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A Multi-agent Based Dynamic Scheduling of Flexible Manufacturing Systems

Author

Listed:
  • Mohd. Shaaban Hussain

    (ZHCET, AMU)

  • Mohammed Ali

    (ZHCET, AMU)

Abstract

Ongoing market requirements and real-time demands have led to intense competiveness in the manufacturing industry. Hence competitors are bound to employ newer means of manufacturing systems that can handle the ongoing market conditions in a flexible and efficient manner. To tackle these problems manufacturing control systems have evolved to the distributed manufacturing control system by exploiting their control architectures. These distributed control architectures provide an efficient mechanism that gives reactive and dynamically optimized system performance. This paper studies the impact of design and control factors on the performance of flexible manufacturing system. The system is evaluated on the basis of makespan, average machine utilization and the average waiting time of parts at the queue. Discrete-event based simulation models are developed to conduct simulation experiments. The results obtained were subjected to multi-response optimization as per Grey based Taguchi methodology. The effect of control architecture was statistically significant on the performance of flexible manufacturing system.

Suggested Citation

  • Mohd. Shaaban Hussain & Mohammed Ali, 2019. "A Multi-agent Based Dynamic Scheduling of Flexible Manufacturing Systems," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(3), pages 267-290, September.
    • Handle: RePEc:spr:gjofsm:v:20:y:2019:i:3:d:10.1007_s40171-019-00214-9
    DOI: 10.1007/s40171-019-00214-9
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    References listed on IDEAS

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    Cited by:

    1. Nitin S. Solke & Pritesh Shah & Ravi Sekhar & T. P. Singh, 2022. "Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 89-112, March.
    2. Mikhail Yurievich Ryabchikov & Elena Sergeevna Ryabchikova, 2022. "Big Data-Driven Assessment of Proposals to Improve Enterprise Flexibility Through Control Options Untested in Practice," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 43-74, March.

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