locations of Dry Ports (DPs) are evaluated based on I5.0 dimensions by applying the Stochastic Best Worst Method (SBWM). Subsequently, the selected DPs are assessed regarding the deployment of Internet of Things (IoT) infrastructure. Eventually, a mathematical model is developed to design the DPS Logistic Network (LN) optimally. To tackle the developed mathematical model, an effective solving approach, called the Stochastic Fuzzy Meta Goal Programming (SFMGP) procedure, is introduced. Among the various time-series prediction models examined, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, characterized by a lower Akaike Information Criterion (AIC), is chosen to forecast demand, as well. In order to ascertain the efficacy of the proposed framework, a real case study from commercial ports of southern Iran has been undertaken. The obtained results illustrate that the deployment IoT-based systems has significant impacts on the prevention and management of disruptions; however it may necessitate a high initial investment outlay. Moreover, the integration of IoT technology within logistics networks offers avenues for optimizing efficiency, transparency, and decision-making procedures. To illustrate, the tardiness rate is reduced by 68 % and transparency is heightened by 71 % based on the results.

Suggested Citation

  • Rekabi, Shabnam & Sazvar, Zeinab, 2025. "A smart and agile dry port-seaport logistic network considering industry 5.0: A multi-stage data-driven approach," Socio-Economic Planning Sciences, Elsevier, vol. 98(C).
    • Handle: RePEc:eee:soceps:v:98:y:2025:i:c:s0038012124003410
    DOI: 10.1016/j.seps.2024.102141
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    References listed on IDEAS

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    1. Tahereh Mohammadi & Seyed Mojtaba Sajadi & Seyed Esmaeil Najafi & Mohammadreza Taghizadeh-Yazdi, 2024. "Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies," Mathematics, MDPI, vol. 12(5), pages 1-30, February.
    2. Fatemeh Sabouhi & Mohammad Saeed Jabalameli & Armin Jabbarzadeh & Behnam Fahimnia, 2020. "A multi-cut L-shaped method for resilient and responsive supply chain network design," International Journal of Production Research, Taylor & Francis Journals, vol. 58(24), pages 7353-7381, December.
    3. Amir Jamali & Amirhossein Ranjbar & Jafar Heydari & Sina Nayeri, 2022. "A multi-objective stochastic programming model to configure a sustainable humanitarian logistics considering deprivation cost and patient severity," Annals of Operations Research, Springer, vol. 319(1), pages 1265-1300, December.
    4. Ahmet Yunus Cil & Dini Abdurahman & Ibrahim Cil, 2022. "Internet of Things enabled real time cold chain monitoring in a container port," Journal of Shipping and Trade, Springer, vol. 7(1), pages 1-26, December.
    5. Gonzalez-Aregall, Marta & Bergqvist, Rickard, 2019. "The role of dry ports in solving seaport disruptions: A Swedish case study," Journal of Transport Geography, Elsevier, vol. 80(C).
    6. Rodriguez Uria, M. Victoria & Caballero, Rafael & Ruiz, Francisco & Romero, Carlos, 2002. "Meta-goal programming," European Journal of Operational Research, Elsevier, vol. 136(2), pages 422-429, January.
    7. Romero, Carlos, 2004. "A general structure of achievement function for a goal programming model," European Journal of Operational Research, Elsevier, vol. 153(3), pages 675-686, March.
    8. Tsao, Yu-Chung & Thanh, Vo-Van, 2019. "A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 124(C), pages 13-39.
    9. Huang, Kerry & Wang, Kedi & Lee, Peter K.C. & Yeung, Andy C.L., 2023. "The impact of industry 4.0 on supply chain capability and supply chain resilience: A dynamic resource-based view," International Journal of Production Economics, Elsevier, vol. 262(C).
    10. Manoj Kumar Tiwari & Bopaya Bidanda & Joseph Geunes & Kiran Fernandes & Alexandre Dolgui, 2024. "Supply chain digitisation and management," International Journal of Production Research, Taylor & Francis Journals, vol. 62(8), pages 2918-2926, April.
    11. Nayeri, Sina & Sazvar, Zeinab & Heydari, Jafar, 2022. "A global-responsive supply chain considering sustainability and resiliency: Application in the medical devices industry," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    12. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    13. Xuejun Feng & Yan Zhang & Yuwei Li & Wei Wang, 2013. "A Location-Allocation Model for Seaport-Dry Port System Optimization," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-9, November.
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