Skip to main content
SpringerLink
Log in

GA-BHTR for Partner Selection Problem

  • Chapter
  • First Online:
Configurable Intelligent Optimization Algorithm

Part of the book series: Springer Series in Advanced Manufacturing ((SSAM))

  • 1560 Accesses

Abstract

In this chapter, GA-BHTR (genetic algorithm maintained by using binary heap and transitive reduction) [1] for addressing partner selection problem (PSP) in a virtual enterprise [2] is introduced. Based on ordinary initialization, an improved binary heap strategy is configured before it with uniform population input and output to realize initialization improvement. It is designed to simplify the directed acrylic graph that represents the precedence relationship among the subprojects in PSP and enhance the searching diversity of the algorithm. Then, in order to avoid solutions from converging to a constant value early during evolution, multiple communities are used instead of a single community in GA-BHTR. Operators are configured in different communities independently. Communication among communities is executed by periodic interchange.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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):2079–2100

    Article  Google Scholar 

  2. Zhang Y, Tao F, Laili YJ, Hou B, Lv L, Zhang L (2012) Green partner supplier selection in virtual enterprise based on pareto genetic algorithms. Int J Adv Manuf Technol 67(9–12):2109–2125

    Google Scholar 

  3. Feng B, Fan ZP, Ma J (2010) A method for partner selection of co-development alliances using individual and collaborative utilities. Int J Prod Econ 124:159–170

    Article  Google Scholar 

  4. Emden Z, Calantone RJ, Droge C (2006) Collaborating for new product development: selecting the partner with maximum potential to create value. J Prod Innov Manage 23(4):330–341

    Article  Google Scholar 

  5. 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):559–568

    Article  Google Scholar 

  6. Cowan R, Jonard N, Zimmermann JB (2007) Bilateral collaboration and the emergence of innovation networks. Manage Sci 53(7):1051–1067

    Google Scholar 

  7. Bremer CF, Eversheim W (2000) From an opportunity identification to its manufacturing: a references model for virtual manufacturing. CIRP Ann Manuf Technol 49(1):325–329

    Article  Google Scholar 

  8. Chen SH, Lee HT, Wu YF (2008) Applying ANP approach to partner selection for strategic alliance. Manag Decis 46(3):449–465

    Article  Google Scholar 

  9. Ye F (2010) An extended TOPSIS method with interval-valued intuitionistic fuzzy numbers for virtual enterprise partner selection. Expert Syst Appl 75:7050–7055

    Article  Google Scholar 

  10. Drissen-Silva MV, Rabelo RJ (2009) A collaborative decision support framework for managing the evolution of virtual enterprises. Int J Prod Res 47(17):4833–4854

    Article  MATH  Google Scholar 

  11. Niu SH, Ony SK, Nee AYC (2011) An enhanced ant colony optimiser for multi-attribute partner selection in virtual enterprises. Int J Prod Res 50(8):2286–2303

    Article  Google Scholar 

  12. 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, Porto, Portugal 27–28 Oct, pp 421–432

    Google Scholar 

  13. Wu N, Su P (2005) Selection of partners in virtual enterprise paradigm. Robot Comput Integr Manuf 21(2):119–131

    Article  Google Scholar 

  14. 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):348–359

    Article  Google Scholar 

  15. Wang TC, Chen YH (2007) Applying consistent fuzzy preference relations to partnership selection. Omega 35(4):384–388

    Article  Google Scholar 

  16. Zeng ZB, Li Y, Zhu WX (2006) Partner selection with a due date constraint in virtual enterprises. Appl Math Comput 175(2):1353–1365

    Article  MATH  MathSciNet  Google Scholar 

  17. 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):9350–9357

    Article  Google Scholar 

  18. 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):124–129

    Article  Google Scholar 

  19. Ding JF, Liang GS (2005) Using fuzzy MCDM to select partners of strategic alliances for liner shipping. Inf Sci 173(1–3):197–225

    Article  MATH  Google Scholar 

  20. Huang JJ, Chen CY, Liu HH, Tzeng GH (2010) A multiobjective programming model for partner selection-perspectives of objective synergies and resource allocations. Expert Syst Appl 37:3530–3536

    Article  Google Scholar 

  21. Liou JJH, Tzeng GH, Tsai CY, Hsu CC (2011) A hybrid ANP model in fuzzy environments for strategic alliance partner selection in the airline industry. Appl Soft Comput 11(4):3515–3524

