投资优化模型及其启发式遗传算法

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。

Cost optimization model and its heuristic genetic algorithms

Interest and escalation are large quantity in proportion to the cost of nuclear power plant construction. In order to optimize the cost, the mathematics model of cost optimization for nuclear power plant construction was proposed, which takes the maximum net present value as the optimization goal. The model is based on the activity networks of the project and is an NP problem. A heuristic genetic algorithms (HGAs) for the model was introduced. In the algorithms, a solution is represented with a string of numbers each of which denotes the priority of each activity for assigned resources. The HGAs with this encoding method can overcome the difficulty which is harder to get feasible solutions when using the traditional GAs to solve the model. The critical path of the activity networks is figured out with the concept of predecessor matrix. An example was computed with the HGAP programmed in C language. The results indicate that the model is suitable for the objectiveness, the algorithms is effective to solve the model.