A multiagent systems approach for managing supply-chain problems: a learning perspective

Carvalho, Rui; Custódio, Luís

doi:10.1109/kimas.2005.1427124

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…In this context, software agents in SCM generally embed one or more techniques from SC Optimization and SC Simulation to support operations planning or simulation. However, agents usually go further by also embedding negotiation protocols (Forget, D'Amours, Frayret & Gaudreault, 2008;Dudek & Stadtler, 2005) or learning algorithms (Carvalho & Custodio, 2005;Emerson & Piramuthu, 2004) to address other issues, such as coordination in distributed and complex contexts.…”

Section: Multi-agent Systems For Supply Chain Planningmentioning

confidence: 99%

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

Santa-Eulalia

Halladjian

D’Amours

et al. 2011

JIEM

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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 field.

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Section: Multi-agent Systems For Supply Chain Planningmentioning

confidence: 99%

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

Santa-Eulalia

Halladjian

D’Amours

et al. 2011

JIEM

View full text Add to dashboard Cite

show abstract

“…The multi‐agent simulation supports predefined structures in which real supply systems are modelled, proactive behaviour simulations, distributed computing and dynamic simulation scenario (Carvalho and Custodio, 2005). In a multi‐agent simulation, there is a high degree of equivalence between the simulation entities and the real SC entities.…”

Section: Mass and Sc Managementmentioning

confidence: 99%

Supply chain modelling using a multi‐agent system

Pino

Fernández

García

et al. 2010

Journal of Advances in Management Research

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PurposeThe purpose of this paper is to focus on a supply chain (SC) simulation of all its management processes by means of a multi‐agent system (MAS).Design/methodology/approachNowadays, the company must develop its activity in an environment characterized by: globalization, hard competitiveness, the necessity of flexibility and of answering dynamically to a changing demand. Thus, a distributed, autonomous approach, strong enough to face changes is necessary, which is what MASs contribute to. An agent can represent each of the components that form the SC. Then the resulting agent system will own similar characteristics to the ones in the studied SC: autonomy, social abilities, reactivity, pro‐activity.FindingsWhen analysing the demand for each SC member (from manufacturer to final consumer), one can observe that while consumer demand is a relatively stable feature, the upper link in the chain (the manufacturer), presents a very pronounced variability. This is known as the “bullwhip effect” or “forrester effect” and is mainly due to the fact that the SC members' strategies are not considered as a whole but as a sum of individual strategies. In the proposed system, each agent will be communicated with other “agents” who will be the only responsible for making forecasts based on information provided to it by all components of the chain. The ultimate goal is for each SC echelon to satisfy its own objectives, while at the same time meet the local and external constraints.Research limitations/implicationsIn this work a standard SC is proposed (one manufacturer – one distributor – one wholesaler – one retailer) although it could easily be modified to incorporate a bigger number of members in each echelon within the SC.Originality/valueThe paper shows the benefits of using artificial intelligence in the SC management.

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A multiagent systems approach for managing supply-chain problems: a learning perspective

Cited by 2 publications

References 10 publications

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

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

Supply chain modelling using a multi‐agent system

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