Pareto-Optimization for Scheduling of Crude Oil Operations in Refinery via Genetic Algorithm
研究炼油厂原油调度中离散与连续过程交互的优化问题，通过将问题转化为储罐和蒸馏器的分配问题，并首次采用改进的非支配排序遗传算法求解，工业案例验证了方法的有效性。
With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems.