IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p14930-d969933.html
   My bibliography  Save this article

Design a Robust Logistics Network with an Artificial Physarum Swarm Algorithm

Author

Listed:
  • Zhengying Cai

    (College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Yuanyuan Yang

    (College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Xiangling Zhang

    (College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Yan Zhou

    (College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

Abstract

The robust optimization of logistics networks can improve the ability to provide sustainable service and business sustainability after uncertain disruptions. The existing works on the robust design of logistics networks insisted that it is very difficult to build a robust network topology, and this kind of optimization problem is an NP-hard problem that cannot be easily solved. In nature, Physarum often needs to build a robust and efficient topological network to complete the foraging process. Recently, some researchers used Physarum to build a robust transportation network in professional biological laboratories and received a good performance. Inspired by the foraging behavior of natural Physarum, we proposed a novel artificial Physarum swarm system to optimize the logistics network robustness just on a personal computer. In our study, first, the robustness optimization problem of a logistics network is described as a topology optimization model based on graph theory, and four robustness indicators are proposed to build a multi-objective robustness function of logistics network topology, including the relative robustness, the betweenness robustness, the edge robustness and the closeness robustness. Second, an artificial Physarum swarm system is developed to simulate the foraging behavior of a natural Physarum swarm to solve this kind of complex robust optimization problem. The proposed artificial Physarum swarm system can search for optimal solutions by expansion and contraction operations and the exchange of information with each other through a self-learning experience and neighbor-learning experiences. The plasmodium of Physarum forms the edges, and the external food sources simulate the logistics nodes. Third, an experimental example is designed on the basis of Mexico City to verify the proposed method, and the results reveal that the artificial Physarum swarm system can help us effectively improve the logistics network robustness under disruptions and receive a better performance than natural Physarum. The article may be helpful for both theory and practice to explore the robust optimization in logistics operation and provide engineers with an opportunity to resist logistics disruptions and risk loss by a novel artificial intelligence tool.

