[Paper Review] Multi-Layers Supply chain modelling based on Multi-Agent Approach

Samia Chehbi Gamoura, Yacine Ouzrout+1 more|arXiv (Cornell University)|Oct 31, 2018

Supply Chain and Inventory Management6 references3 citations

TL;DR

This paper proposes a multi-layered supply chain model using a multi-agent systems approach to optimize strategic design and coordination among suppliers, producers, and distribution centers. It formulates agent behaviors and inter-relationships in a layered architecture, demonstrating a generic, scalable framework for complex supply chain simulation and decision support.

ABSTRACT

This paper proposes a strategic multi layers model based on multi agents approach for supply chain system. It introduces a formulation and a solution methodology for the problem of supply chain design and modeling. In this paper we describe and analyze the relationships among main entities of a supply chain, such as suppliers, producers, and distribution centers, in the aim to design the agents and define their behavior. We also study, how these relationships can be formulated in a multi layer model. Finally, a generic multi agent model is illustrated.

Motivation & Objective

• To address the complexity of strategic supply chain design by modeling interdependencies among suppliers, producers, and distribution centers.
• To develop a formalized multi-agent framework that captures the dynamic behaviors and interactions of supply chain entities.
• To propose a layered architecture that enables modular modeling of supply chain components for better scalability and analysis.
• To provide a generic, reusable model applicable to diverse supply chain configurations and operational scenarios.
• To support decision-making in supply chain design through agent-based simulation and behavior specification.

Proposed method

• The supply chain is decomposed into distinct layers representing functional entities: suppliers, producers, and distribution centers.
• Each entity is modeled as an autonomous agent with defined roles, goals, and decision-making capabilities.
• Agent behaviors are formalized based on strategic objectives such as cost minimization, delivery time optimization, and resource allocation.
• Inter-agent relationships are modeled using message-passing mechanisms to represent information and material flows across layers.
• The multi-layer architecture integrates agents through well-defined interfaces, enabling hierarchical coordination and modularity.
• A generic multi-agent model is implemented and illustrated using a case study to demonstrate feasibility and scalability.

Experimental results

Research questions

• RQ1 How can a multi-agent system effectively model the strategic interactions among suppliers, producers, and distribution centers in a supply chain?
• RQ2 What formalism can be used to define agent behaviors and inter-relationships in a layered supply chain architecture?
• RQ3 How does the multi-layered structure improve modularity, scalability, and analysis of supply chain systems?
• RQ4 What are the key design principles for a generic multi-agent model applicable to diverse supply chain configurations?
• RQ5 How can agent-based modeling support strategic decision-making in complex, multi-tiered supply networks?

Key findings

• The proposed multi-agent model successfully captures the strategic interactions among supply chain entities through formalized agent behaviors.
• The multi-layered architecture enables modular representation of supply chain components, improving system scalability and maintainability.
• Agent communication protocols effectively model information and material flows between suppliers, producers, and distribution centers.
• The framework demonstrates feasibility through a generic illustration, showing applicability to real-world supply chain design problems.
• The model supports strategic decision-making by simulating dynamic behaviors and interdependencies across supply chain tiers.
• The approach provides a foundation for future extensions to include uncertainty, optimization, and real-time coordination in supply chains.