Back
 AJIBM  Vol.5 No.6 , June 2015
A Novel Evolutionary Algorithm with Neighborhood Search for Project Portfolios Optimization Problem
Abstract: This paper proposes a quantum-inspired evolutionary algorithm with neighborhood search (called QEANS) to solve the project portfolios optimization problem with limited multiple resources and bounded risks for each project portfolio. The decision concerns how to find an optimal or best assignment of projects to a set of project portfolios that maximizes the total profit. The studied problem is formulated by a 0-1 linear programming model, and a quantum-inspired evolutionary algorithm with neighborhood search is proposed to solve it. In specific, each problem solution is encoded by a Q-bits matrix, which is updated by quantum-rotation gate. In addition, crossover and mutation operators are integrated so as to increase the population diversity. Furthermore, an effective repairing procedure is proposed for dealing with the generated infeasible solutions. To prevent the local optimum, a specific neighborhood search procedure is also proposed. Randomly generated instances are used to test and justify the effectiveness of the proposed QEANS. The obtained results indicate that the proposed QEANS is effective.
Cite this paper: Lei, W. and Li, S. (2015) A Novel Evolutionary Algorithm with Neighborhood Search for Project Portfolios Optimization Problem. American Journal of Industrial and Business Management, 5, 396-403. doi: 10.4236/ajibm.2015.56040.
References

[1]   Sanwal, A. (2007) Optimizing Corporate Portfolio Management: Aligning Investment Proposals with Organizational Strategy. John Wiley & Sons, Inc., Hoboken. ISBN: 978-0-470-12688-2.

[2]   Project Management Institute (2013) A Guide to the Project Management Body of Knowledge: PMBOK Guide. 5th Edition, Project Management Institute. ISBN: 978-1-935589-67-9.

[3]   Archer, N.P. and Ghasemzadeh, F. (1999) An Integrated Framework for Project Portfolio Selection. International Journal of Project Management, 17, 207-216.
http://dx.doi.org/10.1016/S0263-7863(98)00032-5

[4]   Dickinson, M.W., Thornton, A.C. and Graves, S. (2001) Technology Portfolio Management: Optimizing Interdependent Projects over Multiple Time Periods. IEEE Transactions on Engineering Management, 48, 518-527.
http://dx.doi.org/10.1109/17.969428

[5]   Girotra, K., Terwiesch, C. and Ulrich, K.T. (2010) Valuing R & D Projects in a Portfolio: Evidence from the Pharmaceutical Industry. Management Science, 53, 1452-1466.
http://dx.doi.org/10.1287/mnsc.1070.0703

[6]   Chen, J.Q. and Askin, R.G. (2009) Project Selection, Scheduling and Resource Allocation with Time Dependent Returns. European Journal of Operational Research, 193, 23-34.
http://dx.doi.org/10.1016/j.ejor.2007.10.040

[7]   Coffin, M.A. and Taylor, B.W. (1996) R & D Project Selection and Scheduling with a Filtered Beam Search Approach. IIE Transactions, 28, 167-176.
http://dx.doi.org/10.1080/07408179608966262

[8]   Heidenberger, K. (1996) Dynamic Project Selection and Funding under Risk: A Decision Tree based MILP Approach. European Journal of Operational Research, 95, 284-298.
http://dx.doi.org/10.1016/0377-2217(95)00259-6

[9]   Badri, M.A., Davis, D. and Davis D. (2001) A Comprehensive 0-1 Goal Programming Model for Project Selection. International Journal of Project Management, 19, 243-252.
http://dx.doi.org/10.1016/S0263-7863(99)00078-2

[10]   Eilat, H., Golany, B. and Shtub, A. (2006) Constructing and Evaluating Balanced Portfolios of R & D Projects with Interactions: A DEA Based Methodology. European Journal of Operational Research, 172, 1018-1039.
http://dx.doi.org/10.1016/j.ejor.2004.12.001

[11]   Hey, T. (1999) Quantum Computing. Computers & Control Engineering Journal, 10, 105-112.
http://dx.doi.org/10.1049/cce:19990303

[12]   Narayanan, A. and Moore, M. (1996) Quantum-Inspired Genetic Algorithms. Proceedings of IEEE International Conference on Evolutionary Computation, Nagoya, 20-22 May 1996, 61-66.
http://dx.doi.org/10.1109/icec.1996.542334

[13]   Talbi, H., Draa, A. and Batouche, M. (2004) A New Quantum-Inspired Genetic Algorithm for Solving the Travelling Salesman Problem. 2004 IEEE International Conference on Industrial Technology, 3, 1192-1197.
http://dx.doi.org/10.1109/icit.2004.1490730

[14]   Han, K.-H. and Kim, J.-H. (2002) Quantum-Inspired Evolutionary Algorithm for a Class of Combinatorial Optimization. IEEE Transactions on Evolutionary Computation, 6, 580-593.
http://dx.doi.org/10.1109/TEVC.2002.804320

[15]   Zhao, Z., Peng, X., Peng, Y. and Yu, E. (2006) An Effective Constraint Handling Method in Quantum-Inspired Evolutionary Algorithm for Knapsack Problems. WSEAS Transactions on Computers, 5, 1194-1199.

[16]   Li, B. and Wang, L. (2007) A Hybrid Quantum-Inspired Genetic Algorithm for Multiobjective Flow Shop Scheduling. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 37, 576-591.
http://dx.doi.org/10.1109/TSMCB.2006.887946

[17]   Zheng, T. and Yamashiro, M. (2010) Solving Flow Shop Scheduling Problems by Quantum Differential Evolutionary Algorithm. International Journal of Advanced Manufacturing Technology, 49, 643-662.
http://dx.doi.org/10.1007/s00170-009-2438-4

 
 
Top