Skip to main content
SpringerLink
Log in

Methods and Algorithms for Creating and Reconfiguring Virtual Organizations

  • Chapter
  • First Online:
Decision Making in Social Sciences: Between Traditions and Innovations

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 247))

  • 500 Accesses

Abstract

As organizations tend to specialize in ever narrower and more diverse activities, virtual organizations (VOs) have gradually become a topic of interest among researchers representing numerous fields, ranging from technical domains such as optimization and soft computing to work psychology and organization studies. Due to the ad-hoc, temporary nature of VOs, most of the research attention has been devoted to optimizing the selection of partners for the strategic alliance, and this concern still holds a significant share of the current research agenda in the VO literature. This chapter reviews the most prominent approaches to solving partner selection problems. We present and discuss some of the most documented methods and algorithms for VO creation and reconfiguration, as well as a number of example implementations in applied research. Gaps and future research directions are identified.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

  • Barbati, M., Bruno, G., Genovese, A.: Applications of agent-based models for optimization problems: a literature review. Expert Syst. Appl. 39(5), 6020–6028 (2012)

    Article  Google Scholar 

  • Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181(3), 1653–1669 (2007)

    Article  Google Scholar 

  • Camarinha-Matos, L.M., Afsarmanesh, H.: Virtual enterprise modeling and support infrastructures: applying multi-agent system approaches. In: Luck, M., Marik, V., Stpankova, O., Trappl, R. (eds.) LNAI, vol. 2086, pp. 335–364. Springer (2001)

    Google Scholar 

  • Chuang, C.L., Chiang, T.A., Che, Z.H., Wang, H.S.: Using DEA and GA algorithm for finding an optimal design chain partner combination. In: Global Perspective for Competitive Enterprise, Economy and Ecology (pp. 117–127). Springer, London (2009)

    Chapter  Google Scholar 

  • Coello, C.A., Lamont, G.B., Van, V.: Evolutionary Algorithms for Solving Multi-objective Problems. Springer (2002)

    Google Scholar 

  • Crispim, J.A., de Sousa, J.P.: Partner selection in virtual enterprises. Int. J. Prod. Res. 48(3), 683–707 (2010)

    Article  Google Scholar 

  • Cunha, M.M., Putnik, G.: Agile Virtual Enterprises: Implementation and Management Support. IGI Global, Hershey, New York (2006)

    Book  Google Scholar 

  • Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) Proceedings of the Parallel Problem Solving from Nature VI Conference. Lecture Notes in Computer Science, vol. 1917. Springer, Paris, France, pp. 849–858 (2000)

    Chapter  Google Scholar 

  • Ding, H., Benyoucef, L., Xie, X.: A simulation-based multi-objective genetic algorithm approach for networked enterprises optimization. Eng. Appl. Artif. Intell. 19(6), 609–623 (2006)

    Article  Google Scholar 

  • Ehrgott, M., Gandibleux, X.: Hybrid metaheuristics for multi-objective combinatorial optimization. In: Blum, C., et al. (eds.) Hybrid Metaheuristics—An Emerging Approach to Optimization, pp. 221–260. Springer (2008)

    Google Scholar 

  • Elarbi, M., Bechikh, S., Ben Said, L., Datta, R.: Multi-objective Optimization: classical and evolutionary approaches. In: Bechikh, S., Datta, R., Gupta, A. (eds.) Adaptation, Learning and Optimization. Recent Advances in Evolutionary Multi-objective Optimization, vol. 20, pp. 1–30. Springer (2017)

    Google Scholar 

  • Erfani, T., Utyuzhnikov, S.: Directed search domain: a method for even generation of the Pareto frontier in multiobjective optimization. Eng. Optim. 43(5), 467–484 (2011)

    Article  MathSciNet  Google Scholar 

  • Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: formulation discussion and generalization. In: ICGA, vol. 93, pp. 416–423. Citeseer (1993)

    Google Scholar 

  • Ghadimi, P., Toosi, F.G., Heavey, C.: A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain. Eur. J. Oper. Res. 269(1), 286–301 (2018)

    Article  MathSciNet  Google Scholar 

  • Goyal, R.K., Kaushal, S.: Deriving crisp and consistent priorities for fuzzy AHP-based multicriteria systems using non-linear constrained optimization. Fuzzy Optim. Decis. Making 17(2), 195–209 (2018)

    Article  MathSciNet  Google Scholar 

  • Ho, W., Xu, X., Dey, P.K.: Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. Eur. J. Oper. Res. 202(1), 16–24 (2010)

    Article  Google Scholar 

  • Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence, pp. 82–87. IEEE (1994)

    Google Scholar 

  • Huang, B., Bai, L., Roy, A., Ma, N.: A multi-criterion partner selection problem for virtual manufacturing enterprises under uncertainty. Int. J. Prod. Econ. (2017). https://doi.org/10.1016/j.ijpe.2017.08.024

