Cluster Computing

, Volume 22, Supplement 4, pp 7841–7860 | Cite as

Optimization design and computer simulation of enterprise R&D decision model under resource allocation

  • Qi Na
  • Jian-Yu ZhaoEmail author


Taking into account enterprises’ investment on commodity quality and advertisements as important factors that affect sales volume, and the R&D activities are effective measures to reduce production costs and improve commodity competitiveness. Based on the game theory, this paper establishes the price game model among enterprises, and analyzes the relationship of relevant parameters under different competition modes by using computer simulation technology. The simulation results show that the two enterprises will obtain relatively high sales volume and profits in the Stackelberg competition mode, but the absorptive capacity makes the coordination strategy of the R&D stage become unstable. The non-coordination strategy can not only effectively stimulate enterprises to expand the scale of R&D investment to further reduce the cost of production, but also has a positive effect on the follower enterprises in improving sales volume and profits.


R&D Decision model Optimization Simulation 



This work was supported by the National Science Foundation for Young Scientists of China (71602041).


  1. 1.
    Amir, R.: Modelling imperfectly appropriable R&D via spillovers. Int. J. Ind. Organ. 18(7), 1013–1032 (2000)Google Scholar
  2. 2.
    Aschhoff, B., Schmidt, T.: Empirical evidence on the success of R&D cooperation–happy together? Rev. Ind. Organ. 33(1), 41–62 (2008)Google Scholar
  3. 3.
    Atallah, G.: R&D cooperation with asymmetric spillovers. Can. J. Econ. 38(3), 919–936 (2005)Google Scholar
  4. 4.
    Aviv, Y., Pazgal, A.: Optimal pricing of seasonal products in the presence of forward-looking consumers. Manuf. Serv. Oper. Manag. 10(3), 339–359 (2008)Google Scholar
  5. 5.
    Balasubramanian, S., Bhardwaj, P.: When not all conflict is bad: manufacturing-marketing conflict and strategic incentive design. Manag. Sci. 50(4), 489–502 (2004)Google Scholar
  6. 6.
    Bernstein, J.I., Nadiri, M.I.: Interindustry R&D spillovers, rates of return, and production in high-tech industries. Am. Econ. Rev. 78(2), 429–434 (1988)Google Scholar
  7. 7.
    Boardman, P.C.: Government centrality to university-industry interactions: university research centers and the industry involvement of academic researchers. Res. Policy 38(10), 1505–1516 (2009)Google Scholar
  8. 8.
    Cachon, G.P., Swinney, R.: Purchasing, pricing, and quick response in the presence of strategic consumers. Manag. Sci. 55(3), 497–511 (2009)zbMATHGoogle Scholar
  9. 9.
    Cachon, G.P., Harker, P.T.: Competition and outsourcing with scale economies. Manag. Sci. 48(10), 1314–1333 (2002)zbMATHGoogle Scholar
  10. 10.
    Chesbrough, H.W.: The era of open innovation. MIT Sloan Manag. Rev. 44(3), 35–41 (2003)Google Scholar
  11. 11.
    Dahl, D.W., Moreau, P.: The influence and value of analogical thinking during new product ideation. J. Mark. Res. 39(1), 47–60 (2002)Google Scholar
  12. 12.
    D’Aspremont, C., Jacquemin, A.: Cooperative and noncooperative R&D in duopoly with spillovers. Am. Econ. Rev. 78(78), 1133–1137 (1988)Google Scholar
  13. 13.
    Dumrongsiri, A., Fan, M., Jain, A., Moinzadeh, K.: A supply chain model with direct and retail channels. Eur. J. Oper. Res. 187(3), 691–718 (2008)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Gabszewicz, J.J., Thisse, J.F.: Price competition, quality and income disparities. J. Econ. Theory 20(3), 340–359 (1979)zbMATHGoogle Scholar
  15. 15.
    Goel, R.K.: Spillovers, rivalry and r&d investment. South. Econ. J. 62(1), 71–76 (1995)MathSciNetGoogle Scholar
  16. 16.
    Grünfeld, L.A.: Meet me halfway but don’t rush: absorptive capacity and strategic r&d investment revisited. Int. J. Ind. Organ. 21(8), 1091–1109 (2003)Google Scholar
  17. 17.
    Grünfeld, L.A.: Multinational production, absorptive capacity, and endogenous r&d spillovers. Rev. Int. Econ. 14(5), 922–940 (2006)Google Scholar
  18. 18.
    Hao, L., Yangjian, J., Gu, X., Zhigang, B., Guoning, Q.: A universal enterprise manufacturing services maturity model: a case study in a chinese company. Int. J. Comput. Integr. Manuf. 27(5), 434–449 (2014)Google Scholar
  19. 19.
    He, X., Krishnamoorthy, A., Prasad, A., Sethi, S.P.: Retail competition and cooperative advertising. Oper. Res. Lett. 39(1), 11–16 (2011)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Jan, V., Raymond, D.B.: Asymmetric spillovers and investments in research and development of leaders and followers. Econ. Innov. New Technol. 17(17), 417–433 (2008)Google Scholar
  21. 21.
    Miklós-Thal, J.: Optimal collusion under cost asymmetry. Econ. Theor. 46(1), 99–125 (2011)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Joglekar, N.R., Lévesque, M.: Marketing, r&d, and startup valuation. IEEE Trans. Eng. Manag. 56(2), 229–242 (2009)Google Scholar
