Robust and stable multi-task manufacturing scheduling with uncertainties using a two-stage extended genetic algorithm

ABSTRACT

In cloud manufacturing (CMfg), unexpected uncertainties can occur in real-world manufacturing processes that could make the predetermined schedule infeasible. A new multi-objective proactive method is proposed in this situation to evaluate the proactive schedule. A novel two-stage extended genetic algorithm (2S-EGA) is proposed to generate proactive schedules that consider service interruptions. The experimental results confirmed that the obtained proactive schedule produces great performance when applied to multi-task scheduling problems with service interruptions. Furthermore, the results also showed that the proactive schedule obtained by the proposed approach is more robust and stable than other baseline algorithms taken from the literature.

KEYWORDS:

• CMfg
• flexible multi-task scheduling
• uncertainties
• robustness and stability
• proactive schedule
• 2S-EGA

Supporting information

All experimental data for this paper have been public in the Figshare database (https://doi.org/10.6084/m9.figshare.7275728).

Compliance with ethical standards

Conflicts of interest: The authors declare that there is no conflict of interests regarding the publication of this article.

Ethical standard: The authors state that this research complies with ethical standards. This research does not involve either human participants or animals.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (No. 51875503, No. 51975512, No. 51775496) and Zhejiang Natural Science Foundation of China (No. LY17E050010) .