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

Abstract

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.