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Soft Computing

, Volume 23, Issue 24, pp 13321–13337 | Cite as

Fast artificial bee colony algorithm with complex network and naive bayes classifier for supply chain network management

  • Jianhua JiangEmail author
  • Di Wu
  • Yujun Chen
  • Dianjia Yu
  • Limin Wang
  • Keqin LiEmail author
Methodologies and Application
  • 117 Downloads

Abstract

In supply chain network (SCN) management, multi-objective Pareto optimization means the network can meet the demand for both minimal cost and minimal lead-time in SCN. Due to the compromise between cost and lead-time, it is a non-trivial issue to search for multi-objective Pareto optimal solutions (POS) in SCN. Furthermore, with the wide application of the internet, an increasing number of SCN applications have been based on the internet. As a result, the complexity of SCN increases exponentially with the number of suppliers increasing. It is really a big challenge to find the global multi-objective POS within a limited time in SCN management. In order to solve this problem, first, this paper proposes an artificial bee colony (ABC) optimization algorithm with two improvements: (1) a novel solution framework designed to extend the application field of the SCN based on complex network; (2) the acceleration of search speed by adopting naive Bayes classifier. Second, the paper provides a case example of optimizing a three-echelon SCN with the objective of minimizing both cost and lead-time. After the simulation with this example, it turns out that the enhanced ABC algorithm can satisfy the requirements of: (1) finding the global multi-objective POS; (2) improving the speed of finding optimal solutions in SCN management.

Keywords

Artificial bee colony Complex network Multi-objective optimization Pareto optimal solutions Three-echelon supply chain 

Notes

Acknowledgements

The authors are grateful to the financial support by the National Natural Science Foundation of China (No. 61572225), the Natural Science Foundation of the Science and Technology Department of Jilin Province, China (No. 20180101044JC), the Foundation of the Education Department of Jilin Province, China (No. JJKH20180465KJ), the Foundation of Social Science of Jilin Province, China (No. 2017BS28) and the Foundation of Jilin University of Finance and Economics (No. 2018Z05).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Management Science and Information EngineeringJilin University of Finance and EconomicsChangchunChina
  2. 2.Department of Computer ScienceState University of New YorkNew PaltzUSA

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