Crude oil operations in refineries are characterized as a hybrid system since it contains both discrete-event and continuous processes, and it is extremely difficult to schedule such a system. For scheduling such a system, initially the discrete tasks to be performed during the scheduling horizon is unknown such that heuristics and meta-heuristics are not directly applicable, which further complicates its scheduling problem. Moreover, there are large number of objectives to be optimized, including the minimization of energy consumption due to that crude oil operations consume large amount of energy and therefore lead to large amount of emissions. Furthermore, the energy optimization problem is characterized as highly non-linearity. Hence, the scheduling problem of crude oil operations in refineries belongs to the many-objective optimization problems and it is extremely challenging. This paper addresses this challenging scheduling problem of crude oil operations. This scheduling problem is first converted to a discrete dynamic resource allocation problem such that meta-heuristics can be applicable. Then, with the results of large number of experiments, this work innovatively proposes an NSGA-Ⅲ-based optimization algorithm to efficiently solve the problem for Pareto-optimal solutions. By the proposed method, the genes in a chromosome are generated one by one and, when generating a gene, safeness check is done according to the derived safeness conditions such that each gene is feasible. In this way, the schedule feasibility can be ensured. An industrial case study is given to test its performance and comparison is made with the existing state-of-the-art algorithms for many-objective optimization problems. The results show its good performance in terms of convergence, solution diversity, time efficiency, and its applicability to real-life refinery scheduling problems.

炼油厂的原油操作具有混合系统的特点，因为它既包含离散事件又包含连续过程，并且调度此类系统非常困难。为了调度这样的系统，最初在调度范围内要执行的离散任务是未知的，使得试探法和元试探法不能直接应用，这进一步使其调度问题变得复杂。此外，还有许多目标需要优化，包括使能源消耗最小化，这是由于原油运营消耗大量能源并因此导致大量排放。此外，能量优化问题的特征在于高度非线性。因此，炼油厂原油作业的调度问题属于多目标优化问题，具有极大的挑战性。本文解决了原油作业这一具有挑战性的调度问题。首先将该调度问题转换为离散的动态资源分配问题，以便可以应用元启发式方法。然后，结合大量实验的结果，这项工作创新地提出了一种基于NSGA-Ⅲ的优化算法，可以有效地解决帕累托最优解的问题。通过提出的方法，染色体中的基因被一个接一个地生成，并且在生成基因时，根据导出的安全性条件进行安全性检查，使得每个基因都是可行的。这样，可以确保进度的可行性。给出了一个工业案例研究以测试其性能，并与现有的针对多目标优化问题的最新算法进行了比较。结果表明，它在收敛性，解决方案多样性，时间效率以及对实际炼油厂调度问题的适用性方面均具有良好的性能。