Suggested Citation

  • Zhengying Cai & Yuanyuan Yang & Xiangling Zhang & Yan Zhou, 2022. "Design a Robust Logistics Network with an Artificial Physarum Swarm Algorithm," Sustainability, MDPI, vol. 14(22), pages 1-24, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14930-:d:969933
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/14930/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/22/14930/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bowei Xu & Junjun Li & Yongsheng Yang & Octavian Postolache & Huafeng Wu, 2018. "Robust modeling and planning of radio-frequency identification network in logistics under uncertainties," International Journal of Distributed Sensor Networks, , vol. 14(4), pages 15501477187, April.
    2. Cheng, Chun & Qi, Mingyao & Zhang, Ying & Rousseau, Louis-Martin, 2018. "A two-stage robust approach for the reliable logistics network design problem," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 185-202.
    3. Tosarkani, Babak Mohamadpour & Amin, Saman Hassanzadeh & Zolfagharinia, Hossein, 2020. "A scenario-based robust possibilistic model for a multi-objective electronic reverse logistics network," International Journal of Production Economics, Elsevier, vol. 224(C).
    4. Maryam Philsoophian & Peyman Akhavan & Morteza Abbasi, 2021. "Strategic Alliance for Resilience in Supply Chain: A Bibliometric Analysis," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    5. Sun, Huali & Li, Jiamei & Wang, Tingsong & Xue, Yaofeng, 2022. "A novel scenario-based robust bi-objective optimization model for humanitarian logistics network under risk of disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    6. Chu Li & Jinming Yan & Ze Xu, 2021. "How Does New-Type Urbanization Affect the Subjective Well-Being of Urban and Rural Residents? Evidence from 28 Provinces of China," Sustainability, MDPI, vol. 13(23), pages 1-17, November.
    7. Chia-Nan Wang & Ngoc-Ai-Thy Nguyen & Thanh-Tuan Dang & Chen-Ming Lu, 2021. "A Compromised Decision-Making Approach to Third-Party Logistics Selection in Sustainable Supply Chain Using Fuzzy AHP and Fuzzy VIKOR Methods," Mathematics, MDPI, vol. 9(8), pages 1-27, April.
    8. Kulkarni, Onkar & Dahan, Mathieu & Montreuil, Benoit, 2022. "Resilient Hyperconnected Parcel Delivery Network Design Under Disruption Risks," International Journal of Production Economics, Elsevier, vol. 251(C).
    9. Gong, Hailei & Zhang, Zhi-Hai, 2022. "Benders decomposition for the distributionally robust optimization of pricing and reverse logistics network design in remanufacturing systems," European Journal of Operational Research, Elsevier, vol. 297(2), pages 496-510.
    10. Zarghami, Seyed Ashkan & Dumrak, Jantanee, 2021. "Unearthing vulnerability of supply provision in logistics networks to the black swan events: Applications of entropy theory and network analysis," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    11. Islem Snoussi & Nadia Hamani & Nassim Mrabti & Lyes Kermad, 2021. "A Robust Mixed-Integer Linear Programming Model for Sustainable Collaborative Distribution," Mathematics, MDPI, vol. 9(18), pages 1-27, September.
    12. Tavakoli Kafiabad, Shayan & Zanjani, Masoumeh Kazemi & Nourelfath, Mustapha, 2022. "Robust collaborative maintenance logistics network design and planning," International Journal of Production Economics, Elsevier, vol. 244(C).
    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. Lin Chen & Ting Dong & Jin Peng & Dan Ralescu, 2023. "Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review," Mathematics, MDPI, vol. 11(11), pages 1-45, May.
    2. Deng, Menghua & Bian, Bomin & Zhou, Yanlin & Ding, Jianpeng, 2023. "Distributionally robust production and replenishment problem for hydrogen supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    3. Alikhani, Reza & Eskandarpour, Majid & Jahani, Hamed, 2023. "Collaborative distribution network design with surging demand and facility disruptions," International Journal of Production Economics, Elsevier, vol. 262(C).
    4. Wen, Tao & Gao, Qiuya & Chen, Yu-wang & Cheong, Kang Hao, 2022. "Exploring the vulnerability of transportation networks by entropy: A case study of Asia–Europe maritime transportation network," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Alaa Alden Al Mohamed & Sobhi Al Mohamed, 2023. "Application of fuzzy group decision-making selecting green supplier: a case study of the manufacture of natural laurel soap," Future Business Journal, Springer, vol. 9(1), pages 1-20, December.
    6. Ebrahimi Bajgani, Sahar & Saberi, Sara & Toyasaki, Fuminori, 2023. "Designing a reverse supply chain network with quality control for returned products: Strategies to mitigate free-riding effect and ensure compliance with technology licensing requirements," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    7. Chia-Nan Wang & Ngoc-Ai-Thy Nguyen & Thanh-Tuan Dang, 2023. "Sustainable Evaluation of Major Third-Party Logistics Providers: A Framework of an MCDM-Based Entropy Objective Weighting Method," Mathematics, MDPI, vol. 11(19), pages 1-27, October.
    8. Shuwan Zhu & Wenjuan Fan & Xueping Li & Shanlin Yang, 2023. "Ambulance dispatching and operating room scheduling considering reusable resources in mass-casualty incidents," Operational Research, Springer, vol. 23(2), pages 1-37, June.
    9. Miguel Reyna-Castillo & Alejandro Santiago & Salvador Ibarra Martínez & José Antonio Castán Rocha, 2022. "Social Sustainability and Resilience in Supply Chains of Latin America on COVID-19 Times: Classification Using Evolutionary Fuzzy Knowledge," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
    10. Cheng, Chun & Adulyasak, Yossiri & Rousseau, Louis-Martin, 2021. "Robust facility location under demand uncertainty and facility disruptions," Omega, Elsevier, vol. 103(C).
    11. Asier Baquero, 2022. "Net Promoter Score (NPS) and Customer Satisfaction: Relationship and Efficient Management," Sustainability, MDPI, vol. 14(4), pages 1-19, February.
    12. Mrabti, Nassim & Hamani, Nadia & Boulaksil, Youssef & Amine Gargouri, Mohamed & Delahoche, Laurent, 2022. "A multi-objective optimization model for the problems of sustainable collaborative hub location and cost sharing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    13. Hisatoshi Naganawa & Enna Hirata & Nailah Firdausiyah & Russell G. Thompson, 2024. "Logistics Hub and Route Optimization in the Physical Internet Paradigm," Logistics, MDPI, vol. 8(2), pages 1-18, April.
    14. Wang, Shuliang & Chen, Chen & Zhang, Jianhua & Gu, Xifeng & Huang, Xiaodi, 2022. "Vulnerability assessment of urban road traffic systems based on traffic flow," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    15. Yu, Wuyang, 2023. "A robust model for emergency supplies prepositioning and transportation considering road disruptions," Operations Research Perspectives, Elsevier, vol. 10(C).
    16. Nassim Mrabti & Nadia Hamani & Laurent Delahoche, 2022. "A Comprehensive Literature Review on Sustainable Horizontal Collaboration," Sustainability, MDPI, vol. 14(18), pages 1-38, September.
    17. Yang, Yongjian & Yin, Yunqiang & Wang, Dujuan & Ignatius, Joshua & Cheng, T.C.E. & Dhamotharan, Lalitha, 2023. "Distributionally robust multi-period location-allocation with multiple resources and capacity levels in humanitarian logistics," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1042-1062.
    18. Deng, Jian, 2022. "Probabilistic characterization of soil properties based on the maximum entropy method from fractional moments: Model development, case study, and application," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    19. Jun Zhang & Jinchen Xie & Xinyi Zhang & Jianke Yang, 2022. "Income, Social Capital, and Subjective Well-Being of Residents in Western China," Sustainability, MDPI, vol. 14(15), pages 1-11, July.
    20. Qi, Mingyao & Yang, Ying & Cheng, Chun, 2023. "Location and inventory pre-positioning problem under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).

    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:gam:jsusta:v:14:y:2022:i:22:p:14930-:d:969933. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.