An effective heuristic for adaptive control of job sequences subject to variation in processing times

Abstract

Variation in sequential task processing times is common in manufacturing systems. This type of disturbance challenges most scheduling methods since they cannot fundamentally change job sequences to adaptively control production performance as jobs enter the system because actual processing times, are not known in advance. Some research literature indicates that simple rules are more suitable than algorithmic scheduling methods for adaptive control. In this work, a ‘state space – average processing time’ (SS-APT) heuristic is proposed and compared to four most commonly used scheduling rules and two well-established heuristics based on Taillard’s benchmarks. It is shown that the adaptive control is made possible under variation in processing times given the flexibility and strong performance of the SS-APT heuristic, especially for work-in-process inventory control.

Keywords:

• adaptive control
• flow shop
• scheduling
• variation in processing times

Acknowledgment

We also thank Barrie R. Nault and anonymous referees for their comments, time and effort in helping improve the quality of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by the funding provided by Department of Mechanical Engineering at University of Kentucky, Lexington, KY, USA.