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A combinatorial optimization model for enterprise patent transfer

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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.

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References

  1. Beasley D, Bull D, Martin R (1993) An overview of genetic algorithms: part 1, fundamentals. Univ Comp 15(2):58–69

    Google Scholar 

  2. Bessen J (2009) Estimates of patent rents from firm market value. Res Policy 38(10):1604–1616

    Article  Google Scholar 

  3. Black F, Scholes M (1973) The pricing of options and corporate liabilities. J Polit Econ 81(3):637–654

    Article  MATH  Google Scholar 

  4. Bossaerts P, Plott C (2002) The CAPM in thin experimental financial markets. J Econ Dyn Control 26(7):1093–1112

    Article  MATH  Google Scholar 

  5. Brydon M (2006) Evaluating strategic options using decision-theoretic planning. Inf Technol Manag 7(1):35–49

    Article  MathSciNet  Google Scholar 

  6. Chen Z, Xu L (2001) An object-oriented intelligent CAD system for ceramic kiln. Knowl Based Syst 14:263–270

    Article  CAS  Google Scholar 

  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. 7552

  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 Michigan

  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):105–124

    Article  Google Scholar 

  10. Duan L, Xu L (2012) Business intelligence for enterprise systems: a survey. IEEE Trans Ind Inf 8(3):679–687

    Article  Google Scholar 

  11. El-Mihoub T, Hopgood A, Nolle L, Battersby A (2006) Hybrid genetic algorithms: a review. Eng Lett 13(2):124–137

    Google Scholar 

  12. Ernst H (2003) Patent information for strategic technology management. World Pat Inf 25(3):233–242

    Article  Google Scholar 

  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):14–21

    Article  MATH  MathSciNet  Google Scholar 

  14. Grefenstette J (1986) Optimization of control parameters for genetic algorithms. IEEE Trans Syst Man Cybern Syst 16(1):122–128

    Article  Google Scholar 

  15. Grindley P, Teece D (1997) Managing intellectual capital: licensing and cross-licensing in semiconductors and electronics. Calif Manag Rev 39(2):8–41

    Article  Google Scholar 

  16. Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan, Michigan

    Google Scholar 

  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):55–77

    Google Scholar 

  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 28:9–19

    Article  Google Scholar 

  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):251–262

    Article  Google Scholar 

  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):311–320

    Article  Google Scholar 

  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):272–286

    Article  Google Scholar 

  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):661–673

    Article  Google Scholar 

  23. Li F, Xu L, Jin C, Wang H (2011) Intelligent bionic genetic algorithm (IB-GA) and its convergence. Expert Syst Appl 38(7):8804–8811

    Article  Google Scholar 

  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):8929–8937

    Article  Google Scholar 

  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):60–71

    Article  CAS  Google Scholar 

  26. Lin F (2008) Solving the knapsack problem with imprecise weight coefficients using genetic algorithms. Eur J Oper Res 185(1):133–145

    Article  MATH  Google Scholar 

  27. Low C, Chang C, Li R, Huang C (2014) Coordination of production scheduling and delivery problems with heterogeneous fleet. Int J Prod Econ 153:139–148

    Article  Google Scholar 

  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):169–175

    Article  MATH  MathSciNet  Google Scholar 

  29. Palencia A, Delgadillo G (2012) A computer application for a bus body assembly line using genetic algorithms. Int J Prod Econ 140:431–438

    Article  Google Scholar 

  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):177–190

    Article  Google Scholar 

  31. Rees J, Koehler G (2002) Evolution in groups: a genetic algorithm approach to group decision support systems. Inf Technol Manag 3(3):213–227

    Article  Google Scholar 

  32. Roland J, Figueira J, De Smet Y (2013) The inverse {0, 1}-knapsack problem: theory, algorithms and computational experiments. Discret Optim 10(2):181–192

    Article  MATH  Google Scholar 

  33. Sikora R, Piramuthu S (2005) Efficient genetic algorithm based data mining using feature selection with hausdorff distance. Inf Technol Manag 6(4):315–331

    Article  Google Scholar 

  34. Srinivas M, Patnaik L (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern Syst 24(4):656–667

    Article  Google Scholar 

  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):523–542

    Article  Google Scholar 

  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):214–22838

    Article  Google Scholar 

  37. U.S. PTO (2012) Performance and accountability report

  38. Wang L, Xu L, Bi Z, Xu Y (2014) Data cleaning for RFID and WSN integration. IEEE Trans Ind Inf 10(1):408–418

    Article  Google Scholar 

  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):1071–1074

    Article  Google Scholar 

  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):10414–10419

    Article  Google Scholar 

  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 15–22

  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. Xu L (2013) Introduction: systems science in industrial sectors. Syst Res Behav Sci 30(3):211–213

    Article  Google Scholar 

  44. Xu L, Liang N, Gao Q (2008) An integrated approach for agricultural ecosystem management. IEEE Trans SMC Part C 38(4):590–599

    Google Scholar 

  45. Xu S, Xu L, Basl J (2012) Introduction: advances in e-business engineering. Inf Technol Manag 13(4):201–204

    Article  Google Scholar 

  46. Yang L, Xu L, Shi Z (2012) An enhanced dynamic hash TRIE algorithm for lexicon search. Enterp Inf Syst 6(4):419–432

    Article  MathSciNet  Google Scholar 

  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):298–304

    Article  Google Scholar 

  48. Zeng L, Li L, Duan L (2012) Business intelligence in enterprise computing environment. Inf Technol Manag 13(4):297–310

    Article  Google Scholar 

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Authors and Affiliations

  1. Wuhan Engineering Consulting Bureau, Wuhan, 430014, China

    Yi Xie

  2. Faculty of Technology, University of Vaasa, 65101, Vaasa, Finland

    Josu Takala & Yang Liu

  3. Old Dominion University, Norfolk, VA, 23529, USA

    Yong Chen

Authors
  1. Yi Xie
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  2. Josu Takala
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  3. Yang Liu
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  4. Yong Chen
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Correspondence to Yi Xie.

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Xie, Y., Takala, J., Liu, Y. et al. A combinatorial optimization model for enterprise patent transfer. Inf Technol Manag 16, 327–337 (2015). https://doi.org/10.1007/s10799-014-0207-z

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  • DOI: https://doi.org/10.1007/s10799-014-0207-z

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