The International Journal of Advanced Manufacturing Technology

, Volume 67, Issue 9, pp 2109–2125

Green partner selection in virtual enterprise based on Pareto genetic algorithms

Authors

  • Yue Zhang
    • School of Automation Science and Electrical EngineeringBeihang University
    • School of Automation Science and Electrical EngineeringBeihang University
  • Yuanjun Laili
    • School of Automation Science and Electrical EngineeringBeihang University
  • Baocun Hou
    • Beijing Simulation Center
  • Lin Lv
    • School of Automation Science and Electrical EngineeringBeihang University
  • Lin Zhang
    • School of Automation Science and Electrical EngineeringBeihang University
ORIGINAL ARTICLE

DOI: 10.1007/s00170-012-4634-x

Cite this article as:
Zhang, Y., Tao, F., Laili, Y. et al. Int J Adv Manuf Technol (2013) 67: 2109. doi:10.1007/s00170-012-4634-x

Abstract

The partner selection problem (PSP) in virtual enterprise has been comprehensively investigated from the aspects of research fields, contents, attributes or criteria been considered, and algorithms. With the consideration of environmental protection, the importance of “green criteria” in PSP is introduced, and two new green criteria, i.e., carbon emission and lead content in manufacturing production, are firstly brought into PSP. A formulation of PSP with green criteria is established which includes four objectives and six constraints. A new improved algorithm, named Pareto genetic algorithm for PSP (Pareto-PSGA), is designed for addressing the specific PSP. With Pareto solution ideas, vector encoding, random selection, two-point crossover, and single-point mutation for Pareto solutions are designed in the Pareto-PSGA. Experimental results demonstrate that compared with other typical intelligent algorithms such as simulated annealing and particle swarm optimization, Pareto-PSGA shows high performance in solving the specific PSP with more suitable Pareto solutions in shorter time.

Keywords

Partner selection Virtual enterprise Pareto solutions Genetic algorithm

Copyright information

© Springer-Verlag London 2012