Agent based Manufacturing Simulation for Efficient Assembly Operations

Sudo, Yasuhiro; Matsuda, Michiko

doi:10.1016/j.procir.2013.06.012

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…The structure of factory for agent module is described in hierarchical structure as shown in figure 1. In [11] the concept in [10] is tested through hypothetical problem-solving by building a virtual factory of automatic machines and workers. In [10,11] the authors used Artisoc which is a Japanese commercial multi agent simulator used by this paper to validate the multi agent autonomous assembly concept.…”

Section: B New Factory and New Process Planningmentioning

confidence: 99%

Agent based modeling in factory planning and process control

Negmeldin

Eltawil

2015

2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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Nowadays the need for autonomous distributed manufacturing systems is increasing. Due to the rapid change of technology and the need of more customer oriented products, manufacturing systems need to be more dynamic. In the past few years, Agent Based Modules (ABM) were introduced to optimize autonomous distributed systems as well as for process control. In this paper we review the literature addressing ABM in manufacturing strategy and dynamic process control between 2009 and 2015 to provide an up-to-date picture of the role of ABM to solve such problems and its efficiency as well as its future challenges.

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Section: B New Factory and New Process Planningmentioning

confidence: 99%

Agent based modeling in factory planning and process control

Negmeldin

Eltawil

2015

2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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“…Each station is equipped with component shops and stations are seen in this work as agents with eventually autonomous functions and goals. Methods based on multi-agent programming can be applied between flexible manufacturing processes and cooperation with agents, leading to the development of strategies to control and optimise production planning [31], often obtained by simulations [37].…”

Section: Introductionmentioning

confidence: 99%

Towards trustworthy Cyber-physical Production Systems: A dynamic agent accountability approach

Beregi

Pedone

Preuveneers

2021

AIS

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Smart manufacturing is a challenging trend being fostered by the Industry 4.0 paradigm. In this scenario Multi-Agent Systems (MAS) are particularly elected for modeling such types of intelligent, decentralised processes, thanks to their autonomy in pursuing collective and cooperative goals. From a human perspective, however, increasing the confidence in trustworthiness of MAS based Cyber-physical Production Systems (CPPS) remains a significant challenge. Manufacturing services must comply with strong requirements in terms of reliability, robustness and latency, and solution providers are expected to ensure that agents will operate within certain boundaries of the production, and mitigate unattended behaviours during the execution of manufacturing activities. To address this concern, a Manufacturing Agent Accountability Framework is proposed, a dynamic authorization framework that defines and enforces boundaries in which agents are freely permitted to exploit their intelligence to reach individual and collective objectives. The expected behaviour of agents is to adhere to CPPS workflows which implicitly define acceptable regions of behaviours and production feasibility. Core contributions of the proposed framework are: a manufacturing accountability model, the representation of the Leaf Diagrams for the governance of agent behavioural autonomy, and an ontology of declarative policies for the identification and avoidance of ill-intentioned behaviours in the execution of CPPS services. We outline the application of this enhanced trustworthiness framework to an agent-based manufacturing use-case for the production of a variety of hand tools.

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“…In this context some authors address, for example, a flexible job shop scheduling using meta-heuristics based on multi-agents [11,12]. It is also important to note that few studies present the job shop scheduling problem for efficient simulation layouts [17]. On the other hand, some authors integrate in a collaborative way the job shop scheduling with layout planning, that is, presenting a hybrid system capable of optimizing multiple solutions [15,16].…”

Section: Introductionmentioning

confidence: 99%

Hybrid System for Simultaneous Job Shop Scheduling and Layout Optimization Based on Multi-agents and Genetic Algorithm

Alves

Varela

Rocha

et al. 2019

Advances in Intelligent Systems and Computing

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A challenge is emerging in the design of scheduling support systems and facility layout planning, both for manufacturing environments where dynamic adaptation and optimization become increasingly important on the efficiency and productivity. Focusing on the interactions between these two problems, this work combines two paradigms in sequential manner, optimization techniques and multi-agent systems, to better reflect practical manufacturing scenarios. This approach, in addition to significantly improve the quality of the solutions, enables fast reaction to condition changes. In such stochastic and very volatile environments, the manufacturing industries, the fast rescheduling, or planning, are crucial to maintain the system in operation. The proposed architecture was codified in MatLab R and NetLogo and applied to a realworld job shop case study. The experimental results achieved optimized solutions, as well as in the responsiveness to achieve dynamic results for disruptions and simultaneously layout optimization.

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Agent based Manufacturing Simulation for Efficient Assembly Operations

Cited by 14 publications

References 8 publications

Agent based modeling in factory planning and process control

Agent based modeling in factory planning and process control

Towards trustworthy Cyber-physical Production Systems: A dynamic agent accountability approach

Hybrid System for Simultaneous Job Shop Scheduling and Layout Optimization Based on Multi-agents and Genetic Algorithm

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