In this paper, a new Lagrange relaxation based decomposition algorithm for the integrated offshore oil production planning optimization is presented. In our previous study (Gao et al. Computers and Chemical Engineering, 2020, 133, 106674), a multiperiod mixed-integer nonlinear programming (MINLP) model considering both well operation and flow assurance simultaneously had been proposed. However, due to the large-scale nature of the problem, i.e., too many oil wells and long planning time cycle, the optimization problem makes it difficult to get a satisfactory solution in a reasonable time. As an effective method, Lagrange relaxation based decomposition algorithms can provide more compact bounds and thus result in a smaller duality gap. Specifically, Lagrange multiplier is introduced to relax coupling constraints of multi-batch units and thus some moderate scale sub-problems result. Moreover, dual problem is constructed for iteration. As a result, the original integrated large-scale model is decomposed into several single-batch subproblems and solved simultaneously by commercial solvers. Computational results show that the proposed method can reduce the solving time up to 43% or even more. Meanwhile, the planning results are close to those obtained by the original model. Moreover, the larger the problem size, the better the proposed LR algorithm is than the original model.

中文翻译：

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基于拉格朗日松弛的大规模海上采油计划优化分解算法

在本文中，提出了一种新的基于拉格朗日松弛的综合海上石油生产计划优化分解算法。在我们之前的研究中（Gao et al. Computers and Chemical Engineering, 2020, 133, 106674），已经提出了同时考虑井操作和流动保证的多周期混合整数非线性规划（MINLP）模型。但由于该问题的规模性大，即油井数量多，规划时间周期长，优化问题难以在合理的时间内得到满意的解。作为一种有效的方法，基于拉格朗日松弛的分解算法可以提供更紧凑的边界，从而导致更小的对偶间隙。具体来说，引入拉格朗日乘子来放松多批次单元的耦合约束，从而产生一些中等规模的子问题。此外，对偶问题是为迭代构造的。结果，原始集成的大规模模型被分解为几个单批次子问题，并由商业求解器同时求解。计算结果表明，所提出的方法可以将求解时间减少43%甚至更多。同时，规划结果与原始模型得到的结果接近。此外，问题规模越大，提出的 LR 算法优于原始模型。原始集成的大规模模型被分解为多个单批次子问题，并由商业求解器同时求解。计算结果表明，所提出的方法可以将求解时间减少43%甚至更多。同时，规划结果与原始模型得到的结果接近。此外，问题规模越大，提出的 LR 算法比原始模型越好。原始集成的大规模模型被分解为多个单批次子问题，并由商业求解器同时求解。计算结果表明，所提出的方法可以将求解时间减少43%甚至更多。同时，规划结果与原始模型得到的结果接近。此外，问题规模越大，提出的 LR 算法比原始模型越好。