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Analysis on the Procurement Cost of Construction Supply Chain based on Evolutionary Game Theory

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Abstract

The selection of building materials plays an important role in the control of engineering projects. The subject of this paper is to study the problem of balancing the relationship between the general contractor and building material suppliers in the construction supply chain. Firstly, to solve the problem, this paper from the perspective of material quality safety analyzes the problems between material suppliers and construction general contractor. Then, under the constraints of information asymmetry and bounded rationality, the evolutionary game model of material suppliers and construction contractors is constructed to study the strategy selection in the process of product quality supervision. Finally, the system simulation is carried out by using MATLAB software to demonstrate the evolutionary equilibrium strategy of the model when different parameters change. The results show that the strategy selection of material suppliers and construction contractors is affected by multiple factors such as supervision cost, material cost, risk probability of accident occurrence and penalty amount.

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Reference

  1. Wu, P.; Song, Y.; Zhu, J.B.; Chang, R.D.: Analyzing the influence factors of the carbon emissions from China’s building and construction industry from 2000 to 2015. J. Clean. Prod. 221(06), 552–566 (2019)

    Google Scholar 

  2. Zeng, B.C.; Ben, J.M.; Yen, P.C.: Rethinking the role of partnerships in global supply chains: a risk-based perspective. Int. J. Prod. Econom. 185(03), 52–62 (2017)

    Google Scholar 

  3. Michael, K.: Why shops close again: an evolutionary perspective on the deregulation of shopping hours. Eur. Econom. Rev. 46(01), 51–72 (2002)

    Google Scholar 

  4. Simon, H.A.: Book reviews: models of man, social and rational, mathematical essays on rational human behavior in a social setting. J. Philos. (1957). https://doi.org/10.2307/2023734

    Article  MATH  Google Scholar 

  5. Mark, B., Chris, C.: Evolutionary game theory. eLs. MIT Press. (2010). https://doi.org/10.1002/9780470015902.a0005457.pub2

  6. Neumann, J.V.; Morgenstern, O.: Theory of games and economic behavior. Princet. Univ. Press (1953). https://doi.org/10.1038/157172a0

    Article  MATH  Google Scholar 

  7. William, H.S.: Evolutionary game theory. In: Meyers, R.A. (Ed.) Encyclopedia of complexity and systems science, pp. 3176–3205. Springer, Heidelberg (2009)

    Google Scholar 

  8. Cressman, R.; Apaloo, J.: Evolutionary game theory. In: Basar, T.; Zaccour, G. (Eds.) Handbook of dynamic game theory, pp. 1–50. Springer, Cham (2016)

    Google Scholar 

  9. Fisher, R.A.: Genetical theory of natural selection. Genetics 154, 1419–1426 (2000)

    Google Scholar 

  10. Masahiko, U.: Effect of information asymmetry in Cournot duopoly game with bounded rationality. Appl. Math. Comput. 362, 124535 (2019)

    MathSciNet  MATH  Google Scholar 

  11. Mariano, R.: What model best describes initial choices in a cournot duopoly experiment? Ssrn Electron. J. (2014). https://doi.org/10.2139/ssrn.2373499

    Article  Google Scholar 

  12. Carlos, A.F.; Johannes, B.: Cournot vs. Walras: a reappraisal through simulations. J. Econom. Dyn. Control 82, 257–272 (2017)

    MathSciNet  MATH  Google Scholar 

  13. Nash, J.F.: The bargaining problem. Econometrica 18(02), 155–162 (1950)

    MathSciNet  MATH  Google Scholar 

  14. Nash, J.F.: Equilibrium points in N-person games. Proc Natl Academy Sci. 36(01), 48–49 (1950)

    MathSciNet  MATH  Google Scholar 

  15. Shen, D., Cruz, J.B.: Nash strategies for dynamic noncooperative linear quadratic sequential games. Decision and Control. 2007 46th IEEE Conference on. IEEE (2008)

  16. Luis, J.G.; Marco, C.; Frank, S.: Solving the simultaneous truel in the weakest link: nash or revenge? J. Behav. Exp. Econom. 81, 56–72 (2019)

    Google Scholar 

  17. Garg, S.: Pandey, O.: Srinivasan, A.: Revisiting the Cryptographic Hardness of Finding a Nash equilibrium. In: Robshaw M., Katz J. (eds.) Advances in Cryptology – CRYPTO 2016. Lecture Notes in Computer Science, pp.579-604.Springer, Berlin, Heidelberg (2016)

  18. Smith, J.M.: The theory of games and the evolution of animal conflicts. J. Theor. Biol. 47(01), 209–221 (1974)

    MathSciNet  Google Scholar 

  19. Taylor, P.D.; Jonker, L.: Game dynamics and evolutionary stable strategies. Math. Biosci. 40(01), 145–156 (1978)

    MathSciNet  MATH  Google Scholar 

  20. Han, T.A.; Pereira, L.M.; Lenaerts, T.: Evolution of commitment and level of participation in public goods games. Autonom. Ag Multi-Ag. Syst. 31(03), 561–583 (2016)

