# A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas
## 4 Applications in Energy Field
### Petroleum Supply Chain
#### 4.1.3 Oil Transportation  
Now it is time to focus on the intermediate operation that connects upstream and downstream of the petroleum supply chain: oil transportation. The oil transportation system consists of several methods among crude oil fields, refineries, storage terminals, and sales companies: pipeline, trucks, railway, and tankers [106]. The determination is made with respect to different factors, such as distance, oil type, cost, etc. In this section, applications to problems concerning pipeline scheduling were mainly investigated.  
Oil transportation through pipeline usually involves multi-product scheduling problems. [PERSON] et al. [10] introduced a MILP-based continuous-time approach for the long-term multi-product pipeline scheduling problem, which is a MINLP. Compared to work performed by [PERSON] et al., the formulation proposed by [PERSON] et al. allows 25% less total operation cost. Focusing on the same topic, [PERSON] et al. [12] also presented their work of a continuous-time MILP to address the scheduling of multi-product treelike pipeline scheduling consisting of a single refinery and several downstream depots. The proposed model is proved to be significantly more computationally efficient. Like [PERSON] et al., who focused on pipeline scheduling between a single refinery and multiple terminals, [PERSON] et al. [11] also concentrated on multiple pump stations, which is a crucial structure for long-distance pipelines. The authors presented a hybrid computational approach embedding SM into ACO. Therefore, with such an architecture, two sub-models are more interactive than other multi-step models in previous literary works.  
Now, the reader may look at other approaches. [PERSON] et al. [264] presented a two-stage stochastic MILP model to determine the scheduling of pipeline-transported oil and sequence of ships at the terminal, which is a system with supply uncertainty. [PERSON] et al. [263] developed a continuous-time scheduling model embedding an outer-approximation-based iterative algorithm to tackle the long-distance pipeline transportation problem. [PERSON] et al. [266] proposed an improved variable neighborhood search algorithm (IVNS) for scheduling refined oil transportation with multiple trips and due times. To improve local optimization ability, a greedy strategy is built based on the initial solution obtained by the forwarding insertion heuristic. Therefore, the results converge faster without losing the quality of the solution. [PERSON] et al. [107] constructed an adaptive large neighborhood search (ALNS) heuristic to cope with the fuel-replenishment problem (FRP), a VRP with multiple deliveries, trips, and compartments, where different products are involved. [PERSON] [265] proposed an improved simulated annealing (ISA) algorithm incorporating parallel computation, heuristic rules, and adaptive search to tackle refined oil pipeline transportation problems in order to optimize computation and special constraints. To simplify the structure, the author presented an approach that separates one pipeline transportation scheduling problem into initial-station input scheduling and sub-problems of distribution scheduling at each station along the pipeline. The resulting scheduling plan was proved to be efficient and effective applying to a real instance. Based on previous research of a petroleum products distribution system, where an object-oriented Petri nets (OOPN) framework is proposed, [PERSON] et al. [267] presented a queue theory to improve the safety of the existing model, for the OOPN model only shows boundedness and no deadlock without evidence of safety. The improved model was shown to be effective.  
So far, the references surveyed in this section were all focused on treelike pipelines with multiple destinations, i.e., the models were built on a single refinery concerning several terminals, while in reality, the pipeline for oil transportation would be more like a complicated network. With such complexity, whether the current algorithm would still be competent is worth questioning.