# A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas
## 4 Applications in Energy Field
### Petroleum Supply Chain
#### 4.1.2 Refinery Scheduling  
Refinery scheduling is a significant production operation in the upstream petroleum supply chain in which many complex chemical sub-operations are involved, including crude oil unloading, crude oil mixing, production unit operation, products blending, and refined oil distribution [102]. These scheduling optimization problems are stochastically uncertain and have multiple constraints and objective functions [103]. It is difficult to optimize the model with such complexity, not to mention large-scale scheduling problems. Therefore, different models and algorithms with built-in ML mechanisms were surveyed, where each approach either simplifies the model or reduces the time of solving. Crude oil scheduling sometimes can involve uncertain conditions, especially when dealing with product demands or ship arrival time at the terminal. To solve crude oil scheduling problems under uncertain conditions, [PERSON] et al. [104] developed a two-stage robust model which transforms fuzzy programming and chance-constrained programming into deterministic counterpart problems at the first stage and proposes the second stage based on the scenario. The experiment shows that the feasible rate raised from 32% to 97% compared to the approach proposed by [PERSON] et al. [PERSON] et al. [105] suggested a hybrid mechanism combining DP and mixed genetics to improve the general DP algorithm, which is computationally expensive to solve the model with inequality constraints. The algorithm successfully solves multi-stage production and operation DP problems in refineries under uncertain market demand and yields an adaptive and effective solution. Among all the literature works investigated, many applications of heuristic aim to simplify the model and thus make the algorithm computationally cheaper to obtain the optimization. [PERSON] et al. [102] proposed a heuristic algorithm based on a decomposed network to solve the oil-refinery problem. The authors decomposed the problem into two separate scheduling problems and generated multiple integer cuts at the end of each iteration, which significantly reduces the computational time due to fewer iterations being needed. Similarly, A MIP model integrating logic-expressed heuristic rules was reported by [PERSON] [256] to simplify the model of the scheduling refinery problem as well as to improve the efficiency of the solution without losing optimization guaranteed. [PERSON] et al. [257] first proposed a heuristic rule of the oil transport sequence. Then an asynchronous continuous-time CO model based on a line-up competition algorithm was built to transform the original refinery crude oil scheduling problem (RCSP), a MINLP, into a CO problem that makes it easier to obtain the optimal solution, and then solve it within a relatively short computational time. The total cost of scheduling, the objective function, is proved to reduce by 2.1% compared to other approaches mentioned. To solve the crude oil operation scheduling problem for refineries, which is a multi-objective optimization problem, an NSGA-III based method was proposed by [PERSON] et al. [258]. As a meta-heuristic algorithm, NSGA-III improves population diversity by adaptively updating reference points compared to NSGA-II. There are some sources involving the use of other techniques. [PERSON] et al. [259] presented a MILP clustering mechanism to cope with the operational management of the crude oil supply (OMCOS) problem consisting of the scheduling of vessel traveling between a terminal and platforms. The authors used the clustering solution as a pre-step to simplify operations and reduce the total number of vessel routes. [PERSON] et al. [260] proposed a discretization-based algorithm which can approximate non-convex mixed-integer quadratically constrained programming (MIQCP) as a MILP. To solve the tank blending and scheduling problem, the authors combined the algorithm with a rolling horizon approach, which is evaluated to be supportive of using industrial datasets.