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Multi-agent control of periodic-review supply chain

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Abstract

The bullwhip effect is one of the most important reasons for reducing the performance of the supply chain (SC) and imposes many costs on the industry. Various reasons cause the bullwhip effect (BWE), the most important of which are improper classification of orders or Periodic Reviews. This paper first proposed the state space model of a supply chain with a periodic review policy. We indicated that by categorizing and timing orders periodically with the concepts of supply chain management, we reach a three-agent structure with a T-order (cycle of orders). The interaction of the agents in this model has a spanning tree, so it was shown that the three-agent approach with T-order can efficiently send optimal periodic orders to regulate and stabilize demand fluctuations. The agents moved to an agreed inventory level by maintaining a certain distance from their inventory with other agents to adjust the BWE (an example of swarming in multi-agent control). Due to the existence of storage costs and the demand forecasting that the production agent adopts as the leader, a prohibited level of inventory, should be taken into account, and agents should avoid these levels. This problem was solved using the discussion of avoiding obstacles in multi-agent control. Finally, the efficiency of the proposed method was probed by simulating and expressing the equivalence of concepts, such as position, speed, and acceleration with variables of initial inventory, production, and production rate.

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References

  1. Carland C, Goentzel J, Montibeller G (2018) Modeling the values of private sector agents in multi-echelon humanitarian supply chains. Eur J Oper Res 269(2):532–543. https://doi.org/10.1016/j.ejor.2018.02.010

    Article  Google Scholar 

  2. Weihua L, Wanying W et al (2022) Impacts of leadership on corporate social responsibility management in multi-tier supply chains. Eur J Oper Res 299(2):483–496. https://doi.org/10.1016/j.ejor.2021.06.042

    Article  MathSciNet  Google Scholar 

  3. Song Z, Tang W, Zhao R (2019) A simple game theoretical analysis for incentivizing multi-modal transportation in freight supply chains. Eur J Oper Res 283(1):152–165. https://doi.org/10.1016/j.ejor.2021.06.042

    Article  MathSciNet  Google Scholar 

  4. Aslani Khiavi S, Hashemzadeh F, Khaloozadeh H (2021) Sensitivity analysis of the bullwhip effect in supply chains with time delay. Int J Syst Sci Oper Logistics 10(1). https://doi.org/10.1080/23302674.2021.1968064

  5. Pezhman G, Farshad G, Cathal H (2017) A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain. Eur J Oper Res 269(1):286–301. https://doi.org/10.1016/j.ejor.2017.07.014

    Article  MathSciNet  Google Scholar 

  6. Steven N (1982) Perishable inventory theory: a review. Oper Res 30(4):680–708

    Article  MathSciNet  Google Scholar 

  7. Itir K, Wolf S, Borga D (2010) Managing perishable and aging inventories: review and future research directions planning production and inventories in the extended enterprise. In: International series in operations research & management science, vol 151. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6485-4-15

  8. Alain B, Georges N, Charles T (1975) Optimum inventory and product quality control with deterministic and stochastic deterioration—an application of distributed parameters control systems. IEEE Trans Autom Control 20(3):407–412. https://doi.org/10.1109/TAC.1975.1100980

    Article  MathSciNet  Google Scholar 

  9. Dikko AM, El-Kebir B (1998) Discrete-time inventory models with deteriorating items. Int J Syst Sci 29:1007–1014. https://doi.org/10.1080/00207729808929592

    Article  Google Scholar 

  10. Ahmed A, Mahmoud AD (1998) Analysis of deteriorating inventory/production systems using a linear quadratic regulator. Eur J Oper Res 106(1):82–89. https://doi.org/10.1016/S0377-2217(98)00202-1

    Article  Google Scholar 

  11. Przemysław I, Andrzej B (2012) Linear-quadratic optimal control of periodic-review perishable inventory systems. IEEE Trans Control Syst Technol 20(5):1400–1407. https://doi.org/10.1109/TCST.2011.2161086

