IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2016i3p250-d65341.html
   My bibliography  Save this article

Weighing Efficiency-Robustness in Supply Chain Disruption by Multi-Objective Firefly Algorithm

Author

Listed:
  • Tong Shu

    (Business School, Hunan University, Changsha 410082, China)

  • Xiaoqin Gao

    (Business School, Hunan University, Changsha 410082, China)

  • Shou Chen

    (Business School, Hunan University, Changsha 410082, China)

  • Shouyang Wang

    (Business School, Hunan University, Changsha 410082, China
    Academy of Mathematics and System Science, Chinese Academy of Sciences, Beijing 100190, China)

  • Kin Keung Lai

    (International Business School, Shaanxi Normal University, Xi’an 710062, China
    Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue Kowloon, Hong Kong, China)

  • Lu Gan

    (Office of Humanities and Social Sciences, Hunan University, Changsha 410082, China)

Abstract

This paper investigates various supply chain disruptions in terms of scenario planning, including node disruption and chain disruption; namely, disruptions in distribution centers and disruptions between manufacturing centers and distribution centers. Meanwhile, it also focuses on the simultaneous disruption on one node or a number of nodes, simultaneous disruption in one chain or a number of chains and the corresponding mathematical models and exemplification in relation to numerous manufacturing centers and diverse products. Robustness of the design of the supply chain network is examined by weighing efficiency against robustness during supply chain disruptions. Efficiency is represented by operating cost; robustness is indicated by the expected disruption cost and the weighing issue is calculated by the multi-objective firefly algorithm for consistency in the results. It has been shown that the total cost achieved by the optimal target function is lower than that at the most effective time of supply chains. In other words, the decrease of expected disruption cost by improving robustness in supply chains is greater than the increase of operating cost by reducing efficiency, thus leading to cost advantage. Consequently, by approximating the Pareto Front Chart of weighing between efficiency and robustness, enterprises can choose appropriate efficiency and robustness for their longer-term development.

Suggested Citation

  • Tong Shu & Xiaoqin Gao & Shou Chen & Shouyang Wang & Kin Keung Lai & Lu Gan, 2016. "Weighing Efficiency-Robustness in Supply Chain Disruption by Multi-Objective Firefly Algorithm," Sustainability, MDPI, vol. 8(3), pages 1-27, March.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:3:p:250-:d:65341
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/3/250/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/8/3/250/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mizgier, Kamil J. & Hora, Manpreet & Wagner, Stephan M. & Jüttner, Matthias P., 2015. "Managing operational disruptions through capital adequacy and process improvement," European Journal of Operational Research, Elsevier, vol. 245(1), pages 320-332.
    2. Wang, Weijia & Plante, Robert D. & Tang, Jen, 2013. "Minimum cost allocation of quality improvement targets under supplier process disruption," European Journal of Operational Research, Elsevier, vol. 228(2), pages 388-396.
    3. Mizgier, Kamil J. & Wagner, Stephan M. & Jüttner, Matthias P., 2015. "Disentangling diversification in supply chain networks," International Journal of Production Economics, Elsevier, vol. 162(C), pages 115-124.
    4. Peng, Peng & Snyder, Lawrence V. & Lim, Andrew & Liu, Zuli, 2011. "Reliable logistics networks design with facility disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1190-1211, September.
    5. Hishamuddin, Hawa & Sarker, Ruhul A. & Essam, Daryl, 2014. "A recovery mechanism for a two echelon supply chain system under supply disruption," Economic Modelling, Elsevier, vol. 38(C), pages 555-563.
    6. R. S. Burachik & C. Y. Kaya & M. M. Rizvi, 2014. "A New Scalarization Technique to Approximate Pareto Fronts of Problems with Disconnected Feasible Sets," Journal of Optimization Theory and Applications, Springer, vol. 162(2), pages 428-446, August.
    7. Cao, Erbao & Wan, Can & Lai, Mingyong, 2013. "Coordination of a supply chain with one manufacturer and multiple competing retailers under simultaneous demand and cost disruptions," International Journal of Production Economics, Elsevier, vol. 141(1), pages 425-433.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiao, Liye & Shao, Wei & Wang, Chen & Zhang, Kequan & Lu, Haiyan, 2016. "Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting," Applied Energy, Elsevier, vol. 180(C), pages 213-233.
    2. Jiguang Wang & Yucai Wu, 2018. "An Improved Voronoi-Diagram-Based Algorithm for Continuous Facility Location Problem under Disruptions," Sustainability, MDPI, vol. 10(9), pages 1-13, August.
    3. Sinha, Priyank & Kumar, Sameer & Prakash, Surya, 2020. "Measuring and mitigating the effects of cost disturbance propagation in multi-echelon apparel supply chains," European Journal of Operational Research, Elsevier, vol. 282(1), pages 148-160.
    4. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry & Zennaro, Ilenia, 2021. "Costs of resilience and disruptions in supply chain network design models: A review and future research directions," International Journal of Production Economics, Elsevier, vol. 235(C).

