# A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas
## 5 Challenge
### Challenge of Application in Energy Field  
Given three scenarios in the petroleum supply chain to illustrate the application of the learning approach for COPs, it is undeniable that COPs in the petroleum supply chain are significantly complicated, where each scenario requires a different approach. Refinery production planning involves strategic decisions of each refinery, considering both productions of other refineries and the demand of the market. This scenario is considered a multi-player game, and ML approaches based on game theory are widely used, where each refinery is seen as a single agent (player) competing or cooperating in the game, considering both their own profits and integrated profits in the game to achieve Nash equilibrium. Yet most of the ML approaches using game theory are applied to the aspect of market taking profits as the objective, although cases may differ in the real production process. Hence, more ML methods on the aspect of industrial production are to be investigated. Refinery scheduling is a more micro scenario which focuses on operations inside a single refinery where complex chemical sub-operations are involved. Since no player is involved in this scenario, no learning approach based on game theory is used when solving the problems. Rather, traditional COP frameworks, such as MILP and MINLP with machine learning improvement, such as heuristic algorithm, are more often utilized. Oil transportation including crude oil transportation and refined oil transportation is a classic programming problem that many industrial fields are facing. Problems of transporting through pipeline are mainly surveyed. Again, no learning approaches based on game theory are used due to lack of players in this scenario, and the traditional COP frameworks' embedding approaches, such as the stochastic algorithm, are introduced. Steel making has a similar production operational process to refinery scheduling, and hence they utilize the same scheduling problems and approaches. Traditional COP frameworks are widely used with the RL-based algorithm introduced for improvement. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Problem** & **Reference** & **Year** & **Approach** \\ \hline energy production & [120] & 2013 & model-free \\ optimization & & & distributed learning \\ wind power & [121] & 2014 & non-parametric \\ prediction & & & NN \\ static reserve & [122] & 2014 & two-stage \\ capacity planning & & & relaxation \\ capacity planning & [123] & 2015 & cooperative \\  & & game theory \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Application in wind power. Through investigation, two points of view and corresponding methods to solve scheduling problems in electric power system were discovered. ML-based learning approaches aim at solving CO scheduling problems concentrating on operation, which is similar to approaches for refinery scheduling. On the other hand, ML methods using game theory consider each energy hub as an agent deciding with decentralization. Neither approach provides the decision maker with solutions as the blueprint for the whole system, i.e., the methods emphasize the single stage or partial process in the operation instead of having a macro view. Plus, ML methods based on game theory build the model on the aspect of the economic market rather than industrial production, which may generate application problems. Not many applications for wind power were surveyed. The existing studies build models concerning power generated from and stored in the wind farm. Further investigation in this field is needed, and more surveys of the wind power system are to be conducted. Many applications of learning methods are utilized to simplify the model from MINLP to MIP, thus reducing the computational cost. However, such simplification may yield problems, such as poor generalization, low accuracy, limited scalability, expensive computation, etc.