Green partner selection in virtual enterprise based on Pareto genetic algorithms

  • Yue Zhang
  • Fei Tao
  • Yuanjun Laili
  • Baocun Hou
  • Lin Lv
  • Lin Zhang
ORIGINAL ARTICLE

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 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Feng B, Fan Z-P, Ma J (2010) A method for partner selection of codevelopment alliances using individual and collaborative utilities. Int J Prod Econ 124:159–170CrossRefGoogle Scholar
  2. 2.
    Emden Z, Calantone RJ, Droge C (2006) Collaborating for new product development: selecting the partner with maximum potential to create value. J Prod Innov Manag 23(4):330–341CrossRefGoogle Scholar
  3. 3.
    Afonso P, Nunes M, Paisana A, Braga A (2008) The influence of time-to-market and target costing in the new product development success. Int J Prod Econ 115(2):559–568CrossRefGoogle Scholar
  4. 4.
    Cowan R, Jonard N, Zimmermann JB (2007) Bilateral collaboration and the emergence of innovation networks. Manag Sci 53(7):1051–1067CrossRefGoogle Scholar
  5. 5.
    Bremer CF, Eversheim W (2000) From an opportunity identification to its manufacturing: a references model for virtual manufacturing. CIRP Ann Manuf Technol 49(1):325–329CrossRefGoogle Scholar
  6. 6.
    Chen SH, Lee HT, Wu YF (2008) Applying ANP approach to partner selection for strategic alliance. Manag Decis 46(3):449–465CrossRefGoogle Scholar
  7. 7.
    Ye F (2010) An extended TOPSIS method with interval-valued intuitionistic fuzzy numbers for virtual enterprise partner selection. Expert Syst Appl 75:7050–7055CrossRefGoogle Scholar
  8. 8.
    Drissen-Silva MV, Rabelo RJ (2009) A collaborative decision support framework for managing the evolution of virtual enterprises. Int J Prod Res 47(17):4833–4854MATHCrossRefGoogle Scholar
  9. 9.
    Niu SH, Ony SK, Nee AYC (2012) An enhanced ant colony optimiser for multi-attribute partner selection in virtual enterprises. Int J Prod Res 50(8):2286–2303CrossRefGoogle Scholar
  10. 10.
    Rocha AP, Oliveira E (1999) An electronic market architecture for the formation of virtual enterprises. In: Proceedings of the IFIP TC5 WG5.3/PRODNET Working Conference on Infrastructures for Virtual Enterprises: Networking Industrial Enterprises, 27–28 October, Porto, Portugal, pp. 421–432Google Scholar
  11. 11.
    Wu N, Su P (2005) Selection of partners in virtual enterprise paradigm. Robot Comput Integr Manuf 21(2):119–131CrossRefGoogle Scholar
  12. 12.
    Chang SL, Wang RC, Wang SY (2006) Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle. Int J Prod Econ 100(2):348–359CrossRefGoogle Scholar
  13. 13.
    Wang TC, Chen YH (2007) Applying consistent fuzzy preference relations to partnership selection. Omega 35(4):384–388CrossRefGoogle Scholar
  14. 14.
    Zeng ZB, Li Y, Zhu WX (2006) Partner selection with a due date constraint in virtual enterprises. Appl Math Comput 175(2):1353–1365MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Ye F, Li YN (2009) Group multi-attribute decision model to partner selection in the formation of virtual enterprise under incomplete information. Expert Syst Appl 36(5):9350–9357CrossRefGoogle Scholar
  16. 16.
    Jarimo T, Salo A (2009) Multicriteria partner selection in virtual organizations with transportation costs and other network interdependencies. IEEE Trans Syst Man Cybern Part C Appl Rev 39(1):124–129CrossRefGoogle Scholar
  17. 17.
    Tao F, Qiao K, Zhang L, Li Z, Nee AYC (2012) GA-BHTR: an improved genetic algorithm for partner selection in virtual manufacturing. Int J Prod Res 50(8):2079–2100CrossRefGoogle Scholar
  18. 18.
    Ding JF, Liang GS (2005) Using fuzzy MCDM to select partners of strategic alliances for liner shipping. Inf Sci 173(1–3):197–225MATHCrossRefGoogle Scholar
  19. 19.
    Huang JJ, Chen CY, Liu HH, Tzeng GH (2010) A multi-objective programming model for partner selection-perspectives of objective synergies and resource allocations. Expert Syst Appl 37:3530–3536CrossRefGoogle Scholar
  20. 20.
    Liou J, Tzeng G, Tsai C, Hsu C (2011) A hybrid ANP model in fuzzy environments for strategic alliance partner selection in the airline industry. Appl Soft Comput 11:3515–3524CrossRefGoogle Scholar
  21. 21.
    Famuyiwa O, Monplaisir L, Nepal B (2008) An integrated fuzzy-goal-programming-based framework for selecting suppliers in strategic alliance formation. Int J Prod Econ 113(2):862–875CrossRefGoogle Scholar