    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. Hasani, Aliakbar & Khosrojerdi, Amirhossein, 2016. "Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 20-52.
    2. Li, Bo & Arreola-Risa, Antonio, 2017. "Financial risk, inventory decision and process improvement for a firm with random capacity," European Journal of Operational Research, Elsevier, vol. 260(1), pages 183-194.
    3. Moon, Ilkyeong & Feng, Xuehao, 2017. "Supply chain coordination with a single supplier and multiple retailers considering customer arrival times and route selection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 78-97.
    4. Jie Wu & Zhixin Chen & Xiang Ji, 2020. "Sustainable trade promotion decisions under demand disruption in manufacturer-retailer supply chains," Annals of Operations Research, Springer, vol. 290(1), pages 115-143, July.
    5. Weijun Xie & Yanfeng Ouyang & Sze Chun Wong, 2016. "Reliable Location-Routing Design Under Probabilistic Facility Disruptions," Transportation Science, INFORMS, vol. 50(3), pages 1128-1138, August.
    6. Maiyar, Lohithaksha M. & Thakkar, Jitesh J., 2019. "Modelling and analysis of intermodal food grain transportation under hub disruption towards sustainability," International Journal of Production Economics, Elsevier, vol. 217(C), pages 281-297.
    7. Davood Shishebori & Lawrence Snyder & Mohammad Jabalameli, 2014. "A Reliable Budget-Constrained FL/ND Problem with Unreliable Facilities," Networks and Spatial Economics, Springer, vol. 14(3), pages 549-580, December.
    8. Ivanov, Dmitry & Sokolov, Boris, 2013. "Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty," European Journal of Operational Research, Elsevier, vol. 224(2), pages 313-323.
    9. Albareda-Sambola, Maria & Hinojosa, Yolanda & Puerto, Justo, 2015. "The reliable p-median problem with at-facility service," European Journal of Operational Research, Elsevier, vol. 245(3), pages 656-666.
    10. Ajjima Jiravichai & Ruth Banomyong, 2022. "A Proposed Methodology for Literature Review on Operational Risk Management in Banks," Risks, MDPI, vol. 10(5), pages 1-18, May.
    11. Li, Qingwei & Savachkin, Alex, 2013. "A heuristic approach to the design of fortified distribution networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 50(C), pages 138-148.
    12. Azad, Nader & Hassini, Elkafi, 2019. "Recovery strategies from major supply disruptions in single and multiple sourcing networks," European Journal of Operational Research, Elsevier, vol. 275(2), pages 481-501.
    13. Yun Hui Lin & Yuan Wang & Loo Hay Lee & Ek Peng Chew, 2021. "Robust facility location with structural complexity and demand uncertainty," Flexible Services and Manufacturing Journal, Springer, vol. 33(2), pages 485-507, June.
    14. Zhizhu Lai & Qun Yue & Zheng Wang & Dongmei Ge & Yulong Chen & Zhihong Zhou, 2022. "The min-p robust optimization approach for facility location problem under uncertainty," Journal of Combinatorial Optimization, Springer, vol. 44(2), pages 1134-1160, September.
    15. Donya Rahmani, 2019. "Designing a robust and dynamic network for the emergency blood supply chain with the risk of disruptions," Annals of Operations Research, Springer, vol. 283(1), pages 613-641, December.
    16. An, Shi & Cui, Na & Li, Xiaopeng & Ouyang, Yanfeng, 2013. "Location planning for transit-based evacuation under the risk of service disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 54(C), pages 1-16.
    17. Jeong, Yoonjea & Kim, Gwang, 2023. "Reliable design of container shipping network with foldable container facility disruption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    18. Kamil J. Mizgier & Joseph M. Pasia & Srinivas Talluri, 2017. "Multiobjective capital allocation for supplier development under risk," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5243-5258, September.
    19. Zhang, Yanzi & Diabat, Ali & Zhang, Zhi-Hai, 2021. "Reliable closed-loop supply chain design problem under facility-type-dependent probabilistic disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 180-209.
    20. Jabbarzadeh, Armin & Fahimnia, Behnam & Sheu, Jiuh-Biing & Moghadam, Hani Shahmoradi, 2016. "Designing a supply chain resilient to major disruptions and supply/demand interruptions," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 121-149.

    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:8:y:2016:i:3:p:250-:d:65341. 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.