PROJECT PORTFOLIO FORMATION BASED ON FUZZY MULTI-OBJECTIVE MODEL

被引:0
作者
Avdoshin, Sergey [1 ]
Lifshits, Alexey [1 ]
机构
[1] Natl Res Univ Higher Sch Econ, Fac Business Informat, Sch Software Engn, 20,Myasnitskaya Str, Moscow 101000, Russia
来源
BIZNES INFORMATIKA-BUSINESS INFORMATICS | 2014年 / 27卷 / 01期
关键词
project portofolio; multi-objective model; fuzzy numbers; genetic algorithm; ant colony optimization;
D O I
暂无
中图分类号
F [经济];
学科分类号
02 ;
摘要
Leading IT companies run simultaneously several dozens or even several hundreds of projects. One of the major objectives is to decide whether a project meets the current strategic goals and resource limits of a company or not. This leads firms to the issue of a project portfolio formation, where the challenge is to choose a subset of projects which meet the strategic objectives of a company in the best way. In this present article we propose a multi-objective mathematical model of the project portfolio formation problem, defined on the fuzzy trapezoidal numbers. We provide an overview of methods for solving this problem, which are a Branch and bound approach, an adaptive parameter variation scheme based on the epsilon-constraint method, ant colony optimization method and genetic algorithm. After our analysis, we choose the ant colony optimization method and SPEA II method, which is a modification of genetic algorithm. We describe the implementation of these methods applied to the project portfolio formation problem. The ant colony optimization is based on the max min ant system with one pheromone structure and one ant colony. Three modifications of our SPEA II implementation have been considered. The first adaptation uses the binary tournament selection, while the second requires the rank selection method. The last one is based on another variant of generating initial population. Part of the population is generated by a non-random manner on the basis of solving a one-criterion optimization problem. This fact makes the population stronger than the initial one which is generated completely at random. We compare the ant colony optimization algorithm and the three modifications of a genetic algorithm on the basis of the following parameters: speed of execution and the C-metric between each pair of algorithms. Genetic algorithm with non-random initial population show better results than other methods. Thus, we propose using this algorithm for solving project portfolio formation problem.
引用
收藏
页码:14 / 22
页数:9
相关论文
共 9 条
  • [1] Greedy algorithm for the general multidimensional knapsack problem
    Akcay, Yalcin
    Li, Haijun
    Xu, Susan H.
    [J]. ANNALS OF OPERATIONS RESEARCH, 2007, 150 (01) : 17 - 29
  • [2] Ant colony optimization for multi-objective optimization problems
    Alaya, Ines
    Solnon, Christine
    Ghedira, Khaled
    [J]. 19TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, VOL I, PROCEEDINGS, 2007, : 450 - 457
  • [3] Anshin V., 2008, ISSUES RISK ANAL, V3, P8
  • [4] Bastiani S. S., 2013, P EUR 4 INT WORKSH 2
  • [5] International Standardization Organization, 2013, 21500 ISO
  • [6] An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method
    Laumanns, M
    Thiele, L
    Zitzler, E
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2006, 169 (03) : 932 - 942
  • [7] Matveev A., 2005, MODELI METODY UPRAVL
  • [8] Genetic algorithm-based multi-criteria project portfolio selection
    Yu, Lean
    Wang, Shouyang
    Wen, Fenghua
    Lai, Kin Keung
    [J]. ANNALS OF OPERATIONS RESEARCH, 2012, 197 (01) : 71 - 86
  • [9] Zitzler E., 2001, P EUROGEN2001 C