    Google Scholar 

  21. Jonathan, N.: Evolutionary game theory: a renaissance. Ssrn Electron. J. (2017). https://doi.org/10.2139/ssrn.3077467

    Article  MATH  Google Scholar 

  22. Lu, H.P.: Current situation and prospect of transportation development in China. In: Tandon, M.; Ghosh, P. (Eds.) Mobility engineering, pp. 25–35. Springer, Singapore (2017)

    Google Scholar 

  23. Sung, J.J.; Joris, P.: Counterfactual prediction in complete information games: point prediction under partial identification. J. Econom. 216(02), 394–429 (2020)

    MathSciNet  MATH  Google Scholar 

  24. Sylvain, D.; Nicolas, F.: On Monte-carlo tree search for deterministic games with alternate moves and complete information. ESAIM Prob. Stat. 23, 176–216 (2019)

    MathSciNet  MATH  Google Scholar 

  25. Mehdi, Z.; José, A.F.; Javad, S.; Mehdi, P.; Luca, S.; Pedro, N.M.: An incomplete information static game evaluating community-based forest management in Zagros Iran. Sustainability 12(5), 1750 (2020)

    Google Scholar 

  26. Zhang, J.; Li, Z.Q.; Zhang, C.Y.: Evolutionary dynamics of strategies without complete information on complex networks. Asian J. Control 22(1), 362–372 (2020)

    MathSciNet  Google Scholar 

  27. Luis, H.F.; Ruth, E.C.; José, I.G.: Modelling of a multi-agent supply chain management system using Colored Petri Nets. Procedia Manuf. 42, 288–295 (2020)

    Google Scholar 

  28. Lisa, M.E.; Monique, L.U.M.: Supply chain management in industrial Marketing–relationships matter. Ind. Mark. Manag. 79, 36–45 (2019)

    Google Scholar 

  29. Babak, A.; Toktam, B.; Zahra, H.; Kate, S.M.; Maryam, D.: Predicting solutions of large-scale optimization problems via machine learning: a case study in blood supply chain management. Comput. Oper. Res. 119, 104941 (2020)

    MathSciNet  MATH  Google Scholar 

  30. Jin, M.Z.; Song, L.J.; Wang, Y.N.; Zeng, Y.C.: Longitudinal cooperative robust optimization model for sustainable supply chain management. Chaos, Solitons Fractals 116, 95–105 (2018)

    MathSciNet  MATH  Google Scholar 

  31. Xiong, F.; Gong, P.; Jin, P.: Supply chain scheduling optimization based on genetic particle swarm optimization algorithm. Clust. Comput. 22, 14767–14775 (2019)

    Google Scholar 

  32. Biswajit, S.; Muhammad, O.; Namhun, K.: A cooperative advertising collaboration policy in supply chain management under uncertain conditions. Appl. Soft Comput. 88, 105948 (2020)

    Google Scholar 

  33. Jiasen, S.; Guo, L.; Su, X.X.; Wei, D.: Intermodal transportation service procurement with transaction costs under belt and road initiative. Transp. Res Part E: Logist. Transp. Rev. 127, 31–48 (2019)

    Google Scholar 

  34. Muhammad, S.; Matteo, M.S.: Supply chain coordination to optimize manufacturer’s capacity procurement decisions through a new commitment-based model with penalty and revenue-sharing. Int. J. Prod. Econom. 208, 512–528 (2019)

    Google Scholar 

  35. Nemati, Y.; Alavidoost, M.H.: A fuzzy bi-objective MILP approach to integrate sales, production, distribution and procurement planning in a FMCG supply chain. Soft Comput. 23(07), 4871–4890 (2019). https://doi.org/10.1007/s00500-018-3146-5

    Article  Google Scholar 

  36. Miguel, M.M.; Abdel, E.M.; Paolo, S.: On the logistics of cocoa supply chain in Côte d’Ivoire: simulation-based analysis. Comput. Ind. Eng. 137, 106034 (2019)

    Google Scholar 

  37. Song, Z.Z.; Tang, W.S.; Zhao, R.Q.: A simple game theoretical analysis for incentivizing multi-modal transportation in freight supply chains. Eur. J. Oper. Res. 283(01), 152–165 (2020)

    MathSciNet  MATH  Google Scholar 

  38. Hallikas, J.; Lintukangas, K.; Grudinschi, D.: Sustainability risk management in supply chain. In: Zsidisin, G.; Henke, M. (Eds.) Revisiting supply chain risk, pp. 265–278. Springer series in Supply Chain Management, Springer, Cham (2019)

    Google Scholar 

  39. Ivanov, D.; Tsipoulanidis, A.; Schönberger, J.: Operations and supply chain strategy. In: Global supply chain and operations management. Springer texts in business and economics. Springer, Chams (2019)

