Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system

He, Naihui; Zhang, Zhengwen; Li, Q.

doi:10.1016/j.ijpe.2013.08.022

Cited by 64 publications

(31 citation statements)

References 42 publications

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“…The literature is full of extensive research on integration methodologies like multi agent simulation, game theory, Nash equilibrium, genetic algorithm (He et al, 2014;Jose et al, 2015;Wang et al, 2003;Wang et al, 2001). Various approaches for integrated process planning and production scheduling have been proposed such as just-in-time process planning, flexible process planning, dynamic process planning, non-linear process planning (Chen, 2010); Huang et al, 2013;Kim et al, 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing

Singh

Kumari

Govindan

et al. 2016

International Journal of Production Research

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In modern world, manufacturing processes have become very complex because of consistently fluctuating demand of customers. Numerous production facilities located at various geographical locations are being utilized to address the demands of their multiple clients. Often, the components manufactured at distinct locations are being assembled in a plant to develop the final product. In this complex scenario, manufacturing firms have to be responsive enough to cope with the fluctuating demand of customers. To accomplish it, there is need to develop an integrated, dynamic, autonomous system. In this article, a self-reactive cloud based multi agent architecture for distributed manufacturing system is developed. The proposed architecture will assist manufacturing industry to establish real time information exchange between the autonomous agents, clients, suppliers and manufacturing unit. The mechanism described in this study demonstrates how the autonomous agents interact with each other to rectify the internal discrepancies in manufacturing system. It can also address the external interferences like variations in client's orders to maximise the profit of manufacturing firm in both short and long term. Execution process of proposed architecture is demonstrated using simulated case study.

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Section: Literature Reviewmentioning

confidence: 99%

Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing

Singh

Kumari

Govindan

et al. 2016

International Journal of Production Research

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show abstract

“…The effectiveness and efficiency of real-time decisionmaking can be improved by applying intelligent manufacturing techniques, such as multi-agent systems [93], cloud manufacturing [94], digital manufacturing [95] and cyber physical systems [96]. For example, He et al [93] developed a novel mechanism that enables manufacturing resources to be self-organized cost-efficiently within structural constraints of a make-to-order manufacturing system for fulfilling customer orders.…”

Section: Concurrent Design Of Product Manufacturing Systems and Busmentioning

confidence: 99%

“…For example, He et al [93] developed a novel mechanism that enables manufacturing resources to be self-organized cost-efficiently within structural constraints of a make-to-order manufacturing system for fulfilling customer orders.…”

Section: Concurrent Design Of Product Manufacturing Systems and Busmentioning

confidence: 99%

Reconfigurable manufacturing systems: Principles, design, and future trends

2017

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Reconfigurable manufacturing systems (RMSs), which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are costeffective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.

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“…As the complexity of these manufacturing processes increases, so too must their system models expand. Most organizational and technology-based manufacturing systems are hierarchical in nature (Alvandi, Bienert, Li, & Kara, 2015) (He, Zhang, & Li, 2014), so their models must reflect this reality. In the context of smart manufacturing, the natural structure of these systems of systems can be represented as follows:…”

Section: Smart Manufacturing System Of Systemsmentioning

confidence: 99%

“…Most organizational and technology-based manufacturing systems are hierarchical in nature (Alvandi, Bienert, Li, & Kara, 2015) (He, Zhang, & Li, 2014), and the deployments of HHM have effectively addressed the risks at these multiple levels (Haimes, Kaplan, & Lambert, 2002). During the decomposition process of HHM, two major types of risks will ultimately come to light: those resulting from 1) exogenous events such as new legislation or natural disasters, and 2) endogenous events such as hardware, software, organizational, and human failures (Haimes, 2009).…”

Section: Benefit Of Hhmmentioning

confidence: 99%

Harmonizing Prognostics and Health Management with Risk Analysis for Smart Manufacturing Systems

Malinowski¹

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This thesis develops a methodology for the design phase of a Prognostics Health Management (PHM) system of a manufacturing process. The methodology (i) builds upon Hierarchical Holographic Modeling (HHM), Risk Filtering, Ranking, and Management (RFRM), and Fault Tree Analysis (FTA); (ii) provides scope and direction for a PHM system by identifying a prioritized set of targets that would most benefit from PHM capabilities; and (iii) tests the outcome of the developed methodology with two case studies with U.S. manufacturing companies. Currently, there are multiple methods to determine the major failure modes of a system following an accident or catastrophe. However, the proposed methodology in this thesis allows for a thorough analysis to be conducted even before a failure occurs in a manufacturing environment. Another important goal of this thesis is to demonstrate the compatibility and synergy between two seemingly different methodologies/processes: Risk analysis and PHM for manufacturing. More specifically, this research demonstrates that the theory, methodology, and current practice of the system-based risk analysis are harmonious and compatible with PHM.

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Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system

Cited by 64 publications

References 42 publications

Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing

Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing

Reconfigurable manufacturing systems: Principles, design, and future trends

Harmonizing Prognostics and Health Management with Risk Analysis for Smart Manufacturing Systems

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