Information Technology and Management

, Volume 16, Issue 4, pp 327–337 | Cite as

A combinatorial optimization model for enterprise patent transfer

Article

Abstract

Enterprises need patent transfer strategies to improve their technology management. This paper proposes a combinatorial optimization model that is based on intelligent computing to support enterprises’ decision making in developing patent transfer strategy. The model adopts the Black–Scholes Option Pricing Model and Arbitrage Pricing Theory to estimate a patent’s value. Based on the estimation, a hybrid genetic algorithm is applied that combines genetic algorithms and greedy strategy for the optimization purpose. Encode repairing and a single-point crossover are applied as well. To validate this proposed model, a case study is conducted. The results indicate that the proposed model is effective for achieving optimal solutions. The combinatorial optimization model can help enterprise promote their benefits from patent sale and support the decision making process when enterprises develop patent transfer strategies.

Keywords

Optimization model Knapsack problem 01 Black–Scholes Option Pricing Model Arbitrage Pricing Theory Genetic algorithms Greedy strategy Patent transfer 

References

  1. 1.
    Beasley D, Bull D, Martin R (1993) An overview of genetic algorithms: part 1, fundamentals. Univ Comp 15(2):58–69Google Scholar
  2. 2.
    Bessen J (2009) Estimates of patent rents from firm market value. Res Policy 38(10):1604–1616CrossRefGoogle Scholar
  3. 3.
    Black F, Scholes M (1973) The pricing of options and corporate liabilities. J Polit Econ 81(3):637–654MATHCrossRefGoogle Scholar
  4. 4.
    Bossaerts P, Plott C (2002) The CAPM in thin experimental financial markets. J Econ Dyn Control 26(7):1093–1112MATHCrossRefGoogle Scholar
  5. 5.
    Brydon M (2006) Evaluating strategic options using decision-theoretic planning. Inf Technol Manag 7(1):35–49MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chen Z, Xu L (2001) An object-oriented intelligent CAD system for ceramic kiln. Knowl Based Syst 14:263–270CrossRefGoogle Scholar
  7. 7.
    Cohen W, Nelson R, Walsh J (2000) Protecting their intellectual assets: appropriability conditions and why US manufacturing firms patent (or not). National Bureau of Economic Research, Working paper no. 7552Google Scholar
  8. 8.
    DeJong K (1975) Analysis of the behavior of a class of genetic adaptive systems. Ph.D. Thesis, Dept. Computer and communication sciences, University of MichiganGoogle Scholar
  9. 9.
    Dogan N, Tanrikulu Z (2012) A comparative analysis of classification algorithms in data mining for accuracy, speed and robustness. Inf Technol Manag 14(2):105–124CrossRefGoogle Scholar
  10. 10.
    Duan L, Xu L (2012) Business intelligence for enterprise systems: a survey. IEEE Trans Ind Inf 8(3):679–687CrossRefGoogle Scholar
  11. 11.
    El-Mihoub T, Hopgood A, Nolle L, Battersby A (2006) Hybrid genetic algorithms: a review. Eng Lett 13(2):124–137Google Scholar
  12. 12.
    Ernst H (2003) Patent information for strategic technology management. World Pat Inf 25(3):233–242CrossRefGoogle Scholar
  13. 13.
    Florios K, Mavrotas G, Diakoulaki D (2010) Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms. Eur J Oper Res 203(1):14–21MATHMathSciNetCrossRefGoogle Scholar
  14. 14.
    Grefenstette J (1986) Optimization of control parameters for genetic algorithms. IEEE Trans Syst Man Cybern Syst 16(1):122–128CrossRefGoogle Scholar
  15. 15.
    Grindley P, Teece D (1997) Managing intellectual capital: licensing and cross-licensing in semiconductors and electronics. Calif Manag Rev 39(2):8–41CrossRefGoogle Scholar
  16. 16.
    Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan, MichiganGoogle Scholar
  17. 17.
    Hoyland C, Adams K, Tolk A, Xu L (2014) The RQ-Tech methodology: a new paradigm for conceptualizing strategic enterprise architectures. J Manag Anal 1(1):55–77Google Scholar
  18. 18.
    Imam S, Chan J, Shah S (2013) Equity valuation models and target price accuracy in Europe: evidence from equity reports. Int Rev Financ Anal 28:9–19CrossRefGoogle Scholar
  19. 19.
    Jiang H, Zhao S, Zhang Y, Chen Y (2012) The cooperative effect between technology standardization and industrial technology innovation based on Newtonian mechanics. Inf Technol Manag 13(4):251–262CrossRefGoogle Scholar
  20. 20.
    Jiang H, Zhao S, Qiu S, Chen Y (2012) Strategy for technology standardization based on the theory of entropy. Inf Technol Manag 13(4):311–320CrossRefGoogle Scholar
  21. 21.
    Jiang H, Zhao S, Wang X, Bi Z (2013) Applying electromagnetic field theory to study the synergistic relationships between technology standardization and technology development. Syst Res Behav Sci 30(3):272–286CrossRefGoogle Scholar
  22. 22.
    Jiang Y, Xu L, Wang H, Wang H (2009) Influencing factors for predicting financial performance based on genetic algorithms. Syst Res Behav Sci 26(6):661–673CrossRefGoogle Scholar
