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Analytical approximations to predict performance measures of markovian type manufacturing systems with job failures and parallel processing

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  • Hulett, Maria
  • Damodaran, Purushothaman

Abstract

Manufacturing or service systems with multiple product classes, job circulation due to random failures, resources shared between product classes, and some portions of the manufacturing or assembly carried in series and the rest in parallel are commonly observed in real-life. The web server assembly is one such manufacturing system which exhibits the above characteristics. Predicting the performance measures of these manufacturing systems is not an easy task. The primary objective of this research was to propose analytical approximations to predict the flow times of the manufacturing systems, with the above characteristics, and evaluate its accuracy. The manufacturing system is represented as a network of queues. The parametric decomposition approach is used to develop analytical approximations for a system with arrival and service rates from a Markovian distribution. The results from the analytical approximations are compared to simulation models. In order to bridge the gap in error, correction terms were developed through regression modeling. The experimental study conducted indicates that the analytical approximations along with the correction terms can serve as a good estimate for the flow times of the manufacturing systems with the above characteristics.

Suggested Citation

  • Hulett, Maria & Damodaran, Purushothaman, 2011. "Analytical approximations to predict performance measures of markovian type manufacturing systems with job failures and parallel processing," European Journal of Operational Research, Elsevier, vol. 212(1), pages 89-99, July.
    • Handle: RePEc:eee:ejores:v:212:y:2011:i:1:p:89-99
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    References listed on IDEAS

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    4. Pradhan, Salil & Damodaran, Purushothaman & Srihari, Krishnaswami, 2008. "Predicting performance measures for Markovian type of manufacturing systems with product failures," European Journal of Operational Research, Elsevier, vol. 184(2), pages 725-744, January.
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    Cited by:

    1. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Jula, Payman & Pirayesh, Amir & Ahmadi, Hadi, 2020. "A learning-based metaheuristic for a multi-objective agile inspection planning model under uncertainty," European Journal of Operational Research, Elsevier, vol. 285(2), pages 513-537.
    2. Damodaran, Purushothaman & Hulett, Maria, 2012. "Analytical approximations to predict performance measures of manufacturing systems with general distributions, job failures and parallel processing," European Journal of Operational Research, Elsevier, vol. 221(1), pages 74-86.
    3. Shraga Shoval & Mahmoud Efatmaneshnik & Michael J. Ryan, 2017. "Assembly sequence planning for processes with heterogeneous reliabilities," International Journal of Production Research, Taylor & Francis Journals, vol. 55(10), pages 2806-2828, May.
    4. Wu, Kan & McGinnis, Leon, 2012. "Performance evaluation for general queueing networks in manufacturing systems: Characterizing the trade-off between queue time and utilization," European Journal of Operational Research, Elsevier, vol. 221(2), pages 328-339.

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