Abstract
The relationship among many subsystems in multi-agent complex systems is difficult to quantify and the coordination among multiple processes in multivariate complex processes is hard to clear analysis. In order to solve the problems this paper presents a seesaw model for the basic dual relation of the complex systems and complex processes. The seesaw model can be applied in various fields and in all aspects of people’s lives. In this paper, the application of this model is derived, and the pre-distribution model of distribution industry is obtained. We analyze the time factor of the distribution and optimize the distribution process by using the seesaw model. Concorde process makes the dissatisfaction degree of distribution service decreased. Pre-distribution makes that delivery speed and delivery efficiency are improved and ensures that dissatisfaction degree of distribution service is effectively reduced.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, C., Lan, F., Dai, Y.: National critical infrastructure modeling and analysis based on complex system theory. In: 1st International Conference on Instrumentation, Measurement, Computer, Communication and Control. IEEE, pp. 832–836 (2011)
Huang, J., Feng, Y., Zhang, S.: Research of complex system theory application on reliability analysis of network system. In: International Conference on Reliability, Maintainability and Safety. IEEE, pp. 1141–1145 (2009)
Dominici, G., Levanti, G.: The complex system theory for the analysis of inter-firm networks. A literature overview and theoretic framework. Int. Bus. Res. 4(2), (2011)
O’Brien, T.P., Sornette, D., Mcpherron, R.L.: Statistical asynchronous regression: determining the relationship between two quantities that are not measured simultaneously. J. Geophys. Res. Space Phys. 106(A7), 13247–13259 (2000)
Chamberlain, G.: Analysis of covariance with qualitative data. Rev. Econ. Stud. 47(1), 225–238 (1980)
Byrne, B.M., Shavelson, R.J., Muthén, B.: Testing for the equivalence of factor covariance and mean structures: the issue of partial measurement invariance. Psychol. Bull. 105(3), 456–466 (1989)
Karvanen, J.: Study design in causal models. Scand. J. Stat. 42(2), 361–377 (2015)
Otsuka, J.: Using causal models to integrate proximate and ultimate causation. Biol. Philos. 30(1), 19–37 (2015)
Reininghaus, U., Depp, C.A., Myingermeys, I.: Ecological interventionist causal models in psychosis: targeting psychological mechanisms in daily life. Schizophr. Bull. 42, 264–269 (2015)
Irvine, K.M., Miller, S.W., Al-Chokhachy, R.K., et al.: Empirical evaluation of the conceptual model underpinning a regional aquatic long-term monitoring program using causal modelling. Ecol. Ind. 50, 8–23 (2015)
Masa’Deh, R., Shannak, R.O., Obeidat, B.Y., et al.: Investigating a causal model of it-business partnership and competitive advantage. In: Global Business Transformation through Innovation and Knowledge Management: An Academic Perspective (2016)
Xiang, L.I., Liang, W.H.: On customer satisfaction evaluation of the distribtion service under the B2C environment. J. North China Electric Power University (2012)
Zhou, X., Guizhou Normal University: Study on TPL distribution service quality evaluation index system based on customer satisfaction. Logist. Technol. (2013)
Cui, L., Zhang, H., He, M., et al.: City distribution service quality: a factor impacting on supplier-customer relationship and customer satisfaction. In: International Conference on Logistics Systems and Intelligent Management. IEEE, pp. 813–816 (2010)
Wang, H., Wei, F.X.: Evaluation of service quality and customer satisfaction of distribution center-direct selling. J. Yibin University (2007)
Jueliang, H., Lihua, W., Han, S., et al.: Apparel distribution model and algorithm based on time satisfaction. J. Text. 31(2), 138–142 (2010)
Acknowledgments
This work was financially supported by the Project of Natural Science Foundation of Hainan Province in China (Grant No. 20166232), the National Natural Science Foundation of China (Grant No. 61561017), Hainan Province Natural Science Foundation of China (Grant No. 617033) and Open Sub-project of State Key Laboratory of Marine Resource Utilization in South China Sea (Grant No. 2016013B).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd
About this paper
Cite this paper
Xie, M., Deng, Y., Bai, Y., Huang, M., Hu, Z. (2017). Research on the Pre-distribution Model Based on Seesaw Model. In: Chen, G., Shen, H., Chen, M. (eds) Parallel Architecture, Algorithm and Programming. PAAP 2017. Communications in Computer and Information Science, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-6442-5_18
Download citation
DOI: https://doi.org/10.1007/978-981-10-6442-5_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6441-8
Online ISBN: 978-981-10-6442-5
eBook Packages: Computer ScienceComputer Science (R0)