Adaptive Planning for Supply Chain Networks

Andreev, Michael; Rzevski, George; Skobelev, Petr; Shveykin, Peter; Tsarev, Alexander; Tugashev, Andrew

doi:10.1007/978-3-540-74481-8_21

Cited by 14 publications

(7 citation statements)

References 3 publications

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“…Contributions in the domain cover dissimilar topics. For example, several papers propose agent-based architectures (Frayret et al, 2007;Andreev et al, 2007;Feng et al, 2007;Monteiro et al, 2007;Venkatadri & Kiralp, 2007), some deal with the famous TAC -Trade Agent Competition (Andrews et al, 2007;Si, Edmond, Dumas & Chong, 2007;Benisch et al, 2009), certain approaches propose coordination and information-sharing mechanisms (Lee & Kumara, 2007), others focus on mathematical models for agents (Gaudreault et al, 2009), a number use an agent-based environment only as a testbed to test SC strategies (Cid-Yanez et al, 2009;Santa-Eulalia et al, 2009b), and finally some propose agent-based methodological frameworks (e.g. Karam et al, 2010;Santa-Eulalia et al, 2010;Labarthe et al, 2007).…”

Section: Main Contributionsmentioning

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: Main Contributionsmentioning

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

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“…The closed-loop control systems are of a particular interest in these settings. One of the efficient approaches to implement control of the systems in this class is adaptive planning (Oktilev et al 2006;Andreev et al 2007). Adaptive planning uses not only simple open time slots (in contrast to incremental planning) but employs conflict-driven plan changes during the system execution.…”

Section: Uncertainty and Dynamicsmentioning

confidence: 99%

An adaptive framework for aligning (re)planning decisions on supply chain strategy, design, tactics, and operations

Ivanov

2009

International Journal of Production Research

114

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Over the last decade, a wealth of valuable approaches to supply chain strategic, tactical, and operational planning has been extensively developed. However, conventionally the planning decisions at each of these levels have been considered in isolation from the other levels. Moreover, decisions on supply chain strategy, design, tactics, and operations are interlinked and dispersed over different supply chain structures (functional, organizational, informational, financial, etc.). This study develops a framework to increase the efficiency, consistency, implacability, and sustainability of decisions on how to design, plan, and run supply chains. In this paper, we propose to consider the planning as an adaptive process that encapsulates the planning decisions to be interrelated at all the decision-making levels. We describe a conceptual model interlinking the supply chain strategy, design, planning, and operations on adaptation principles. Subsequently, we present a mathematical modelling complex and its realization in a software environment. This study contributes to developing methodical basics and practical tools to transit from simple open time slots incremental planning to dynamic, feedback-based adaptive supply chain planning. The elaborated framework serves to increase the efficiency, consistency, implacability, and sustainability of decisions on supply chain strategy, design, tactics, and operations. The decisions' alignment can help to increase organizations' wealth by producing demand-corresponding products in the most cost-effective manner through increasing agility, responsiveness, reaction speed to market changes, and continuous comprehensive improvements/adaptation of supply chains.

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“…This tool enables users to design and edit both the static and dynamic (called scenes here) aspects of an ontology. The ontology developed by this toolset for supply chain and logistic planning is then presented (Andreev et al, 2007). The examples of concepts are factory, cross-dock, truck, etc., and relations like isBookedForADemand.…”

Section: Domain-specific Ontologies For Agent-based Manufacturing Conmentioning

confidence: 99%

The Impacts of Semantic Technologies on Industrial Systems

Mařı́k

Vrba²,

Obitko³

2009

Handbook of Research on Social Dimensions of Semantic Technologies and Web Services

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Industrial systems face the challenges of robust and flexible control of industrial processes while satisfying the demand of mass customization and reduced time to market. To meet these requirements, systems need to work in a distributed manner, because the traditional centralized approaches are not sufficient. To achieve automated cooperation and coordination of distributed components at a larger scale, semantic technologies are necessary to enable truly open systems. The authors review state of the art of the research of ontologies, Semantic Web and Semantic Web services, together with advances of usage of semantic technologies in industry. The usage of semantic technologies is illustrated on two applications – semantics in multi-agent manufacturing systems and structural search in industrial data.

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Adaptive Planning for Supply Chain Networks

Cited by 14 publications

References 3 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

An adaptive framework for aligning (re)planning decisions on supply chain strategy, design, tactics, and operations

The Impacts of Semantic Technologies on Industrial Systems

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