  22. 22.
    Mukherjee A, Kwon HM (2010) General auction-theoretic strategies for distributed partner selection in cooperative wireless networks. IEEE Trans Commun 58(10):2903–2915CrossRefGoogle Scholar
  23. 23.
    Baum JAC, Cowan R, Jonard N (2010) Network-independent partner selection and the evolution of innovation networks. Manag Sci 56(11):2094–2110CrossRefGoogle Scholar
  24. 24.
    Hajidimitriou YA, Georgiou AC (2002) A goal programming model for partner selection decisions in international joint ventures. Eur J Oper Res 138(3):649–662MATHCrossRefGoogle Scholar
  25. 25.
    Huang XG, Wong YS, Wang JG (2004) A two-stage manufacturing partner selection framework for virtual enterprises. Int J Comput Integr Manuf 17(4):294–304CrossRefGoogle Scholar
  26. 26.
    Fischer M, Jahn H, Teich T (2004) Optimizing the selection of partners in production networks. Robot Comput Integr Manuf 20(6):593–601CrossRefGoogle Scholar
  27. 27.
    Amid A, Ghodsypour SH, Brien CO (2006) Fuzzy multiobjective linear model for supplier selection in a supply chain. Int J Prod Econ 104(2):394–407CrossRefGoogle Scholar
  28. 28.
    Deans I (1999) An approach to the environment management of purchasing in the utilities sector. Eco-Manage 6(1):11–17Google Scholar
  29. 29.
    Crispima JA, de Sousab JP (2010) Partner selection in virtual enterprises. Int J Prod Res 48(3):683–707CrossRefGoogle Scholar
  30. 30.
    Saen RF (2007) Suppliers selection in the presence of both cardinal and ordinal data. Eur J Oper Res 183(2):741–747MATHCrossRefGoogle Scholar
  31. 31.
    Sari B, Sen T, Kilic SE (2008) AHP model for the selection of partner companies in virtual enterprises. Int J Adv Manuf Technol 38(3–4):367–376CrossRefGoogle Scholar
  32. 32.
    Chan FTS, Kumar N, Tiwari MK, Lau HCW, Choy KL (2008) Global supplier selection: a fuzzy-AHP approach. Int J Prod Res 46(14):3825–3857MATHCrossRefGoogle Scholar
  33. 33.
    Ip WH, Yung KL, Dingwei W (2004) A branch and bound algorithm for sub-contractor selection in agile manufacturing environment. Int J Prod Econ 87(2):195–205CrossRefGoogle Scholar
  34. 34.
    Wang ZJ, Xu XF, Zhan DC (2009) Genetic algorithm for collaboration cost optimization-oriented partner selection in virtual enterprises. Int J Prod Res 47(4):859–881CrossRefGoogle Scholar
  35. 35.
    Bu Y, Zhou W, Yu J (2008) A discrete PSO algorithm for partner selection of virtual enterprise. In: The 2nd International Symposium on Intelligent Information Technology Application. Shanghai, China: IEEE, 814–817Google Scholar
  36. 36.
    Crispim JA, de Sousa JP (2009) Partner selection in virtual enterprises: a multi-criteria decision support approach. Int J Prod Res 47(17):4791–4812MATHCrossRefGoogle Scholar
  37. 37.
    Tao F, Zhao D, Hu Y, Zhou Z (2010) Correlation-aware resource service composition and optimal-selection in manufacturing grid. Eur J Oper Res 201(1):129–143MATHCrossRefGoogle Scholar
  38. 38.
    Tao F, Zhang L, Zhang ZH, Nee AYC (2010) A quantum multi-agent evolutionary algorithm for selection of partners in a virtual enterprise. CIRP Ann Manuf Technol 59(1):485–488CrossRefGoogle Scholar
  39. 39.
    Yeh WC, Chuang MC (2011) Using multi-objective genetic algorithm for partner selection in green supply chain problems. Expert Syst Appl 38:4244–4253CrossRefGoogle Scholar
  40. 40.
    Rezaei J, Davoodi M (2011) A joint pricing, lot-sizing, and supplier selection model. Int J Prod Res 50(16):4524–4542CrossRefGoogle Scholar
  41. 41.
    Tavakkoli-Moghaddam R, Azarkish M, Sadeghnejad-Barkousaraie A (2011) Solving a multi-objective job shop scheduling problem with sequence-dependent setup times by a Pareto archive PSO combined with genetic operators and VNS. Int J Adv Manuf Technol 53:733–750CrossRefGoogle Scholar
  42. 42.
    Li JQ, Pan QK, Gao KZ (2011) Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int J Adv Manuf Technol 55:1159–1169CrossRefGoogle Scholar
  43. 43.
    Wang XJ, Gao L, Zhang CY, Shao XY (2010) A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem. Int J Adv Manuf Technol 51:757–767CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Yue Zhang
    • 1
  • Fei Tao
    • 1
  • Yuanjun Laili
    • 1
  • Baocun Hou
    • 2
  • Lin Lv
    • 1
  • Lin Zhang
    • 1
  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
  2. 2.Beijing Simulation CenterBeijingChina

Personalised recommendations