Adaptive control of criticality infrastructure in automatic closed-loop supply chain considering uncertainty

Saraeian, Shideh; Shirazi, Babak; Motameni, Homayun

doi:10.1016/j.ijcip.2019.02.004

Cited by 6 publications

(2 citation statements)

References 64 publications

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“…Not only that research, but all the above-mentioned research works ignore the uncertainty in supplying materials and product demand (Zeballos, Méndez, Barbosa-Povoa, & Novais, 2014), the number of returned products (Salema, Barbosa-Povoa, & Novais, 2007), exchange rates (S. Li & Wang, 2010), economic instability (Kwak, Rodrigues, Mason, Pettit, & Beresford, 2018), political instability (Ras & Vermeulen, 2009), and changes in the regulatory environment (Sink & Langley Jr, 1997). However, uncertainty is more crucial in the context of CLSC (Saraeian, Shirazi, & Motameni, 2019). Accordingly, Goh, Lim, and Meng (2007) presented a multiechelon stochastic model to maximize a GSCs profit, while minimizing the risk of uncertainty in supply, demand, exchange, and disruption.…”

Section: Literature Reviewmentioning

confidence: 99%

Closing the loop of a global supply chain through a robust optimal decentralized decision support system

Golpîra

2022

Environ Sci Pollut Res

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This paper proposes an original risk-based robust mixed-integer linear programming to global closed-loop supply chain network design based on a novel, uncertain, bi-level programming model. The results show that using the proposed approach significantly improves the solution robustness so that the standard deviation of profit, income, and cost is reduced by about 28%, 34%, and 36% on average. However, a more conservative DM, compared to an optimistic DM, faces more income cuts to achieve greater robustness. As far as the managerial implications are concerned, this research can assist the DM in identifying essential trade-offs among risk, cost, profit, income, robustness, and leanness regarding its attitude toward risk. Because a similar change in the level of risk-taking of the DM does not necessarily have the same economic consequences for pessimistic and optimistic DMs. In addition, the proposed decentralized structure dramatically improves the leanness of the network, especially for a pessimistic DM.

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Section: Literature Reviewmentioning

confidence: 99%

Closing the loop of a global supply chain through a robust optimal decentralized decision support system

Golpîra

2022

Environ Sci Pollut Res

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“…A PI controller based on immune algorithm is designed to reduce the impact of time delay on NCS performance, and Smith predictor is used to compensate for random time delay to further reduce its impact on system performance. is method makes the PID controller have the ability to predict the network delay, and the PID parameters are adjusted online according to the predicted output error at the future time, which greatly improves the control performance of the system and has strong engineering significance [24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

Computer Network Dynamic Balance Flow Distribution Based on Closed-Loop Particle Swarm Feedback Model

Zhou¹

2021

Mathematical Problems in Engineering

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Based on the closed-loop particle swarm feedback model, this paper proposes a graphical method to analyze the stability of the computer network dynamic balance system. First, based on the second-order time delay system model of congestion control, the stability of the system is described by characteristic pseudopolynomials. Secondly, based on the inverse line, the stability of the system is verified by graphical analysis methods, and the PID controller parameter range that guarantees the stability of the system is obtained, and the relationship between the controller proportional gain boundary and the network characteristic parameters is analyzed. Then, based on the analysis of the basic particle swarm optimization algorithm, the particle swarm evolution formula is divided into two parts, its own factors and social factors, and the influence of each part on the evolution speed and position of the particle swarm is analyzed, and an improved particle swarm is proposed. Finally, according to the above analysis, we find the corresponding equation from the appropriate solution in turn, thereby designing a class of particle swarm optimization algorithm with fewer intermediate variables. In view of the system involved in the classical PID control parameter tuning method, the improved particle swarm algorithm is applied to the parameter tuning and optimization of the PID controller. During the experiment, the improved PSO-PID controller optimization algorithm was used in the random early detection algorithm of active queue management, the process of the improved algorithm was researched and designed, and the relevant performance of the improved algorithm was verified through simulation experiments.

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Optimal autonomous architecture for uncertain processes management

Saraeian

Shirazi

Motameni

2019

Information Sciences

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Adaptive control of criticality infrastructure in automatic closed-loop supply chain considering uncertainty

Cited by 6 publications

References 64 publications

Closing the loop of a global supply chain through a robust optimal decentralized decision support system

Closing the loop of a global supply chain through a robust optimal decentralized decision support system

Computer Network Dynamic Balance Flow Distribution Based on Closed-Loop Particle Swarm Feedback Model

Optimal autonomous architecture for uncertain processes management

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