# A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System
## 6 Conclusions  
In this paper, the MCVS problem of coal intelligent loading systems is studied, and a MCVS method considering customer priority is proposed. By enhancing the SSA, the optimal vehicle scheduling scheme is obtained. Finally, the proposed method is verified through the case of enterprise A as an example. The main conclusions are as follows:  
(1) To address the issues of unclear customer priority and low satisfaction, the enterprise customer priority is obtained by using the VIKOR method, which provides a foundation for MCVS.  
(2) In light of the poor quality of the traditional SSA solving plan, the Chebyshev chaotic mapping and cross-mutation operations are introduced into the traditional SSA algorithm. This improvement aims to boost the solving speed and search capability of the algorithm, ultimately validating its effectiveness.  
(3) Aiming to address the issues of low efficiency in vehicle scheduling and long waiting times for customers, a vehicle scheduling plan is proposed for coal mining enterprises through the construction of the MCVS model. The total cost of this plan is reduced by 79% compared to the pre-optimization plan.  
The MCVS method proposed in this paper provides a scientific, efficient, and intelligent scheduling plan for coal mining enterprises. However, the current research mainly focuses on the real-time scheduling of customer arrival times and actual demand, and has not yet involved the demand prediction and path uncertainty. The specific outlook is as follows:  
Figure 9: Comparison diagram of plan costs before and after optimization.  
(1) Future research should introduce demand forecasting methods to predict customer demand in advance by analyzing historical data and market trends [35]. This will make the scheduling plan more forward-looking, reduce response time to sudden changes in demand, and improve overall scheduling efficiency.  
(2) Vehicles may encounter uncertain factors such as traffic congestion and weather changes on their way to coal mining enterprises. These factors may have an impact on scheduling. Therefore, subsequent studies should adjust the model by combining it with these uncertainties and explore robust optimization or real-time adjustment mechanisms to improve the flexibility and adaptability of scheduling systems.  
All in all, although the MCVS method proposed in this paper provides strong support for the vehicle scheduling of coal mining enterprises, there is still room for improvement in demand forecasting and uncertainty processing. Future research will focus on these problems, provide more perfect and efficient scheduling plans for coal mining enterprises, and further promote the sustainable development and competitiveness of coal mining enterprises in the complex operating environment.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14188178/s1](https://www.mdpi.com/article/10.3390/app14188178/s1)]([https://www.mdpi.com/article/10.3390/app14188178/s1](https://www.mdpi.com/article/10.3390/app14188178/s1)), Table S1. Detailed customer information table.  
Conceptualization, R.L.; Data curation, R.L.; Formal analysis, H.W.; Investigation, L.W.; Methodology, L.W. and X.H.; Software, R.L.; Supervision, Y.W.; Validation, R.L.; Visualization, R.L.; Writing--original draft, R.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.