    Google Scholar 

  22. 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):862–875

    Article  Google Scholar 

  23. Mukherjee A, Kwon HM (2010) General auction-theoretic strategies for distributed partner selection in cooperative wireless networks. IEEE Trans Commun 58(10):2903–2915

    Article  Google Scholar 

  24. Baum JAC, Cowan R, Jonard N (2010) Network-independent partner selection and the evolution of innovation networks. Manage Sci 56(11):2094–2110

    Google Scholar 

  25. Hajidimitriou YA, Georgiou AC (2002) A goal programming model for partner selection decisions in international joint ventures. Eur J Oper Res 138(3):649–662

    Article  MATH  Google Scholar 

  26. Huang XG, Wong YS, Wang JG (2004) A two-stage manufacturing partner selection framework for virtual enterprises. Int J Comput Integr Manuf 17(4):294–304

    Article  Google Scholar 

  27. Fischer M, Jahn H, Teich T (2004) Optimizing the selection of partners in production networks. Robot Comput Integr Manuf 20(6):593–601

    Article  Google Scholar 

  28. Amid A, Ghodsypour SH, Brien CO (2006) Fuzzy multiobjective linear model for supplier selection in a supply chain. Int J Prod Econ 104(2):394–407

    Article  Google Scholar 

  29. Deans I (1999) An approach to the environment management of purchasing in the utilities sector. Eco-Manage 6(1):11–17

    Google Scholar 

  30. Crispima JA, de Sousab JP (2010) Partner selection in virtual enterprises. Int J Prod Res 48(3):683–707

    Article  Google Scholar 

  31. Saen RF (2007) Suppliers selection in the presence of both cardinal and ordinal data. Eur J Oper Res 183(2):741–747

    Article  MATH  Google Scholar 

  32. 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):367–376

    Article  Google Scholar 

  33. Chan FTS, Kumar N, Tiwari MK, Lau HCW, Choy KL (2008) Global supplier selection: a fuzzy-AHP approach. Int J Prod Res 46(14):3825–3857

    Article  MATH  Google Scholar 

  34. 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):195–205

    Article  Google Scholar 

  35. 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):859–881

    Article  Google Scholar 

  36. Bu Y, Zhou W, Yu J (2008) A discrete pso algorithm for partner selection of virtual enterprise. In: Proceedings of the 2nd IEEE international symposium on intelligent information technology application. Shanghai, China, pp 814–817

    Google Scholar 

  37. Crispim JA, de Sousa JP (2009) Partner selection in virtual enterprises: a multi-criteria decision support approach. Int J Prod Res 47(17):4791–4812

    Article  MATH  Google Scholar 

  38. 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):129–143

    Article  MATH  Google Scholar 

  39. 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):485–488

    Article  Google Scholar 

  40. Yeh WC, Chuang MC (2011) Using multi-objective genetic algorithm for partner selection in green supply chain problems. Expert Syst Appl 38:4244–4253

    Article  Google Scholar 

  41. Renner G, Ekart A (2003) Genetic algorithms in computer aided design. Comput Aided Des 35(8):709–726

    Article  Google Scholar 

  42. Toledo CFM, França PM, Morabito R, Kimms A (2009) Multi-population genetic algorithm to solve the synchronized and integrated two-level lot sizing and scheduling problem. Int J Prod Res 47(11):3097–3119

    Article  MATH  Google Scholar 

  43. Li Y, Zhang S, Zheng X (2009) Research of multi-population agent genetic algorithm for feature selection. Expert Syst Appl 36(9):11570–11581

    Article  Google Scholar 

  44. Kapanoglu M, Koc IO (2006) A multi-population parallel genetic algorithm for highly constrained continuous galvanizing line scheduling. Lect Notes Comput Sci 4030:28–41

    Article  Google Scholar 

  45. Simplesource (2007) Genetic algorithm for addressing TSP. http://simplesource.blog.163.com/blog/static/1034140620076104130312/2007.7. Accessed 25 Mar 2010

Download references

Author information

Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, China

    Fei Tao, Lin Zhang & Yuanjun Laili

Authors
  1. Fei Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuanjun Laili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fei Tao .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Tao, F., Zhang, L., Laili, Y. (2015). GA-BHTR for Partner Selection Problem. In: Configurable Intelligent Optimization Algorithm. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-319-08840-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08840-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08839-6

  • Online ISBN: 978-3-319-08840-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation