**6. Conclusions**

In this paper, we studied the FHFGSP with fuzzy processing time that minimizes makespan and total energy consumption. To solve FHFGSP, a discrete artificial bee colony algorithm based on similarity and non-dominated solution ordering was proposed. After extensive numerical experiments, it can be demonstrated that the proposed strategy and algorithm outperforms other algorithms in terms of performance.

In the employed bee phase, individuals fully explore around the dominant solution; in the onlooker bee phase, individuals at the front of the sequence have a greater chance of being followed; in addition, a mutation strategy was proposed to prevent the population from falling into a local optimum. The algorithm produced solutions of high-quality in terms of quantity, quality, convergence, and distribution.

In future, our aim is to study more flexible HFGSPs, such as the proficiency of shop workers, and to consider other green metrics, such as noise and carbon emissions. We will verify the effectiveness of the algorithm by comparing it with more optimization algorithms based on mimicking animal behavior, which will have a positive impact on the role of such algorithms in relation to the green shop scheduling problem. In addition, as smart manufacturing continues to evolve and people start to use information physical systems and industrial Internet of Things to obtain data in real time during manufacturing processes, it is also interesting to study how to process real-time state data for decision making and optimization of green shop scheduling.

**Author Contributions:** Conceptualization, M.L. and G.-G.W.; methodology, M.L.; software, M.L.; validation, M.L., G.-G.W. and H.Y.; formal analysis, M.L.; investigation, G.-G.W.; resources, H.Y.; data curation, H.Y.; writing—original draft preparation, M.L.; writing—review and editing, M.L., G.-G.W. and H.Y.; visualization, M.L., G.-G.W.; supervision, G.-G.W.; project administration, H.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** National Natural Science Foundation of China (No. U19A2061), National Key R&D Program of China (No. 2019YFC1710700), Science and Technology Development Project of Jilin Province (No. 20190301024NY and No. 20200301047RQ).

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