  23. 23.
    Kamien, M.I., Muller, E., Zang, I.: Research joint ventures and r&d cartels. Am. Econ. Rev. 82(5), 1293–1306 (1992)Google Scholar
  24. 24.
    Karray, S., Martín-Herránb, G.: A dynamic model for advertising and pricing competition between national and store brands. Eur. J. Oper. Res. 193(2), 451–467 (2009)MathSciNetzbMATHGoogle Scholar
  25. 25.
    Kunz, W., Schmitt, B., Meyer, A.: How does perceived firm innovativeness affect the consumer? J. Bus. Res. 64(8), 816–822 (2011)Google Scholar
  26. 26.
    Lambertini, L., Orsini, R.: Quality improvement and process innovation in monopoly: a dynamic analysis. Oper. Res. Lett. 43(4), 370–373 (2015)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Li, H., Ji, Y., Gu, X., Qi, G., Tang, R.: Module partition process model and method of integrated service product. Comput. Ind. 63(4), 298–308 (2012)Google Scholar
  28. 28.
    Lu, J.C., Tsao, Y.C., Charoensiriwath, C.: Competition under manufacturer service and retail price. Econ. Model. 28(3), 1256–1264 (2011)Google Scholar
  29. 29.
    Lyons, A.C., Ma’Aram, A.: An examination of multi-tier supply chain strategy alignment in the food industry. Int. J. Prod. Res. 52(7), 1911–1925 (2014)Google Scholar
  30. 30.
    Mitra, S.: Revenue management for remanufactured products. Omega 35(5), 553–562 (2007)Google Scholar
  31. 31.
    Moukrim, A., Quilliot, A., Toussaint, H.: An effective branch-and-price algorithm for the preemptive resource constrained project scheduling problem based on minimal interval order enumeration. Eur. J. Oper. Res. 244(2), 360–368 (2015)MathSciNetzbMATHGoogle Scholar
  32. 32.
    Nguyen, D., Shi, L.: Competitive advertising strategies and market-size dynamics: a research note on theory and evidence. Manag. Sci. 52(6), 965–973 (2006)zbMATHGoogle Scholar
  33. 33.
    Piga, C., Poyago-Theotoky, J.: Endogenous R&D spillovers and locational choice. Reg. Sci. Urban Econ. 35(2), 127–139 (2005)Google Scholar
  34. 34.
    Ouardighi, F.E., Kogan, K.: Dynamic conformance and design quality in a supply chain: an assessment of contracts’ coordinating power. Ann. Oper. Res. 211(1), 137–166 (2013)MathSciNetzbMATHGoogle Scholar
  35. 35.
    Radford, S.K., Bloch, P.H.: Linking innovation to design: consumer responses to visual product newness. J. Prod. Innov. Manag. 28(s1), 208–220 (2011)Google Scholar
  36. 36.
    Rindova, V.P., Williamson, I.O., Petkova, A.P., Sever, J.M.: Being good or being known: an empirical examination of the dimensions, antecedents, and consequences of organizational reputation. Acad. Manag. J. 48(6), 1033–1049 (2005)Google Scholar
  37. 37.
    Sandberg, B.: Customer-related proactiveness in the radical innovation development process. Eur. J. Innov. Manag. 10(2), 252–267 (2007)Google Scholar
  38. 38.
    Smith, K.G., Collins, C.J., Clark, K.D.: Existing knowledge, knowledge creation capability, and the rate of new product introduction in high-technology firms. Acad. Manag. J. 48(2), 346–357 (2005)Google Scholar
  39. 39.
    Spence, M.: Cost reduction, competition, and industry performance. Econometrica 52(52), 101–121 (1984)Google Scholar
  40. 40.
    Stone-Romero, E.F., Stone, D.L., Grewal, D.: Development of a multidimensional measure of perceived product quality. J. Qual. Manag. 2(1), 87–111 (1997)Google Scholar
  41. 41.
    Su, X., Zhang, F.: Strategic customer behavior, commitment, and supply chain performance. Soc. Sci. Electron. Publ. 54(10), 1759–1773 (2008)zbMATHGoogle Scholar
  42. 42.
    Szmerekovsky, J.G., Zhang, J.: Pricing and two-tier advertising with one manufacturer and one retailer. Eur. J. Oper. Res. 192(3), 904–917 (2009)MathSciNetzbMATHGoogle Scholar
  43. 43.
    Yu, M., Debo, L.G., Kapuscinski, R.: Strategic waiting for consumer-generated quality information: dynamic pricing of new experience goods. SSRN Electron. J. 62(2), 410–435 (2016)Google Scholar
  44. 44.
    Ulaga, W., Chacour, S.: Measuring customer-perceived value in business markets: a prerequisite for marketing strategy development and implementation. Ind. Mark. Manag. 30(6), 525–540 (2001)Google Scholar
  45. 45.
    Xie, G., Yue, W., Wang, S., Lai, K.K.: Quality investment and price decision in a risk-averse supply chain. Eur. J. Oper. Res. 214(2), 403–410 (2011)MathSciNetzbMATHGoogle Scholar
  46. 46.
    Xie, J., Wei, J.C.: Coordinating advertising and pricing in a manufacturer-retailer channel. Eur. J. Oper. Res. 197(2), 785–791 (2009)MathSciNetzbMATHGoogle Scholar
  47. 47.
    Zahra, S.A., George, G.: Absorptive capacity: a review and reconceptualization, and extension. Acad. Manag. Rev. 27(2), 185–203 (2002)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Economics and ManagementHarbin Engineering UniversityHarbinChina

Personalised recommendations