## 5 Conclusions and Prospects  
This paper has defined the pairwise stable matching of coal mining and power generation companies, analyzed the matching preference rankings of the two sectors, designed a stable trading matching mechanism and algorithm of coal mining and power generation companies, and then tested the stability and sensitivity of stable matching results through a numerical example. The research conclusions are as follows:  
This paper presents a delayed acceptance algorithm for trading between coal mining and power generation companies considering the transaction volume. The trading matching results that were obtained by this algorithm are pairwise stable. The trading relations between the coal mining and power generation companies under the stable matching mechanism are resistant to disintegration, which makes the companies unwilling to readily give up the stable matching relations that have been formed. The pairwise stable matching result is clearly sensitive. Within a sensitivity range, an increase in supply by a single coal mining company or a decrease in demand by a single power generation company would not generally cause a change in the trading relations.  
This paper has certain theoretical innovation significance from the matching problem of non-separable commodities to that of separable commodities considering the trading volume between coal mining and power generation companies. Furthermore, it is a complement and perfection of the existing coal-power trading platform in its transaction mechanism and trading function.  
However, there are some limitations of this study. This paper mainly studies the stable matching mechanism and algorithm of coal mining and power generation companies in theory. Only one numerical example is given, which lacks the related big data support that is required for the stability verification of the algorithm. It is prospected that the stable matching mechanism can be applied to China's existing coal trading centers in order to supplement the trading function of stable transactions and then the matching mechanism and algorithm between coal mining and power generation companies can be further verified by relying on the big data platform of trading centers. The current study can be extended in future research by using neutrosophic statistics; neutrosophic statistics are the extension of classical statistics and are applied when the data are coming from a complex process or from an uncertain environment ([PERSON] and [PERSON], 2019; [PERSON], 2021) [25,26].  
Conceptualization, R.S.; methodology, Z.L.; software, Z.L. and Z.T.; validation, Z.T.; data curation, D.W. and Z.L.; writing--original draft preparation, R.S.; writing--review and editing, D.W. and R.S.; project administration, D.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fundamental Research Funds for the Central Universities (No. 2020 QN18, No. 2020 ZDPYSK05); Jiangsu Postdoctoral Research Support Program (No. 2020Z7294); China Postdoctoral Science Foundation (No. 2020M681783); Humanity and Social Science Youth Foundation of Ministry of Education of China (No. 21 YJC630115); National Natural Science Foundation of China (No. 71874191, No. 71974191); National Social Science Foundation of China (No.19 FGLB057); Postgraduate Education Teaching Reform and Practice Project of China University of Mining and Technology (No. 2021 YJSJG059).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.