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Highlights • A novel two-stage modeling framework based on event sequence is developed. • A SIASA algorithm is presented to re-optimize the solution of first-stage model. • A data-driven method is proposed to accomplish the fast optimization of pipeline scheduling. • The experimental results of 50 cases are given to illustrate the superiority of the proposed method. Abstract The detailed scheduling of distributing products through pipelines is one of the most essential tasks in the management of multiproduct pipelines. The current methods of scheduling optimization are still limited by the dilemma between optimality and efficiency, especially when dealing with real-world scheduling issues. This paper introduces a two-stage mathematical model based on the event sequence, whose model scale greatly decreases after stripping the terms of event sequence. Then, a data-driven method is presented to learn a large number of existing scheduling data and accelerate the calculation of first-stage model. The method is decomposed into three parts: (1) take cold start to generate a good deal of basic training data; (2) train the neural network for the fast solution of first-stage model; (3) take real-world cases to improve the self-learning of neural network. A multiproduct pipeline in China is taken as the example to prove the practicability and superiority of the proposed method. The experimental results show that the proposed method could decrease the computation time from about several hours to several minutes. [ABSTRACT FROM AUTHOR]

Highlights • A MINLP model is developed for scheduling of branched multiproduct pipeline networks. • The model rigorously accounts for nonlinear hydraulic calculation associated with flowrate. • A priority algorithm based on continuity, urgency and compatibility is presented. • Three cases are given to validate the high efficiency of the proposed method. Abstract This paper aims at the detailed scheduling of branched multiproduct pipeline networks with multiple sources and an output terminal. Nevertheless, the complexity of the pipeline structure, coupling of hydraulic and scheduling factors, impacts of physical properties of products, and simultaneous injections at different sources make it challenging to draw out the injection operations at each source. Based on a continuous-volume and discrete-time representation, this paper develops a MINLP model to find the optimal injection operations of all sources at minimum total operational cost, rigorously tracking batch migration and power consumption at each pipeline segment. A priority algorithm is presented to obtain a high-quality solution of this large-scale and nonlinear scheduling problem. Finally, experimental results on two virtual pipeline networks and a real-world pipeline network in China are given to validate the proposed approach. [ABSTRACT FROM AUTHOR]

• A hybrid-time MILP model is developed for the low-energy scheduling of straight pipelines. • The proposed model can be reformulated as adjusting model to improve solving efficiency. • An efficient decomposition approach that integrates heuristics and MILP models is proposed. • High-quality schedules are obtained by the proposed approach with the shorter time. This paper proposes a hybrid-time mixed integer linear programming model for low-energy scheduling of single-source multiproduct pipelines. The model divides the time horizon into slots by event points of batches reaching stations and fixed points of electricity price changing. Through this division, some practical factors are easily modelled as linear items, including time-varying electricity price and flowrate limits related to interfaces. The key technology for this model is the initialization of all time points' sequence, so a decomposition approach is developed to efficiently seek satisfactory sequence of all time points. Its superiority is validated by two real-world cases. [ABSTRACT FROM AUTHOR]

• A discrete-time MOMILP model for pump scheduling of products pipelines is built. • Various factors about pump operation are considered comprehensively in the model. • A two-stage strategy is adopted to deal with the proposed model. • Two cases from a products pipeline system in China illuminate model's practicability. As one of the major means to link refineries to local markets, pipelines are crucial for refined oil supply chains. Pump scheduling is vital to optimally operate the refined products pipelines. Existing studies mostly quantified the number of pump stop/restart in the form of cost, whereas the cost coefficient was often subjectively selected. The present study built a multi-objective mixed-integer linear programming (MOMILP) model for refined products pipelines to minimize the number of pump stop/restart and reduce the pump running cost simultaneously. In this study, the minimum continuous running time of pumps, changing electricity price (i.e., time-changing electricity price and local electricity) and the pressure limits of special points were considered meticulously, e.g. high-elevation points, low-elevation points and pump stations. In the first stage, the improved augmented ε-constraint method (AUGMECON) was adopted to deal with this model, and obtained a seris of pump operating schemes, acting as a Pareto set. Then the results of the AUGMECON were evaluated by an established evaluation model based on neural network. Finally, two cases from a refined products pipeline system in China were employed to verify the practicability and accuracy of the proposed model. The results of this study can effectively guide the pump scheduling of refined products pipelines. [ABSTRACT FROM AUTHOR]

• An MILP model for DOSC planning is developed. • Distribution plan and new pipeline route plan are optimized simultaneously. • Refined oil transportation from SDs to SDs is considered. • A real-world application of DOSC planning in China is studied. The planning of downstream oil supply chains (DOSCs) is important to guarantee that the demand of retail markets (RMs) can be satisfied. As an economic method for refined oil transport, pipelines are widely used in many DOSCs. The distribution cost can be reduced, and the efficiency can be improved by constructing new pipelines in a DOSC. In this paper, an MILP model that optimises a DOSC with new pipeline route planning is developed. Constraints for refined oil distribution and pipeline route planning are proposed. The model also considers the transport between storage depots (SDs) and makes it practical for the real-world application of a DOSC in China. Based on the current DOSC, the routes of new pipelines and supply chain planning after new pipeline construction can be obtained. This method is applied to a real DOSC case in China, and two situations are analysed. This method can also help decision makers conduct optimal pipeline route planning and create distribution plans for other DOSCs. [ABSTRACT FROM AUTHOR]

Highlights • A discrete-time MILP model is developed for multi-product pipelines in pressure control mode. • Pipeline scheduling and pump scheduling are optimized simultaneously by the model. • Hydraulic constraints associated with flowrate are linearized by piecewise linear approximation. • Two cases are given to illustrate the model’s superiority. Abstract Scheduling with hydraulic constraints is one of the crucial aspects to guarantee multi-product pipeline safety. This paper develops a discrete-time mixed-integer linear programming (MILP) model for a single-source multi-product pipeline in pressure control mode, minimizing the pump cost as well as the labor cost of pump stoppage/restart. Instead of limiting pressure through flowrate constraints, this paper adopts piecewise linear approximation to deal with pump characteristic curves and frictional loss associated to flowrate and establish corresponding pressure constraints. The pressure at all key points along the pipeline should be within the allowable range to ensure transport safety. In this way, the proposed model can attain the integrated optimization of the pipeline scheduling and pump scheduling, thereby avoiding the possible mismatch between pressure and flowrate obtained by two-step solving strategy. Finally, the proposed method is compared with another discrete-time MILP model (Chen et al., 2017) which sets the minimum pump rate variation as objective function for the operating economy. Two cases tested on a Chinese real-world pipeline are given to demonstrate that the optimal detailed schedules obtained by the proposed model perform better in both economy and the convenience of pump operations. [ABSTRACT FROM AUTHOR]