  40. Barry, B.; Andrew, N.A.: Design for procurement: What procurement driven design initiatives result in environmental and economic performance improvement? J. Purch. Supply Manag. 23(01), 28–39 (2017)

    Google Scholar 

  41. Shu, L.L.; Qu, S.J.; Wu, Z.: Supply chain coordination with optimal pricing and logistics service decision in online retailing. Arab J. Sci. Eng. 45, 2247–2261 (2020)

    Google Scholar 

  42. Zhao, T.Y.; Xu, X.P.; Chen, Y.; Liang, L.; Yu, Y.G.; Wang, K.: Coordination of a fashion supply chain with demand disruptions. Transp. Res. Part E: Logist. Transp. Rev. 134, 101838 (2020)

    Google Scholar 

  43. Cong, J.; Wang, H.: Research on Three-Level Supply Chain Coordination Based on Revenue Sharing Contract and Option Contract. In: Xu J., Hajiyev A., Nickel S., Gen M. (eds.) Proceedings of the Tenth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing. Springer, Singapore pp 1215-1226 (2017)

  44. Nelly, B.; Tatyana, C.; Tal, A.: Revenue sharing contracts in a supply chain: a literature review. IFAC-Papers On Line 52(13), 1578–1583 (2019)

    Google Scholar 

  45. Xiao, D.; Wang, J.Y.; Lu, Q.H.: Stimulating sustainability investment level of suppliers with strategic commitment to price and cost sharing in supply chain. J. Clean. Prod. 252, 119732 (2020)

    Google Scholar 

  46. Maher, A.N.; Agi, Ö.H.: Game theory-based research in green supply chain management: a review. IFAC-Papers On Line. 52(13), 2267–2272 (2019)

    Google Scholar 

  47. Sun, H.X.; Wan, Y.; Zhang, L.L.; Zhou, Z.: Evolutionary game of the green investment in a two-echelon supply chain under a government subsidy mechanism. J. Clean. Prod. 235, 1315–1326 (2019)

    Google Scholar 

  48. Wang, P.P.; Sun, Z.H.: Optimization analysis of distribution and distribution based on ASM process. J. Beijing Univ. Inf. Technol. 039(05), 59–63 (2013)

    Google Scholar 

  49. Zhang, H.W.; Ling, J.T.; Dong, S.H.; Feng, S.L.: New modification method for safety factor of ASME. Consid. Pipeline Big Data. 11(3), 198 (2020)

    Google Scholar 

  50. Wang, L.; Yang, B.: Application of BPR and ASME in supplier selection. J. Liaoning Inst. Technol. 05, 70–73 (2005)

    Google Scholar 

  51. Juan, B.; Alvaro, C.C.: Informality costs: informal entrepreneurship and innovation in emerging economies. Strat. Entrep. J. 14(3), 329–368 (2020)

    Google Scholar 

  52. Rob, J.M.; Viola, A.; Jochen, O.M.; Laura, V.: Moral hazard and selection for voluntary deductibles. Health Econom. 29(10), 1251–1269 (2020)

    Google Scholar 

  53. Zhao, K.: Research on Price Adjustment of Construction Contract Caused by Force Majeure. Proceedings of 2018 International Conference on Computer, Civil Engineering and Management Science (ICCEMS 2018).03, 223-229 (2018)

  54. Shen, J.R.; Ling, H.: The compound method for the excavation optimization and stability analysis of high rock slope. J. East China Inst. Technol. 35(004), 350–357 (2009)

    Google Scholar 

  55. Holland, J.N.; Deangelis, D.L.; Schultz, S.T.: Evolutionary stability of mutualism: interspecific population regulation as an evolutionarily stable strategy. Proc. Royal Soc. B: Biol. Sci. 271(1550), 1807–1814 (2014)

    Google Scholar 

  56. Yu, T.; Liu, C.Y.: Evolutionary game analysis and simulation of government and third party in product quality supervision. Chin. J. Manag. Sci. 06(24), 90–96 (2016)

    Google Scholar 

  57. Huang, H.T.; Liu, Q.M.; Ye, C.M.; Chen, X.: Game analysis of government procurement contract financing based on blockchain technology. J. Syst. Simul. 11, 1–11 (2020)

    Google Scholar 

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (No. 71871144). The authors are grateful to the editors and anonymous reviewers for their suggestions to improve the quality of the manuscript. Declaration of competitive interests

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Correspondence to Bo Wang.

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Tao, Z., Wang, B. & Shu, L. Analysis on the Procurement Cost of Construction Supply Chain based on Evolutionary Game Theory. Arab J Sci Eng 46, 1925–1940 (2021). https://doi.org/10.1007/s13369-020-05261-4

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  • DOI: https://doi.org/10.1007/s13369-020-05261-4

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