    Article  Google Scholar 

  12. Somayeh N, Alireza K, Mahmoud DK (2022) Agent-based modeling of milk and its productions supply chain and bullwhip effect phenomena. Int J Suppl Oper Manag 7(3):279–294. https://doi.org/10.22034/IJSOM.2020.3.6

  13. Jinglin Z, Hong Z (2018) Design of a prototype system for multi-agent supply chain information sharing benefit distribution management. IseB 18(4):581–602. https://doi.org/10.1007/s10257-018-0386-y

    Article  Google Scholar 

  14. Alexandru M, Bogdan P, Radu P, Ciprian C, Bala Z (2023) Designing a multi-agent control system for a reconfigurable manufacturing system. In: Borangiu T, Trentesaux D, Leitão P (eds) Service oriented, holonic and multi-agent manufacturing systems for industry of the future. SOHOMA 2022. Studies in computational intelligence, vol 1083. Springer, Cham. https://doi.org/10.1007/978-3-031-24291-5_34

  15. Lin FR, Tan GW, Shaw MJ (2002) Modeling supply-chain networks by a multi-agent system. In: Proceedings of the Thirty-First Hawaii International Conference on System Sciences. https://doi.org/10.1109/HICSS.1998.648302

  16. Yang N, Ding Y, Leng J, Zhang L (2022) Supply chain information collaborative simulation model integrating multi-agent and system dynamics. Promet Traffic Transport 34(5):711–724. https://doi.org/10.7307/ptt.v34i5.4092

    Article  Google Scholar 

  17. Tajima E, Ishigaki A, Takashima R, Nishida H, Okammoto T (2023) Effectiveness of multi-agent cooperation game in multi-stage supply chain. J Japan Indus Manag Assoc 73(4E):234–250 https://doi.org/10.11221/jima.73.234

  18. Roberto D, Salvatore C (2020) Insights on multi-agent systems applications for supply chain management. Sustainability 12:5. https://doi.org/10.3390/su12051935

    Article  Google Scholar 

  19. Olfati-Saber R (2006) Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Trans Autom Control 51(3):401–420. https://doi.org/10.1109/TAC.2005.864190

    Article  MathSciNet  Google Scholar 

  20. Housheng Su, Wang X, Lin Z (2007) Flocking of multi-agent systems with a virtual leader. IEEE Trans Autom Control 54(2):293–307. https://doi.org/10.1109/TAC.2008.2010897

    Article  Google Scholar 

  21. Luo X, Li S, Guan X (2010) Flocking algorithm with multi-target tracking for multi-agent systems. Pattern Recogn Lett 31(9):800–805. https://doi.org/10.1016/j.patrec.2010.01.014

    Article  Google Scholar 

  22. Aslani Khiavi S, Khaloozadeh H, Soltanian F (2019) Nonlinear modeling and performance analysis of a closed-loop supply chain in the presence of stochastic noise. Math Comput Model Dyn Syst 25(5):499–521. https://doi.org/10.1080/13873954.2019.1663876

    Article  MathSciNet  Google Scholar 

  23. Aslani Khiavi S, Khaloozadeh H, Soltanian F (2021) Suboptimal sliding manifold for nonlinear supply chain with time delay. J Comb Optim 42:151–173. https://doi.org/10.1007/s10878-021-00733-1

    Article  MathSciNet  Google Scholar 

  24. Ren W, Moore K, Chen Y (2007) High-order and model reference consensus algorithms in cooperative control of multivehicle systems. J Dyn Syst Meas Contr 11:678–688. https://doi.org/10.1115/1.2764508

    Article  Google Scholar 

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Authors and Affiliations

  1. K. N. Toosi University of Technology, Tehran, Iran

    Sajjad Aslani Khiavi, Mahdi Jafari-Nadoushan & Saeed Khankalantary

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  1. Sajjad Aslani Khiavi
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  2. Mahdi Jafari-Nadoushan
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  3. Saeed Khankalantary
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Aslani Khiavi, S., Jafari-Nadoushan, M. & Khankalantary, S. Multi-agent control of periodic-review supply chain. Prod. Eng. Res. Devel. (2024). https://doi.org/10.1007/s11740-024-01277-z

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