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A production planning and optimisation of multi-mode job shop scheduling problem for an avionics manufacturing plant

Author

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  • Pu-Hai Chiang
  • Chau-Chen Torng

Abstract

In this paper, we study the job shop scheduling problem (JSSP) with the objective of minimising the total weighted tardiness (TWT). The planning of a suitable production scheduling algorithm is necessarily established in accordance with workflows, facilities and machines of an avionics manufacturing plant. In this research, we propose an iterated greedy (IG) algorithm to solve the problem and to find an optimised solution, and we also exploit this synergy to develop a state-of-the-art algorithm for the job shop TWT problem. The computational results in effectiveness of the algorithm are compared with standard benchmark instances from the literature. The optimised production scheduling results in reduced production cost and manpower; moreover, it improves the relevant resources utilisation and overall efficiency. This paper also demonstrated a practical application of production scheduling management in aerospace industry.

Suggested Citation

  • Pu-Hai Chiang & Chau-Chen Torng, 2016. "A production planning and optimisation of multi-mode job shop scheduling problem for an avionics manufacturing plant," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 30(3/4), pages 179-195.
  • Handle: RePEc:ids:ijmtma:v:30:y:2016:i:3/4:p:179-195
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    Citations

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    Cited by:

    1. Mohamed Habib Zahmani & Baghdad Atmani, 2021. "Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation," Journal of Scheduling, Springer, vol. 24(2), pages 175-196, April.
    2. Gabriel Mauricio Zambrano-Rey & Eliana María González-Neira & Gabriel Fernando Forero-Ortiz & María José Ocampo-Monsalve & Andrea Rivera-Torres, 2024. "Minimizing the expected maximum lateness for a job shop subject to stochastic machine breakdowns," Annals of Operations Research, Springer, vol. 338(1), pages 801-833, July.

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