  23. 23.
    Li F, Xu L, Jin C, Wang H (2011) Intelligent bionic genetic algorithm (IB-GA) and its convergence. Expert Syst Appl 38(7):8804–8811CrossRefGoogle Scholar
  24. 24.
    Li F, Xu L, Jin C, Wang H (2011) Structure of multi-stage composite genetic algorithm (MSC-GA) and its performance. Expert Syst Appl 38(7):8929–8937CrossRefGoogle Scholar
  25. 25.
    Li H, Xu L, Wang J, Mo Z (2003) Feature space theory in data mining: transformations between extensions and intensions in knowledge representation. Expert Syst 20(2):60–71CrossRefGoogle Scholar
  26. 26.
    Lin F (2008) Solving the knapsack problem with imprecise weight coefficients using genetic algorithms. Eur J Oper Res 185(1):133–145MATHCrossRefGoogle Scholar
  27. 27.
    Low C, Chang C, Li R, Huang C (2014) Coordination of production scheduling and delivery problems with heterogeneous fleet. Int J Prod Econ 153:139–148CrossRefGoogle Scholar
  28. 28.
    Martello S, Toth P (1977) An upper bound for the zero-one knapsack problem and a branch and bound algorithm. Eur J Oper Res 1(3):169–175MATHMathSciNetCrossRefGoogle Scholar
  29. 29.
    Palencia A, Delgadillo G (2012) A computer application for a bus body assembly line using genetic algorithms. Int J Prod Econ 140:431–438CrossRefGoogle Scholar
  30. 30.
    Qi J, Xu L, Shu H, Li H (2006) Knowledge management in OSS–an enterprise information system for the telecommunications industry. Syst Res Behav Sci 23(2):177–190CrossRefGoogle Scholar
  31. 31.
    Rees J, Koehler G (2002) Evolution in groups: a genetic algorithm approach to group decision support systems. Inf Technol Manag 3(3):213–227CrossRefGoogle Scholar
  32. 32.
    Roland J, Figueira J, De Smet Y (2013) The inverse {0, 1}-knapsack problem: theory, algorithms and computational experiments. Discret Optim 10(2):181–192MATHCrossRefGoogle Scholar
  33. 33.
    Sikora R, Piramuthu S (2005) Efficient genetic algorithm based data mining using feature selection with hausdorff distance. Inf Technol Manag 6(4):315–331CrossRefGoogle Scholar
  34. 34.
    Srinivas M, Patnaik L (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern Syst 24(4):656–667CrossRefGoogle Scholar
  35. 35.
    Tan W, Xu W, Yang F, Xu L, Jiang C (2013) A framework for service enterprise workflow simulation with multi-agents cooperation. Enterp Inf Syst 7(4):523–542CrossRefGoogle Scholar
  36. 36.
    Tian Y, Li Y, Wei Z (2013) Managerial incentive and external knowledge acquisition under technological uncertainty; a nested system perspective. Syst Res Behav Sci 30(3):214–22838CrossRefGoogle Scholar
  37. 37.
    U.S. PTO (2012) Performance and accountability reportGoogle Scholar
  38. 38.
    Wang L, Xu L, Bi Z, Xu Y (2014) Data cleaning for RFID and WSN integration. IEEE Trans Ind Inf 10(1):408–418CrossRefGoogle Scholar
  39. 39.
    Wang P, Xu L, Zhou S, Fan Z, Li Y, Feng S (2010) a novel bayesian learning method for information aggregation in modular neural networks. Expert Syst Appl 37(2):1071–1074CrossRefGoogle Scholar
  40. 40.
    Wang P, Zhang J, Xu L, Wang H, Feng S, Zhu H (2011) How to measure adaptation complexity in evolvable systems—a new synthetic approach of constructing fitness functions. Expert Syst Appl 38(8):10414–10419CrossRefGoogle Scholar
  41. 41.
    Wilamowski B (2010) Challenges in applications of computational intelligence in industrial electronics. In: Proceedings of IEEE International Symposium on Industrial Electronics (IEEE ISIE 2010), Bari, Italy, July 4–7, pp 15–22Google Scholar
  42. 42.
    WIPO (2013) World intellectual property indicators. Retrived from http://www.wipo.int/export/sites/www/freepublications/en/intproperty/941/wipo_pub_941_2013.pdf
  43. 43.
    Xu L (2013) Introduction: systems science in industrial sectors. Syst Res Behav Sci 30(3):211–213CrossRefGoogle Scholar
  44. 44.
    Xu L, Liang N, Gao Q (2008) An integrated approach for agricultural ecosystem management. IEEE Trans SMC Part C 38(4):590–599Google Scholar
  45. 45.
    Xu S, Xu L, Basl J (2012) Introduction: advances in e-business engineering. Inf Technol Manag 13(4):201–204CrossRefGoogle Scholar
  46. 46.
    Yang L, Xu L, Shi Z (2012) An enhanced dynamic hash TRIE algorithm for lexicon search. Enterp Inf Syst 6(4):419–432MathSciNetCrossRefGoogle Scholar
  47. 47.
    Zhang M, Xu L, Zhang W, Li H (2003) A rough set approach to knowledge reduction based on inclusion degree and evidence reasoning theory. Expert Syst 20(5):298–304CrossRefGoogle Scholar
  48. 48.
    Zeng L, Li L, Duan L (2012) Business intelligence in enterprise computing environment. Inf Technol Manag 13(4):297–310CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Wuhan Engineering Consulting BureauWuhanChina
  2. 2.Faculty of TechnologyUniversity of VaasaVaasaFinland
  3. 3.Old Dominion UniversityNorfolkUSA

Personalised recommendations