A flow shop batch scheduling model with pre-processing and time-changing effects to minimize total actual flow time

• Kurniawan, Dwi ^[1] ; Yusriski, Rinto ^[2] ; Isnaini, Mohammad Mi'radj ^[3] ; Ma'ruf, Anas ^[3] ; Halim, Abdul Hakim ^[3]
  1. [1] Institut Teknologi Nasional Bandung
  2. [2] Universitas Jenderal Achmad Yani
  3. [3] Institut Teknologi Bandung

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• Localización: Journal of Industrial Engineering and Management, ISSN-e 2013-0953, Vol. 17, Nº. 2, 2024, págs. 542-561
• Idioma: inglés
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  • Purpose: This paper investigates a batch scheduling problem where pre-processing is required for parts before processing, considering time-changing effects from part deterioration and operator learning-forgetting.Design/methodology/approach: A mathematical model was developed with the decision variables of the number of batches, the number of pre-processings, batch sizes, and the schedule of processes and pre-processings to minimize total actual flow time. Different numbers of batches were gradually tried and increased until the objective function stopped improving. The minimum number of pre-processings that resulted in a feasible solution was examined at each number of batches.Findings: Our experiment showed that: (1) A faster operator learning led to a lower optimal number of batches and a lower total actual flow time, (2) A faster part deterioration brought a higher number of batches and a higher total actual flow time, (3) The model minimized the number of pre-processings by only scheduling pre-processings before the operations at machine 1, and (4) The model divided the parts into small batches to prevent increased processing time due to part deterioration.Research limitations: The research did not consider multi-due date and multi-item system which require pre-processings with different times and capacities.Practical implications: Production managers should assign fast learning operators to shorter batches and faster deteriorating parts.Originality/value: This research was the first to consider pre-processing in batch scheduling.