基于混合遗传算法的工期费用优化研究

发布时间：2018-05-31 07:03

本文选题：遗传算法 + 蚁群算法　；参考：《大连理工大学》2015年硕士论文

【摘要】：在工程项目管理中,合理地安排进度和压缩成本能够拓展可得利润的空间,为项目带来可观的经济效益。传统的工期费用优化方法如常用的数学规划法、启发式算法等都存在着一定的缺陷性。而混合算法因其计算高效、结果优良的特性在近几年引起学者们的关注,并在多个领域取得良好的效果。本文在前人研究的基础上,针对传统方法存在的一些问题,利用混合遗传算法在双目标组合优化方面展现的独特优势,将遗传算法和蚁群算法进行混合应用于网络计划的工期费用优化中。为实现工期和费用同时优化的目标,利用遗传算法大范围全局搜索的优点以及蚁群算法正反馈性、求精解效率高等特征进行两种算法的混合交叉衔接工作。对活动的持续时间和直接费用为连续性函数关系的费用优化问题进行重点研究,将混合算法的所得结果分别与传统算法和蚁群算法的所得结果进行分析与比较,结果表明本文所用的混合算法在关键路径上的寻找更高效,所得的工期结果也更合理和准确。在一定程度上证明了其数学优化模型的实际应用可行性,为工期费用优化提供了一种新的思路和解决问题的途径。
[Abstract]:In the engineering project management, reasonable arrangement of schedule and reduction of cost can expand the available profit space and bring considerable economic benefits to the project. The traditional optimization methods such as mathematical programming and heuristic algorithms have some defects. The hybrid algorithm has attracted the attention of scholars in recent years because of its high efficiency and excellent results, and has achieved good results in many fields. In this paper, based on the previous studies, the unique advantages of hybrid genetic algorithm (HGA) in dual-objective combinatorial optimization are presented in view of some problems existing in traditional methods. Genetic algorithm (GA) and ant colony algorithm (ACA) are used to optimize the cost of network planning. In order to achieve the goal of time limit and cost optimization at the same time, the hybrid crossover of the two algorithms is carried out by using the advantages of genetic algorithm (GA) in large scale global search and the positive feedback of ant colony algorithm (ACA). This paper focuses on the cost optimization problem in which the duration and direct cost of activities are continuous function. The results of hybrid algorithm are analyzed and compared with those of traditional algorithm and ant colony algorithm, respectively. The results show that the hybrid algorithm used in this paper is more efficient in finding critical paths, and the obtained results are more reasonable and accurate. To a certain extent, the practical application feasibility of its mathematical optimization model is proved, which provides a new way of thinking and solving the problem for the optimization of construction period and cost.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TU72

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