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On Implementing Autonomous Supply Chains: a Multi-Agent System Approach

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arxiv 2310.09435 v2 pith:4SXD52HM submitted 2023-10-13 cs.MA

classification cs.MA
keywords supplysystema2scapproachautonomouschainsaddresschain
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Trade restrictions, the COVID-19 pandemic, and geopolitical conflicts have significantly exposed vulnerabilities within traditional global supply chains. These events underscore the need for organisations to establish more resilient and flexible supply chains. To address these challenges, the concept of the autonomous supply chain (ASC), characterised by predictive and self-decision-making capabilities, has recently emerged as a promising solution. However, research on ASCs is relatively limited, with no existing studies specifically focusing on their implementations. This paper aims to address this gap by presenting an implementation of ASC using a multi-agent approach. It presents a methodology for the analysis and design of such an agent-based ASC system (A2SC). This paper provides a concrete case study, the autonomous meat supply chain, which showcases the practical implementation of the A2SC system using the proposed methodology. Additionally, a system architecture and a toolkit for developing such A2SC systems are presented. Despite limitations, this work demonstrates a promising approach for implementing an effective ASC system.

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  1. Orchestrator: Active Inference for Multi-Agent Systems in Long-Horizon Tasks

    cs.MA 2025-09 conditional novelty 4.0 of 10

    Orchestrator, an active-inference-inspired feedback system for LLM multi-agent teams, substantially raises maze-solving success rates on medium-difficulty mazes but not consistently on hard mazes.

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