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


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.


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



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.


  1. Akay B, Karaboga DA (2012) Modified artificial bee colony algorithm for real-parameter optimization. Inform Sci 192:120–142. CrossRefGoogle Scholar
  2. Aslam T, Ng AHC (2010) Multi-objective optimization for supply chain management: a literature review and new development. In: 2010 8th international conference on supply chain management and information systems (SCMIS) pp 1–8Google Scholar
  3. Banharnsakun A, Sirinaovakul B, Achalakul T (2012) Job shop scheduling with the best-so-far ABC. Eng Appl Artif Intel 25:583–593. CrossRefGoogle Scholar
  4. Boccaletti S, Latora V, Moreno Y et al (2006) Complex networks: structure and dynamics. Phys Rep 424:175–308. MathSciNetCrossRefzbMATHGoogle Scholar
  5. Bolaji AL, Khader AT, Al-Betar MA et al (2013) Artificial bee colony algorithm, its variants and applications: a survey. J Theor Appl Inform Tech 47:434–459Google Scholar
  6. Corner JL, Buchanan JT (1995) Experimental consideration of preference in decision making under certainty. J Multi-Criteria Decis Anal 4:107–121CrossRefGoogle Scholar
  7. Ebubekir K (2010) Bees algorithm: theory, improvements and applications. Cardiff University, Cardiff UniversityGoogle Scholar
  8. Goetschalckx M, Vidal CJ, Dogan K (2002) Modeling and design of global logistics systems: a review of integrated strategic and tactical models and design algorithms. Eur J Oper Res 143:1–18. CrossRefzbMATHGoogle Scholar
  9. Gou QL, Liang L, Huang ZM et al (2017) Supply chain management, sustainability, and productivity efficiency evaluations Introduction. Int J Inf Tech Decis 16:899–905CrossRefGoogle Scholar
  10. Kamali A, Ghomi SMTF, Jolai FA (2011) multi-objective quantity discount and joint optimization model for coordination of a single-buyer multi-vendor supply chain. Comput Math Appl 62:3251–3269. MathSciNetCrossRefzbMATHGoogle Scholar
  11. Karaboga D, Gorkemli B, Ozturk C et al (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42:21–57. CrossRefGoogle Scholar
  12. Ke, Huang H, Gao X (2017) Pricing decision problem in dual-channel supply chain based on experts belief degrees. Soft Comput.
  13. Kleindorfer PR, Kalyan S, Wassenhove LN (2005) Sustainable operations management. Prod Oper Manag 14:482–492. CrossRefGoogle Scholar
  14. Li R, Hu S, Wang Y et al (2017) A local search algorithm with tabu strategy and perturbation mechanism for generalized vertex cover problem. Neural Comput Appl 28:1775–1785. CrossRefGoogle Scholar
  15. Linton JD, Klassen R, Jayaraman V (2007) Sustainable supply chains: an introduction. J Oper Manag 25:1075–1082. CrossRefGoogle Scholar
  16. Mastrocinque E, Yuce B, Lambiase A (2013) A multi-objective optimization for supply chain network using the bees algorithm. Int J Eng Bus Manag 5:1–11. CrossRefGoogle Scholar
  17. Moncayo-Martnez LA, Zhang DZ (2011) Multi-objective ant colony optimisation: a meta-heuristic approach to supply chain design. Int J Prod Econ 131:407–420. CrossRefGoogle Scholar
  18. Moncayo-Martnez LA, Zhang DZ (2013) Optimising safety stock placement and lead time in an assembly supply chain using bi-objective MAXMIN ant system. Int J Prod Econ 145:18–28. CrossRefGoogle Scholar
  19. Nasiri GR, Davoudpour H, Karimi B et al (2010) A lagrangian-based solution algorithm for strategic supply chain distribution design in uncertain environment. Int J Inf Tech Decis 9:393–418. CrossRefzbMATHGoogle Scholar
  20. Nemati Y, Alavidoost MH (2018) A fuzzy bi-objective MILP approach to integrate sales, production, distribution and procurement planning in a FMCG supply chain. Soft Comput.
  21. Pasandideh SH, Niaki ST, Asadi K et al (2015) Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA. