建筑外文文献翻译--在项目优先权和成本的基础上对多项目中人力资源配置的研究(编辑修改稿)

建筑外文文献翻译--在项目优先权和成本的基础上对多项目中人力资源配置的研究(编辑修改稿)内容摘要：

riorities may not be fully guaranteed. In this situation, the decrease of the resource supply will lead to the increase of the duration time of activities and the project, while the workload is fixed. Optimization Model Based on the above hypotheses, the resource allocation model in multiproject environment can be established .Here, the optimization model is : Fi=min Zi = min Ni iNi Ci11  =miniiNi iNi ct   11  (2) =min Ni iNi 11  EiRikii t1 ic 2F =min Z2=min it =min EiRikii t1 (3) Where wj=max(wi) ,( Nji 3,2,1,  ) (4) Subject to : 0 Rk kiNi 11 =R (5) The model is a multiobjective one .The two objective functions are respectively to minimize the total cost loss ,which is to conform to the economic target ,and to shorten the time delay of the project with highest 建筑大学毕业设计外文文献及译文 6 priority .The first objective function can only optimize the apparent economic cost。 therefore the second objective function will help to make up this limitation .For the project with highest priority ,time delay will damage not only the economic benefits ,but also the strategy and the prestige of the enterprise .Therefore we should guarantee that the most important project be finished on time or ahead of schedule . 4. SOLUTION TO THE MULTIOBJECTIVE MODEL USING GENETIC ALGORITHM The multiobjective optimization problem is quite mon .Generally ,each objective should be optimized in order to get the prehensive objective optimized .Therefore the weight of each subobjective should be considered .Reference [8] proposed an improved ant colony algorithm to solve this problem .Supposed that the weights of the two optimizing objectives are α and β ,where α+β=1 .Then the prehensive goal is F* ,where F*=αF1+βF2. The Principle of Geic Algorithm Geic Algorithm roots from the concepts of natural selection and geics .It’s a random search technique for global optimization in a plex search space .Because of the parallel nature and less restrictions ,it has the key features of great currency ,fast convergence and easy calculation .Meanwhile ,its search scope is not limited ,so it’s an effective method to solve the resource balancing problem ,as in [9]. The main steps of GA in this paper are as follow: (1) Encoding An integer string is short, direct and efficient .According to the characteristics of the model, the human resource can be assigned to be a code object .The string length equals to the total number of human resources allocated. (2) Choosing the fitness function This paper choose the objective function as the foundation of fitness 建筑大学毕业设计外文文献及译文 7 function .To rate the values of the objective function ,the fitness of the nth individual is 1/ n。 (3) Geic operation It’s the core of GA .This process includes three basic operators: selection operator, crossover operator, and mutation operation. 1) Selection operation is to select the good individuals among the group .The probability of a string to be selected as a parent is proportional to its fitness .The higher the string’s fitness is, the greater the probability of the string to be selected as a parent will be. 2) Crossover operator The socalled crossover is that the paten chromosomes exchange some genes to yield two offspring strings in some rule .We can use uniform crossover ,that the two chromosomes exchange the genes on the same positions with the same crossover probability to yield two new individuals. 3) Mutation operator Mutation adds to the diversity of a population and thereby increases the likelihood that the algorithm will generate individuals with better fitness values .The mutation operator determines the search ability of GA ,maintain the diversity of a population ,and avoid the prematurity .There are several mutation is quite easy .。