【正文】 m in multiproject environment with resources constrained. Reference [1] designed an iterative algorithm and proposed a mathematical model of the resourceconstrained multiproject scheduling .Based 建筑大學(xué)畢業(yè)設(shè)計外文文獻及譯文 2 on work breakdown structure (WBS) and DantzigWolfe deposition method ,a feasible multiproject planning method was illustrated , as in [2] . References [3,4] discussed the resourceconstrained project scheduling based on Branch Delimitation method .Reference [5] put forward the framework of human resource allocation in multiproject in Longterm ,mediumterm and shortterm as well as research and development(Ramp。 (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 . 4) Standard for the terminal of GA Without human control ,the evolution process of the algorithm will never end .The population size affects the final result and the operation speed .If the size is greater ,the diversity of the population can be added ,and the best result can be obtained easier .However ,the efficiency is reduced .Recently ,in most GA progress , the biggest evolvement algebra is determined by humanbeings to control the course the algorithm. 建筑大學(xué)畢業(yè)設(shè)計外文文獻及譯文 8 5. NUMERICAL EXAMPLE We use a numerical example to illustrate the effectiveness of Geic Algorithm . Assume that there are three projects with the same work ,and the priority weights have been put forward .There is only one critical path in each project . The data we have known are shown in Table 1. Table 1 Data of the Three Projects Project Priority weight w tE Cost loss(human yuan/day) Workload (person*day) 1 10 100 100 2 8 150 80 3 12 80 120 The steps of Geic Algorithm to solve the model are as follow: Step1: An integer string is adopted .Encode with [0,1,2] for there are three projects .The length of the chromosome is 16 ,the total number of human resource to be allocated . Step 2: The initial population size is 50. Step 3: Doing geic operation .Adopt Roulette Wheel and Elitist tactic to determined selection operator .The offspring can be yielded by uniform crossover .The mutation operator can be determined by uniform mutation .We assume that the mutation probability equal to . Step 4: Adopt the maximum population size is 100 when terminated. After the puter simulation, we can obtain the Pareto results with different importance weights of the two objective functions, as shown in Table 2 : Table 2 The Solution Result of the Model R1* R2* R3* F1(Hundred Yuan) F2(Day) α=1,β=0 6 5 5 α=,β= 7 5 4 α=,β= 8 4 4 建筑大學(xué)畢業(yè)設(shè)計外文文獻及譯文 9 α=,β= 10 3 3 0 From table 2 we can learn that , when α and β change ,the result is different .However we can obtain a series of Pareto results. 6. CONCLUSION Human resource allocation in multiproject environment is a plicated problem .This paper analyzes the importance of project’s priority in resource allocation and establishes a human resource allocation model based on priority and cost of projects .Finally, geic Algorithm is adop