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

Daniel Leitold, Agnes Vathy-Fogarassy, Janos Abonyi

Research output: Article

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)455-473
Number of pages19
JournalCentral European Journal of Operations Research
Volume27
Issue number2
DOIs
Publication statusPublished - jún. 13 2019

ASJC Scopus subject areas

  • Management Science and Operations Research

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