Integrated methodological frameworks for modelling agent-based advanced supply chain planning systems: a systematic literature review

Purpose: The objective of this paper is to provide a systematic literature review of recent developments in methodological frameworks for the modelling and simulation of agent-based advanced supply chain planning systems. Design/methodology/approach: A systematic literature review is provided to identify, select and make an analysis and a critical summary of all suitable studies in the area. It is organized into two blocks: the first one covers agent-based supply chain planning systems in general terms, while the second one specializes the previous search to identify those works explicitly containing methodological aspects. Findings: Among sixty suitable manuscripts identified in the primary literature search, only seven explicitly considered the methodological aspects. In addition, we noted that, in general, the notion of advanced supply chain planning is not considered unambiguously, that the social and individual aspects of the agent society are not taken into account in a clear manner in several studies and that a significant part of the works are of a theoretical nature, with few real-scale industrial applications. An integrated framework covering all phases of the modelling and simulation process is still lacking in the literature visited. Research limitations/implications: The main research limitations are related to the period covered (last four years), the selected scientific databases, the selected language (i.e. English) and the use of only one assessment framework for the descriptive evaluation part. Practical implications: The identification of recent works in the domain and discussion concerning their limitations can help pave the way for new and innovative researches towards a complete methodological framework for agent-based advanced supply chain planning systems. Originality/value: As there are no recent state-of-the-art reviews in the domain of methodological frameworks for agent-based supply chain planning, this paper contributes to systematizing and consolidating what has been done in recent years and uncovers interesting research gaps for future studies in this emerging fieldPeer Reviewe

From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

Agent-based negotiation and decision making for dynamic supply chain formation

Modern businesses are facing the challenge of effectively coordinating their supply chains from upstream to downstream services. It is a complex problem to search, schedule, and coordinate a set of services from a large number of service resources under various constraints and uncertainties. Existing approaches to this problem have relied on complete information regarding service requirements and resources, without adequately addressing the dynamics and uncertainties of the environments. The real-world situations are complicated as a result of ambiguity in the requirements of the services, the uncertainty of solutions from service providers, and the interdependencies among the services to be composed. This paper investigates the complexity of supply chain formation and proposes an agent-mediated coordination approach. Each agent works as a broker for each service type, dedicated to selecting solutions for each service as well as interacting with other agents in refining the decision making to achieve compatibility among the solutions. The coordination among agents concerns decision making at strategic, tactical, and operational level. At the strategic level, agents communicate and negotiate for supply chain formation; at the tactical level, argumentation is used by agents to communicate and understand the preferences and constraints of each other; at the operational level, different strategies are used for selecting the preferences. Based on this approach, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach. © 2008 Elsevier Ltd. All rights reserved.postprin

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems

Collaborative and adaptive supply chain planning

Dans le contexte industriel d'aujourd'hui, la compétitivité est fortement liée à la performance de la chaîne d'approvisionnement. En d'autres termes, il est essentiel que les unités d'affaires de la chaîne collaborent pour coordonner efficacement leurs activités de production, de façon a produire et livrer les produits à temps, à un coût raisonnable. Pour atteindre cet objectif, nous croyons qu'il est nécessaire que les entreprises adaptent leurs stratégies de planification, que nous appelons comportements, aux différentes situations auxquelles elles font face. En ayant une connaissance de l'impact de leurs comportements de planification sur la performance de la chaîne d'approvisionnement, les entreprises peuvent alors adapter leur comportement plutôt que d'utiliser toujours le même. Cette thèse de doctorat porte sur l'adaptation des comportements de planification des membres d'une même chaîne d'approvisionnement. Chaque membre pouvant choisir un comportement différent et toutes les combinaisons de ces comportements ayant potentiellement un impact sur la performance globale, il est difficile de connaître à l'avance l'ensemble des comportements à adopter pour améliorer cette performance. Il devient alors intéressant de simuler les différentes combinaisons de comportements dans différentes situations et d'évaluer les performances de chacun. Pour permettre l'utilisation de plusieurs comportements dans différentes situations, en utilisant la technologie à base d'agents, nous avons conçu un modèle d'agent à comportements multiples qui a la capacité d'adapter son comportement de planification selon la situation. Les agents planificateurs ont alors la possibilité de se coordonner de façon collaborative pour améliorer leur performance collective. En modélisant les unités d'affaires par des agents, nous avons simulé avec la plateforme de planification à base d'agents de FORAC des agents utilisant différents comportements de planification dits de réaction et de négociation. Cette plateforme, développée par le consortium de recherche FORAC de l'Université Laval, permet de simuler des décisions de planification et de planifier les opérations de la chaîne d'approvisionnement. Ces comportements de planification sont des métaheurisciques organisationnelles qui permettent aux agents de générer des plans de production différents. La simulation est basée sur un cas illustrant la chaîne d'approvisionnement de l'industrie du bois d'œuvre. Les résultats obtenus par l'utilisation de multiples comportements de réaction et de négociation montrent que les systèmes de planification avancée peuvent tirer avantage de disposer de plusieurs comportements de planification, en raIson du contexte dynamique des chaînes d'approvisionnement. La pertinence des résultats de cette thèse dépend de la prémisse que les entreprises qui adapteront leurs comportements de planification aux autres et à leur environnement auront un avantage concurrentiel important sur leurs adversaires

Negotiation-Based Capacity Planning With A Learning Mechanism Using Adaptive Neurofuzzy Inference System

In decentralized manufacturing environment with multiple factories that are scattered geographically, the complexity of production systems increases, and capacity planning and allocation of resources have become a significant concern that affects system performances. This study focuses on the development of an integrated framework to allocate limited budget in a multiple-factory environment. We develop a negotiation framework with learning mechanism to allocate autonomously finite budget provided by a headquarter and to facilitate the use of limited manufacturing resources that are scattered over individual factories. The outcome of the experiments shows good prediction of the opponent offers during negotiation, so it enables the reduction of negotiation time