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Autor
Van Roi Ho (Department of Logistics, Korea Maritime and Ocean University, Yeongdo-gu, Busan, Republic of Korea), You Sam-Sang (Korea Maritime and Ocean University, Yeongdo-gu, Busan, Republic of Korea), Anh Nguyen Duy (Ho Chi Minh City University of Technology (HCMUT)-Vietnam National University Ho Chi Minh City, Vietnam), Kim Hwan-Seong (Department of Logistics, Korea Maritime and Ocean University, Yeongdo-gu, Busan, Republic of Korea)
Tytuł
Adaptive Decision-Making Strategy for Supply Chain Systems Under Stochastic Disruptions
Źródło
LogForum, 2023, vol. 19, nr 3, s. 497-514, rys., tab., wykr., bibliogr. 17 poz.
Słowa kluczowe
Łańcuch dostaw, Podejmowanie decyzji, Procesy stochastyczne, Rentowność
Supply chain, Decision making, Stochastic processes, Profitability
Uwagi
summ.
Abstrakt
Background: Supply chain management is becoming more complex and essential with the development of the economy and globalization. Due to several interrelated and integrated logistical components, today's global supply chains are typically nonlinear dynamical systems that may show unpredictable, chaotic, or counterintuitive behaviors. In a volatile business environment, a company must integrate a decision-making strategy to achieve its strategic goals. Digitizing any business can keep up with supply chains that have become increasingly global and complex. Methods: Digital transformation has been rapidly adopted across supply chain networks. A three-echelon supply network has been formulated in discrete time domains for exploring the complex behavior of the dynamical system. The discretetime models fit more naturally to describe supply chain activities. This paper presents the adaptive management strategy to control the dynamic supply chain systems under uncertainty. The adaptive law is implemented based on the gradient descent method so that it can readily update the control gains of the decision-making strategy. The efficient management strategy helps policymakers implement a decision-support system more precisely and timely. Results: The paper aims to implement the PID controller with adaptation law in supply chain management's chaotic suppression and synchronization problems under stochastic events. Numerical simulations are presented to evaluate the validity of the proposed algorithms for the operations management of dynamic supply chain networks. The proposed adaptive control strategy provides superior performance and accuracy over classical control strategies. The decisionmaking algorithms ensuring business profitability are realized by an adaptive management strategy to cope with market disruptions. Conclusions: Disruptions like customer demand and market conditions impact on the multi-echelon supply chain system. A novel adaptive management strategy is presented to regulate uncertain supply chain systems against market disruptions. The control policy effectively utilizes chaos suppression and synchronization schemes to manage complex supply chain networks. The proposed management solutions will help logistics providers prepare for the future and gain a competitive advantage guaranteeing business resilience and sustainability against a volatile market.
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Bibliografia
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  1. Amir Parnianifard, Ali Zemouche, Muhammad Ali Imran, L. W. (2020). Robust simulation optimization of dynamic-stochastic production/inventory control system under uncertainty using computational intelligence. Uncertain Supply Chain Management, 8(4), 633-648. https://doi.org/10.5267/j.uscm.2020.9.002
  2. Anne, K. R., Chedjou, J. C., & Kyamakya, K. (2009). Bifurcation analysis and synchronisation issues in a three-echelon supply chain. International Journal of Logistics Research and Applications, 12(5), 347-362. https://doi.org/10.1080/1367556090318152 7
