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A Study on the Optimization Model for the Project Portfolio Manpower Assignment Using Genetic Algorithm

유전자 알고리즘을 이용한 프로젝트 포트폴리오 투입인력 최적화 모델에 관한 연구

  • 김동욱 (서울과학기술대학교 IT정책전문대학원 산업정보시스템전공) ;
  • 이원영 (서울과학기술대학교 IT정책전문대학원 산업정보시스템전공)
  • Received : 2018.07.18
  • Accepted : 2018.10.08
  • Published : 2018.12.31

Abstract

Companies are responding appropriately to the rapidly changing business environment and striving to lead those changes. As part of that, we are meeting our strategic goals through IT projects, which increase the number of simultaneous projects and the importance of project portfolio management for successful project execution. It also strives for efficient deployment of human resources that have the greatest impact on project portfolio management. In the early stages of project portfolio management, it is very important to establish a reasonable manpower plan and allocate performance personnel. This problem is a problem that can not be solved by linear programming because it is calculated through the standard deviation of the input ratio of professional manpower considering the uniformity of load allocated to the input development manpower and the importance of each project. In this study, genetic algorithm, one of the heuristic methods, was applied to solve this problem. As the objective function, we used the proper input ratio of projects, the input rate of specialist manpower for important projects, and the equal load of workload by manpower. Constraints were not able to input duplicate manpower, Was used as a condition. We also developed a program for efficient application of genetic algorithms and confirmed the execution results. In addition, the parameters of the genetic algorithm were variously changed and repeated test results were selected through the independent sample t test to select optimal parameters, and the improvement effect of about 31.2% was confirmed.

Keywords

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Value Creation Framework

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Project, Program and Project Portfolio

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A Structure of Chromosome

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Design of Genetic Algorithm

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Genetic Algorithm Implementation Screen

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Output of the Best Chromosome

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Result of Genetic Algorithm Execution

Feature Comparison of Meta-Heuristics

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Criteria of Assigning Manpower

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Constants for Project Information

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Constants for Manpower

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Objective Function

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Criteria for Constraints

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Results of Population Size Test

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Results of Rate of Elite Preserving

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Results of Rate of Elite Preserving

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Results of Rate of Elite Preserving

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Comparison Traditional and GA

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