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Empirical working time distribution-based line balancing with integrated simulated annealing and dynamic programming

Author

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  • Daniel Leitold

    (University of Pannonia)

  • Agnes Vathy-Fogarassy

    (University of Pannonia)

  • Janos Abonyi

    (University of Pannonia)

Abstract

According to the Industry 4.0 paradigms, the balancing of stochastic production lines requires easily implementable, flexible and robust tools for task to workstations assignment. An algorithm that calculates the performance indicators of the production line based on the convolution of the empirical density distribution functions of the working times and applies dynamic programming to assign tasks to the workstations is proposed. The sequence of tasks is optimised by an outer simulated annealing loop that operates on the set of interchangeable task-pairs extracted from the precedence graph of the task-ordering constraints. Eight line-balancing problems were studied and the results by Monte Carlo simulations were validated to demonstrate the applicability of the algorithm. The results confirm that our methodology does not just provide optimal solutions, but it is an excellent tool in terms of the sensitivity analysis of stochastic production lines.

Suggested Citation

  • Daniel Leitold & Agnes Vathy-Fogarassy & Janos Abonyi, 2019. "Empirical working time distribution-based line balancing with integrated simulated annealing and dynamic programming," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(2), pages 455-473, June.
    • Handle: RePEc:spr:cejnor:v:27:y:2019:i:2:d:10.1007_s10100-018-0570-7
    DOI: 10.1007/s10100-018-0570-7
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    References listed on IDEAS

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    Cited by:

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    2. Reinhard Neck, 2020. "CEJOR special issue: dynamic optimization in management and economics," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(2), pages 367-369, June.
    3. Diefenbach, Johannes & Stolletz, Raik, 2022. "Stochastic assembly line balancing: General bounds and reliability-based branch-and-bound algorithm," European Journal of Operational Research, Elsevier, vol. 302(2), pages 589-605.

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