Soft Computing

, Volume 21, Issue 20, pp 6001–6018 | Cite as

A hybrid artificial bee colony for optimizing a reverse logistics network system

  • Jun-qing Li
  • Ji-dong Wang
  • Quan-ke Pan
  • Pei-yong Duan
  • Hong-yan Sang
  • Kai-zhou Gao
  • Yu Xue
Focus

Abstract

This paper proposes a hybrid discrete artificial bee colony (HDABC) algorithm for solving the location allocation problem in reverse logistics network system. In the proposed algorithm, each solution is represented by two vectors, i.e., a collection point vector and a repair center vector. Eight well-designed neighborhood structures are proposed to utilize the problem structure and can thus enhance the exploitation capability of the algorithm. A simple but efficient selection and update approach is applied to the onlooker bee to enhance the exploitation process. A scout bee applies different local search methods to the abandoned solution and the best solution found so far, which can increase the convergence and the exploration capabilities of the proposed algorithm. In addition, an enhanced local search procedure is developed to further improve the search capability. Finally, the proposed algorithm is tested on sets of large-scale randomly generated benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed HDBAC algorithm is shown against several efficient algorithms from the literature.

Keywords

Reverse logistics network Location allocation problem Artificial bee colony Neighborhood structure 

Notes

Acknowledgements

This research is partially supported by National Science Foundation of China under Grant 61573178, 61374187, 61603169, 51575212, and 61503170, basic scientific research foundation of Northeastern University under Grant N110208001, starting foundation of Northeastern University under Grant 29321006, Science Foundation of Liaoning Province in China (2013020016), and Science Research and Development of Provincial Department of Public Education of Shandong under Grant J12LN39, Postdoctoral Science Foundation of China (2015T80798, 2014M552040), and State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201602).

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Jun-qing Li
    • 1
    • 2
  • Ji-dong Wang
    • 3
  • Quan-ke Pan
    • 4
  • Pei-yong Duan
    • 2
  • Hong-yan Sang
    • 2
  • Kai-zhou Gao
    • 2
  • Yu Xue
    • 5
  1. 1.College of Computer ScienceLiaocheng UniversityLiaochengPeople’s Republic of China
  2. 2.State Key Laboratory of Synthetic Automation for Process IndustriesNortheastern UniversityShenyangPeople’s Republic of China
  3. 3.School of Electrical EngineeringNorth China University of Water Resources and Electric PowerZhengzhouPeople’s Republic of China
  4. 4.State Key Lab of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  5. 5.School of Computer and SoftwareNanjing University of Information Science and Technology (NUIST)NanjingPeople’s Republic of China

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