Non-hierarchical control of a flexible manufacturing cell

Duffie, Neil A.; Piper, Rex S.

doi:10.1016/0736-5845(87)90099-8

Cited by 104 publications

(30 citation statements)

References 2 publications

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“…Examples can be found in [23], [58], [79], [83], and [128]. However, the autonomous approach is only well suited for developing distributed systems consisting of a small number of agents.…”

Section: System Structuresmentioning

confidence: 99%

“…Thus, various improvements to the basic contract net approach have been proposed, such as the following: 1) sending offers to a limited number of nodes, instead of broadcasting them; 2) anticipating offers, i.e., contractors send bids in advance; 3) varying the time when commitment is decided; 4) allowing de-commitment (breaking commitments); 5) allowing several agents to answer as a group (coalition formation); 6) introducing priorities for solving tasks. The CNP-based bidding mechanism used in manufacturing scheduling can be part-oriented [23], [58], resource-oriented [6], [12], or bidirectional [83].…”

Section: Coordination and Negotiationmentioning

confidence: 99%

See 1 more Smart Citation

Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

Shen

Wang

Hao

2006

IEEE Trans. Syst., Man, Cybern. C

352

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Abstract-Manufacturing process planning is the process of selecting and sequencing manufacturing processes such that they achieve one or more goals and satisfy a set of domain constraints. Manufacturing scheduling is the process of selecting a process plan and assigning manufacturing resources for specific time periods to the set of manufacturing processes in the plan. It is, in fact, an optimization process by which limited manufacturing resources are allocated over time among parallel and sequential activities. Manufacturing process planning and scheduling are usually considered to be two separate and distinct phases. Traditional optimization approaches to these problems do not consider the constraints of both domains simultaneously and result in suboptimal solutions. Without considering real-time machine workloads and shop floor dynamics, process plans may become suboptimal or even invalid at the time of execution. Therefore, there is a need for the integration of manufacturing process-planning and scheduling systems for generating more realistic and effective plans. After describing the complexity of the manufacturing process-planning and scheduling problems, this paper reviews the research literature on manufacturing process planning, scheduling as well as their integration, particularly on agent-based approaches to these difficult problems. Major issues in these research areas are discussed, and research opportunities and challenges are identified.

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“…Examples can be found in [23], [58], [79], [83], and [128]. However, the autonomous approach is only well suited for developing distributed systems consisting of a small number of agents.…”

Section: System Structuresmentioning

confidence: 99%

Section: Coordination and Negotiationmentioning

confidence: 99%

Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

Shen

Wang

Hao

2006

IEEE Trans. Syst., Man, Cybern. C

352

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show abstract

“…Recently, some researchers [HAT85,DUF86] have attempted to address these problems. In addition, they seek to eliminate the rigid supervisor/subordinate relationships found in hierarchies.…”

Section: Heterarchical Controlmentioning

confidence: 99%

The cell as part of a manufacturing system

Jones¹

1990

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“…The traditional manufacturing control architectures are not designed to exhibit good agility and adaptation. In fact, their centralized and hierarchical control approaches present good production optimization, but the rigidity and centralization of the control structure implies a weak response to disturbances, therefore, the substitutional heterarchical control architecture has been proposed and investigated [3,4]. On the other hand, heterarchical manufacturing control architecture presents a good response to change and unpredictable disturbances, but as autonomous manufacturing cells have different information, knowledge, goals and understanding of current situations, and decisions are based on partial knowledge of the system, their action plan may be disparate or even conflictive.…”

Section: Introductionmentioning

confidence: 99%

Pheromone-based Dynamic Coordination for Manufacturing System

Wang¹,

Li²,

Cai³

et al. 2014

TOAUTOCJ

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Manufacturing enterprises must face increasingly difficult production environments. External factors such as varying product variants and varying quantities as well as internal factors such as reworking and resource breakdowns pose a high challenge for task allocation and control. Inspired by the similarity between ant foraging behavior model and task allocation in manufacturing system, a pheromone-based coordination mechanism for manufacturing system is proposed. The pheromone-based static and dynamic coordination algorithms for task allocation are given. Experimental results demonstrate that not only the relatively optimal processing costs but also balanced use of machines can be achieved by using the proposed algorithm, and that it has high adaptability to the disturbance from inside or outside of manufacturing system. Therefore, it provides a feasible new idea for dealing with an actual allocation problem of production tasks for manufacturing system.

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Non-hierarchical control of a flexible manufacturing cell

Cited by 104 publications

References 2 publications

Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

The cell as part of a manufacturing system

Pheromone-based Dynamic Coordination for Manufacturing System

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