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

Dwi Kurniawan, Rinto Yusriski, Mohammad Mi'radj Isnaini, Anas Ma'ruf, Abdul Hakim Halim


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.


Scheduling, flow shop, pre-processing, time-changing effects, actual flow time

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DOI: https://doi.org/10.3926/jiem.7134

Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2024

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience