Robust controlling of chaotic behavior in supply chain networks

Nav, H. Norouzi; Motlagh, Mohammad Reza Jahed; Makui, Ahmad

doi:10.1057/s41274-016-0112-4

Cited by 6 publications

(1 citation statement)

References 54 publications

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“…Often the management of supply chains is performed with decentralized decision taking procedures which can be conflicting. On the other side, centralized control schemes can ensure stability of supply chains, synchronization and optimized inventory management [21][22]. Among various control approaches for supply chain networks one can distinguish model-free control, model predictive control and sliding-mode control [23][24].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear optimal control for supply chain networks under time-delays

Rigatos,

Siani,

Al-Numay

et al. 2024

Preprint

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The article proposes a nonlinear optimal control method for treating the control and stabilization problem of supply chain networks and inventories under time-delays. The state-space model of the supply chain network is considered to have as state variables the customer's demand and the inventories of the manufacturer, of the retailers and of the distributors. The control inputs of the model are the manufacturer's production and the retailer's ordering quantities. The model is subject to time-delays. It is proven that the dynamic model of the supply chain is differentially flat. This model undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the supply chain a stabilizing optimal (H-infinity) feedback controller is designed. This controller stands for the solution to the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller's feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The method achieves fast and accurate tracking of the targeted setpoints by the state variables of the supply chain system under moderate variations of the control inputs.

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Section: Introductionmentioning

confidence: 99%

Nonlinear optimal control for supply chain networks under time-delays

Rigatos,

Siani,

Al-Numay

et al. 2024

Preprint

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A Gravitation-Based Hierarchical Community Detection Algorithm for Structuring Supply Chain Network

Dong

2023

Int J Comput Intell Syst

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As industrial production outsourcing expands, the collaboration relationship of firms evolves to be more entangled, which means that the enterprise communities in the supply chain network become increasingly overlapping and their boundaries are ambiguous. Given the network complexity, deeper insight into the sequencing orders of suppliers and assemblers is required to orchestrate the supply chain partner collaboration. Considering the overlapping community and multi-layered connectivity characteristics of the supply chain network, in this paper, we design a gravitation-based hierarchical community detection algorithm for structuring the supply chain network. The solution applies a functional modules identification strategy based on node gravitation and a hierarchical clustering strategy based on module gravitation to structure the supply chain network architecture. The key technique is to investigate the global gravitational influence of focal firms, segment the functional modules by characterizing the overlapping conditions among communities, and construct the dendrogram by measuring the gravitational forces between modules in order to map the hierarchical architecture of the dendrogram to structure the supply chain network. The proposed algorithm does not necessitate a prior knowledge about the network. It is adaptable to construct the supply chain network that exhibits scale-free, highly overlapped modular community, and hierarchical characteristics. Experimental results on synthetic benchmark and real-world networks demonstrate the effectiveness and applicability of the proposed algorithm.

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