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建设期利息和物价浮动在核电站工程投资中占有很大的比例。为优化工程投资，提出了以最大净现值为目标的核电站投资优化数学模型。该模型基于工程的活动网络且是ＮＰ问题。针对该模型给出了一种启发式遗传算法（ＨＧＡｓ）。在该算法中，解是一串表示活动分配资源优先级的数，这种编码方法克服了传统遗传算法求解该问题时难以找到可行解的困难。本文提出的前件矩阵的概念能有效地求解活动网络的关键路径。用Ｃ语言编制了启发式遗传算法程序（ＨＧＡＰ），并用该程序求解了一个实例。计算结果表明该模型符合工程实际，该算法能有效解决该模型。
Interest rates and price fluctuations in the construction period accounted for a large proportion of nuclear power plant project investment. In order to optimize the project investment, a mathematical model of NPP investment optimization with maximum NPV is proposed. The model is based on the engineering activity network and is an NP problem. A heuristic genetic algorithm (HGAs) is given for the model. In this algorithm, the solution is a string of numbers representing the priorities of resources allocated by the activity. This coding method overcomes the difficulty of finding feasible solutions when the traditional genetic algorithm is solving the problem. The concept of antecedent matrices proposed in this paper can effectively solve the critical path of an active network. Heuristic genetic algorithm program (HGAP) was compiled in C language, and an example was solved by this program. The calculation results show that the model is in accordance with engineering practice, and the algorithm can effectively solve the model.