Inventory Consensus Control in Supply Chain Networks using Dissipativity-Based Control and Topology Co-Design

TL;DR

This paper introduces a dissipativity-based co-design method for inventory consensus in supply chain networks, improving robustness and coordination by optimizing distributed controllers and topology through convex optimization.

Contribution

It presents a novel dissipativity-based co-design approach for supply chain consensus control, enhancing scalability, robustness, and computational efficiency.

Findings

Improved consensus performance over standard control strategies.

Enhanced robustness against uncertainties and ripple effects.

Scalable and efficient convex optimization-based design.

Abstract

Recent global and local phenomena have exposed vulnerabilities in critical supply chain networks (SCNs), drawing significant attention from researchers across various fields. Typically, SCNs are viewed as static entities regularly optimized to maintain their optimal operation. However, the dynamic nature of SCNs and their associated uncertainties have motivated researchers to treat SCNs as dynamic networked systems requiring robust control techniques. In this paper, we address the SCN inventory consensus problem, which aims to synchronize multiple parallel supply chains, enhancing coordination and robustness of the overall SCN. To achieve this, we take a novel approach exploiting dissipativity theory. In particular, we propose a dissipativity-based co-design strategy for distributed consensus controllers and communication topology in SCNs. It requires only the dissipativity information of the individual supply chains and involves solving a set of convex optimization problems, thus contributing to scalability, compositionality, and computational efficiency. Moreover, it optimizes the robustness of the SCN to various associated uncertainties, mitigating both bullwhip and ripple effects. We demonstrate our contributions using numerical examples, mainly by comparing the consensus performance with respect to standard steady-state control, feedback control, and consensus control strategies.