## 6 Conclusions and Future Work  
To improve the rapid response capability and save energy consumption for enterprises, we propose a novel MEIFM problem. FJSSP and MRWLP are traditionally addressed as two separate decisions, without considering the interaction between them. This paper focuses on the coordinated optimization between FJSSP and MRWLP, and integrates FJSSP and MRWLP decisions into a leader-follower decision framework. As a leader, the FJSSP aims at optimizing the performance of the scheduling scheme, while the MRWLP acts as a follower which responds to the leader's decision on workshop layout. Furtherly, the paper proposes an MOBLP model to reveal the inherent bi-level structure in the MEIFM problem, which includes an upper-level model and a lower-level model. The upper-level model is FJSSP, which seeks the optimal scheduling scheme by minimizing the makespan and total energy consumption. The lower-level model is the MRWLP problem, which finds the optimal layout scheme by minimizing material handling quantity. As the integrated optimization problem is NP-hard, this paper proposes an IMHGA to solve it. The experimental results indicate that the proposed method can identify a set of Pareto optimal solutions with better convergence and distribution.  
Based on the experimental results, the findings and implications are as follows:  
1. Separate optimization of scheduling and layout planning can limit the performance of the manufacturing system because the interaction between them is ignored. Therefore, the coordination optimization of scheduling and layout planning is necessary and can greatly improve the compatibility of the manufacturing system;
2. The solutions of the MEIFM problem proposed by this paper not only improve the responsiveness of enterprises facing rapid changes of market demand, but also provide energy-saving methods from a systematic optimization perspective for manufacturing enterprises;
3. The methodology developed in this paper will provide efficient guidance and reference for solving complex bilevel optimization problems.  
In the future, some other objectives, such as total tardiness, closeness rating score, and re-layout cost, should be considered when applying the proposed method. Besides, although the efficiency of this method is verified by the experimental results, its robustness and stability should be strengthened in future research.  
[PERSON] contributed to the overall idea, algorithm, and writing of the manuscript; [PERSON] coded the algorithm in MATLAB and contributed to the detailed writing; [PERSON] contributed to the ideas and discussions on the integrated FJSSP and MRLP considering energy consumption, as well as the revision, preparation, and publishing of this paper; [PERSON] analyzed the characteristics of the integrated optimization problem. All authors have read and approved the final manuscript.  
**Funding:** This research is supported by the National Natural Science Foundation of China (Grant No. 71772002).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Convergence Metric**} & \multirow{2}{*}{**Spacing Metric**} \\ \cline{2-3} \cline{5-5}  & **C(IMHGA, MHGA)** & & **C(MHGA, IMHGA)** \\ \hline IMHGA & 0.9338 & — & 3.2292 \\ MHGA & — & 0.2000 & 5.9856 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Result of the evaluation metrics of the IMHAG and MHGA.