    Article  Google Scholar 

  • Huang, S.H., Keskar, H.: Comprehensive and configurable metrics for supplier selection. Int. J. Prod. Econ. 105(2), 510–523 (2007)

    Article  Google Scholar 

  • Karpak, B., Kumcu, E., Kasuganti, R.: An application of visual interactive goal programming: a case in vendor selection decisions. J. MultiCriteria Decis. Anal. 8(2), 93–105 (1999)

    Article  Google Scholar 

  • Ko, C.S., Kim, T., Hwang, H.: External partner selection using tabu search heuristics in distributed manufacturing. Int. J. Prod. Res. 39(17), 3959–3974 (2001)

    Article  Google Scholar 

  • Miettinen, K.: Nonlinear multiobjective optimization, international series in operations research and management. Science 12 (1999)

    Google Scholar 

  • Mladineo, M., Veza, I., Gjeldum, N.: Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm. Int. J. Prod. Res. 55(9), 2506–2521 (2017). https://doi.org/10.1080/00207543.2016.1234084

    Article  Google Scholar 

  • Murata, T., Ishibuchi, H., Tanaka, H.: Multi-objective genetic algorithm and its applications to flowshop scheduling. Comput. Ind. Eng. 30(4), 957–968 (1996)

    Article  Google Scholar 

  • Rao, R.V.: Decision Making in the Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods. Springer, London (2007)

    MATH  Google Scholar 

  • Ravindran, A.R., Ufuk Bilsel, R., Wadhwa, V., Yang, T.: Risk adjusted multicriteria supplier selection models with applications. Int. J. Prod. Res. 48(2), 405–424 (2010)

    Article  Google Scholar 

  • Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100. L. Erlbaum Associates Inc. (1985)

    Google Scholar 

  • Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)

    Article  Google Scholar 

  • Von Danwitz, S.: Managing inter-firm projects: a systematic review and directions for future research. Int. J. Project Manag. (2017). https://doi.org/10.1016/j.ijproman.2017.11.004

    Article  Google Scholar 

  • Wang, Z.J., Xu, X.F., Zhan, D.C.: Genetic algorithm for collaboration cost optimization-oriented partner selection in virtual enterprises. Int. J. Prod. Res. 47(4), 859–881 (2009)

    Article  Google Scholar 

  • Wu, C., Barnes, D.: A literature review of decision-making models and approaches for partner selection in agile supply chains. J. Purch. Supply Manag. 17(4), 256–274 (2011)

    Article  Google Scholar 

  • Yeh, W.C., Chuang, M.C.: Using multi-objective genetic algorithm for partner selection in green supply chain problems. Expert Syst. Appl. 38(4), 4244–4253 (2011)

    Article  Google Scholar 

  • Zato, C., De Paz, J.F., de Luis, A., Bajo, J., Corchado, J.M.: Model for assigning roles automatically in egovernment virtual organizations. Expert Syst. Appl. 39(12), 10389–10401 (2012)

    Article  Google Scholar 

  • Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  • Zhang, Y., Tao, F., Laili, Y., Hou, B., Lv, L., Zhang, L.: Green partner selection in virtual enterprise based on Pareto genetic algorithms. Int. J. Adv. Manuf. Technol. 1–17 (2013)

    Google Scholar 

  • Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Parallel Problem Solving from Nature-PPSN VIII, pp. 832–842. Springer (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Mathematics and Informatics, Ovidius University of Constanta, 124 Mamaia Bd, 900527, Constanta, Romania

    Anata-Flavia Ionescu

Authors
  1. Anata-Flavia Ionescu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anata-Flavia Ionescu .

Editor information

Editors and Affiliations

  1. Faculty of History and Political Science, Ovidius University of Constanţa, Aleea Universităţii, Romania

    Daniel Flaut

  2. Faculty of Military Tech, Dept of Mat & Phy, University of Defence in Brno, Brno, Czech Republic

    Šárka Hošková-Mayerová

  3. Universitatea "Eftimie Murgu" din Reșița (UEMR), Resita, Romania

    Cristina Ispas

  4. Department of Mathematics and Physics, University of Campania \Luigi Vanvitelli, CASERTA, Caserta, Italy

    Fabrizio Maturo

  5. Faculty of Mathematics and Computer Science, Ovidius University of Constanţa, Constanţa, Romania

    Cristina Flaut

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ionescu, AF. (2020). Methods and Algorithms for Creating and Reconfiguring Virtual Organizations. In: Flaut, D., Hošková-Mayerová, Š., Ispas, C., Maturo, F., Flaut, C. (eds) Decision Making in Social Sciences: Between Traditions and Innovations. Studies in Systems, Decision and Control, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-030-30659-5_2

Download citation

Publish with us

Policies and ethics

Search

Navigation