The Research Of Multi-state Multi-resource Leveling Optimization Based On Genetic Optimization

Resources are important factors which affect a project's schedule, duration and cost. Success or failure of resources planning will result the project's realization of progress and investment objectives or not, especially for large projects. "Duration Fixed - Resource leveling" optimization is one of the resource optimization issues that should be solved. Thus, this paper, combining the practical engineering application, for the previous assumptions of "fixed duration - resource leveling" Optimization Study, put forward multi-state and multi-stage concepts and theories.In the study multi-resource leveling optimization, this article re-examine the project resources' connotation and extension, in accordance with the theory of factors of production. Through the dialysis understanding of resources, a new perspective is developed in the leveling optimization of resources.According to engineering practice, this paper studies the multi-state multi-stage multi-resource leveling optimization, build optimization model and apply genetic optimization algorithms (GO) to solve this problem .This study includes:The first chapter describes the issues raised and introduction of the research status. By analyzing the current network resources leveling optimization research status and the development trend, this paper points out the imperfections in this study. Based on it, thisarticle puts forward the subject of this paper——the research of multi-state multi-stagemulti-resources leveling optimization in network planning based on genetic optimization algorithm.The second chapter describes the basic theory of resource leveling optimization and evolutionary optimization algorithm and its theoretical basis.The third chapter studies the multi-state multi-stage multi-resource and dynamic control theory.ChapterⅣestablishes resource leveling optimization model and the process of the genetic optimization algorithm. From the algorithm proposed, the basic principle, model selection to make an introduction. And by comparing with other genetic algorithm, this paper obtains (GO) the advantages for solving the resource leveling optimization problem in network planning, and the project multi-objective optimization value and great potential development.ChapterⅤselects an example and realizes programming. Select an instance and apply MATLAB to programming solution. Then adoption of a specific engineering example verifies the effectiveness and practicability of the model. ChapterⅥsummarizes the paper's main content and analysis. This article lists the main tasks and achievements, meanwhile conducts an analysis and prospects against the paper's Imperfections.