  3. Ardakani, E. S., Seifbarghy, M., Tikani, H., & Daneshgar, S. (2020). Designing a multi period production-distribution system considering social responsibility aspects and failure modes. Sustainable Production and Consumption, 22, 239-250. https://doi.org/https://doi.org/10.1016/j.spc. 2020.03.009
  4. Chang, W.-D., & Yan, J.-J. (2005). Adaptive robust PID controller design based on a sliding mode for uncertain chaotic systems. Chaos, Solitons & Fractals, 26(1), 167-175. https://doi.org/https://doi.org/10.1016/j.cha os.2004.12.013
  5. Cuong, T. N., Kim, H.-S., Nguyen, D. A., & You, S.-S. (2021). Nonlinear analysis and active management of production distribution in nonlinear supply chain model using sliding mode control theory. Applied Mathematical Modelling, 97, 418-437. https://doi.org/https://doi.org/10.1016/j.apm .2021.04.007
  6. Ghadimi, F., & Aouam, T. (2021). Planning capacity and safety stocks in a serial production-distribution system with multiple products. European Journal of Operational Research, 289(2), 533-552. https://doi.org/https://doi.org/10.1016/j.ejor. 2020.07.024
  7. Gholami, F., Paydar, M. M., HajiaghaeiKeshteli, M., & Cheraghalipour, A. (2019). A multi-objective robust supply chain design considering reliability. Journal of Industrial and Production Engineering, 36(6), 385- 400. https://doi.org/10.1080/21681015.2019.165 8136
  8. Govindan, K., & Cheng, T. C. E. (2018). Advances in stochastic programming and robust optimization for supply chain planning. Computers & Operations Research, 100, 262-269. https://doi.org/https://doi.org/10.1016/j.cor. 2018.07.027
  9. Ivanov, D. (2022). Viable supply chain model: integrating agility, resilience and sustainability perspectives-lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research, 319(1), 1411-1431. https://doi.org/10.1007/s10479-020-03640- 6
  10. Kocamaz, U. E., Taşkın, H., Uyaroğlu, Y., & Göksu, A. (2016). Control and synchronization of chaotic supply chains using intelligent approaches. Computers & Industrial Engineering, 102, 476-487. https://doi.org/https://doi.org/10.1016/j.cie. 2016.03.014
  11. Kocamaz, U. E., & Uyaroğlu, Y. (2014). Controlling Rucklidge chaotic system with a single controller using linear feedback and passive control methods. Nonlinear Dynamics, 75(1), 63-72. https://doi.org/10.1007/s11071-013-1049-7
  12. Lei, Z., Li, Y., & Xu, Y. (2006). Chaos Synchronization of Bullwhip Effect in a Supply Chain. 2006 International Conference on Management Science and Engineering, 557-560. https://doi.org/10.1109/ICMSE.2006.31395 5
  13. Sarimveis, H., Patrinos, P., Tarantilis, C. D., & Kiranoudis, C. T. (2008). Dynamic modeling and control of supply chain systems: A review. Computers & Operations Research, 35(11), 3530-3561. https://doi.org/https://doi.org/10.1016/j.cor. 2007.01.017
  14. Tempelmeier, H. (2006). Supply chain inventory optimization with two customer classes in discrete time. European Journal of Operational Research, 174(1), 600-621. https://doi.org/https://doi.org/10.1016/j.ejor. 2005.01.044
  15. Tsai, C.-C., Tai, F.-C., Chang, Y.-L., & Tsai, C.-T. (2017). Adaptive Predictive PID Control Using Fuzzy Wavelet Neural Networks for Nonlinear Discrete-Time Time-Delay Systems. International Journal of Fuzzy Systems, 19(6), 1718-1730. https://doi.org/10.1007/s40815-017-0405-z
  16. Xu, X., Lee, S.-D., Kim, H.-S., & You, S.-S. (2021). Management and optimisation of chaotic supply chain system using adaptive sliding mode control algorithm. International Journal of Production Research, 59(9), 2571-2587. https://doi.org/10.1080/00207543.2020.173 5662
  17. Zhang, S., & Cui, Y. (2021). Research on Robust Financing Strategy of Uncertain Supply Chain System Based on Working Capital. IEEE Transactions on Fuzzy Systems, 29(9), 2593-2602. https://doi.org/10.1109/TFUZZ.2020.30039 87
Cytowane przez
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ISSN
1895-2038
Język
eng
URI / DOI
http://dx.doi.org/10.17270/J.LOG.2023.866
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