State of the Art and Future Trends of Optimality and Adaptability Articulated Mechanisms for Manufacturing Control Systems

Jiménez, Jose-Fernando; Bekrar, Abdhelghani; Trentesaux, Damien; Montoya‐Torres, Jairo R.; Leitão, Paulo

doi:10.1109/smc.2013.219

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…Third, a shop-floor control and rescheduling in the operational level is implemented. This practice is made mainly because it presents good and near-optimal performance within manufacturing productivity (Jimenez et al, 2013). Unfortunately, due to the difficulty of visualizing the entire manufacturing system, the production planning is based on some uncertain information, such as estimated capabilities, aggregated forecast and virtual inventories.…”

Section: Manufacturing Control Systems (Mcs)mentioning

confidence: 99%

An adaptive genetic algorithm for a dynamic single-machine scheduling problem

Jiménez¹,

González-Neira²,

Zambrano-Rey³

2018

10.5267/j.msl

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Nowadays, industries cope with a wide range of situations and/or perturbations that endanger the manufacturing productivity. Traditionally, manufacturing control systems are responsible for managing the manufacturing scheduling and execution, as these have the capability of maintaining the production operations regardless of a given perturbation. Still, the challenge of these systems is to achieve an optimal performance after the perturbations occur. For this reason, manufacturing control systems must incorporate a mechanism with intelligent capabilities to look for optimal performance and operation reactivity regardless of any scenario. This paper proposes a generic control strategy for a manufacturing control system for piloting the execution of a dynamic scheduling problem, considering a new job arrival as the manufacturing perturbation. The study explores a predictive-reactive approach that couples a genetic algorithm for the predictive scheduling and an adaptive genetic algorithm for reactivity control aiming to minimize the weighted tardiness in a dynamic manufacturing scenario. The results obtained from this proposal verify that the effectiveness was improved by using adaptive metaheuristic in a dynamic scheduling problem, considering absorbing the degradation caused by the perturbation.

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Section: Manufacturing Control Systems (Mcs)mentioning

confidence: 99%

An adaptive genetic algorithm for a dynamic single-machine scheduling problem

Jiménez¹,

González-Neira²,

Zambrano-Rey³

2018

10.5267/j.msl

Self Cite

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“…Thereby, since few years, researchers integrate dynamic features in the manufacturing control system architectures (Jimenez et al 2013). From our point of view, the term control system architecture (CSA) refers to the structural and behavioural characteristics that define the elements, attributes, structure composition and operational behaviour of a control system.…”

Section: Introductionmentioning

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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