Reducing myopic behavior in FMS control: A semi-heterarchical simulation–optimization approach

Rey, Gabriel Zambrano; Bonte, Thérèse; Prabhu, Vittaldas V.; Trentesaux, Damien

doi:10.1016/j.simpat.2014.01.005

Cited by 48 publications

(15 citation statements)

References 220 publications

(291 reference statements)

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“…Unfortunately, given the myopic behaviour of Fig. 1 Two strategies to respond to online perturbations local entities, it becomes hard to predict the system's overall performance for the remaining production time [17].…”

Section: Rescheduling In Hcamentioning

confidence: 99%

Analysing the Impact of Rescheduling Time in Hybrid Manufacturing Control

Jiménez¹,

Zambrano-Rey²,

Bekrar³

et al. 2017

Service Orientation in Holonic and Multi-Agent Manufacturing

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Hybrid manufacturing control architectures merge the benefits of hierarchical and heterarchical approaches. Disturbances can be handled at upper or lower decision levels, depending on the type of disturbance, its impact and the time the control system has to react. This paper focuses particularly on a disturbance handling mechanism at upper decision levels using a rescheduling manufacturing method. Such rescheduling is more complex that the offline scheduling since the control system must take into account the current system status, obtain a satisfactory performance under the new conditions, and also come up with a new schedule in a restricted amount of time. Then, this paper proposes a simple and generic rescheduling method which, based on the satisfying principle, analyses the trade-off between the rescheduling time and the performance achieved after a perturbation. The proposed approach is validated on a simulation model of a realistic assembly cell and results demonstrate that adaptation of the rescheduling time might be beneficial in terms of overall performance and reactivity.

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Section: Rescheduling In Hcamentioning

confidence: 99%

Analysing the Impact of Rescheduling Time in Hybrid Manufacturing Control

Jiménez¹,

Zambrano-Rey²,

Bekrar³

et al. 2017

Service Orientation in Holonic and Multi-Agent Manufacturing

Self Cite

View full text Add to dashboard Cite

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“…At the end, the control program has an alternative operating mode to accommodate according to manufacturing needs. Another CSA is proposed by (Zambrano Rey, 2014). The author introduced a flexible decision-making technique (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Governance mechanism in control architectures for flexible manufacturing systems

Jiménez¹,

Bekrar²,

Trentesaux³

et al. 2015

IFAC-PapersOnLine

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Manufacturing systems, and specifically Flexible Manufacturing Systems (FMS), face the challenge of accomplish global optimal performance and reactiveness at dynamic manufacturing environments. For this reason, manufacturing control systems must incorporate mechanisms that support dynamic custom-build responses. This paper introduces a framework that includes a governance mechanism in control system architectures that dynamically steers the autonomy of decision-making between predictive and reactive approaches. Results from experiments led in simulation show that it is worth studying in depth a governance mechanism that tailors the structure and/or behaviour of a manufacturing control system and, at the same time, potentiate the reactivity required in manufacturing operations.

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“…However some limitation, called "myopic behaviour", must be noticed [8]. Due to the limited visibility of the IPs on the future behaviour of the manufacturing systems, an optimal performance of the overall system is not assured [9].…”

Section: Motivationmentioning

confidence: 99%

A Model for Manufacturing Scheduling Optimization Through Learning Intelligent Products

Bouazza

Sallez²,

Aissani

et al. 2015

Studies in Computational Intelligence

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The needs of flexibility, agility and adaptation capabilities for modern manufacturing systems increase constantly. In this paper, we propose an original approach combining active/intelligent product architecture with learning mechanism to assure flexibility and agility to the overall manufacturing system. Using learning approaches as Reinforcement Learning (RL) mechanism, an active product can be able to reuse learned experiences to enhance its decisional performances. A contextualization method is proposed to improve the decision making of the product for scheduling tasks. The approach is then applied to a case study using a multi-agent simulation platform.

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Reducing myopic behavior in FMS control: A semi-heterarchical simulation–optimization approach

Cited by 48 publications

References 220 publications

Analysing the Impact of Rescheduling Time in Hybrid Manufacturing Control

Analysing the Impact of Rescheduling Time in Hybrid Manufacturing Control

Governance mechanism in control architectures for flexible manufacturing systems

A Model for Manufacturing Scheduling Optimization Through Learning Intelligent Products

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