A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines

Konstantinos S. Boulas (), Georgios D. Dounias () and Chrissoleon T. Papadopoulos ()
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Konstantinos S. Boulas: University of the Aegean
Georgios D. Dounias: University of the Aegean
Chrissoleon T. Papadopoulos: Aristotle University of Thessaloniki

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 2, No 24, 823-852

Abstract: Abstract The analytical evaluation of production system performance measures is a difficult task. Over the years, various methods have been developed to solve specific cases of very short production lines. However, formulae for estimating the mean production rate (throughput) are lacking. Recent developments in artificial intelligence simplify their use in the solution of symbolic regression problems. In this work, we use genetic programming (GP) to obtain approximate formulae for calculating the throughput of short reliable approximately balanced production lines, for which the processing times are exponentially distributed. A hybrid GP&GA scheme reduces the search space, in which GP uses genetic algorithms (GA) as a search engine. The scheme produces polynomial formulae for throughput estimation for the first time. To train the GP algorithm we use MARKOV, an accurate algorithm for calculating numerically the exact throughput of short exponential production lines. A few formulae, not previously reported in the literature, are presented. These formulae give close results to the exact results from the MARKOV algorithm, for short (up to five stations) reliable approximately balanced production lines without intermediate buffers. Also, the robustness of these formulae is satisfactory. In addition, the proposed hybrid GP&GA scheme is useful for design/production engineers to adjust the formulae to other ranges of the mean processing rates; the algorithms are quickly retrained to generate a new approximate formula.

Keywords: Discrete part reliable production lines; Performance evaluation; Throughput; Hybrid evolutionary algorithms; Genetic programming; Genetic algorithms (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-021-01828-6

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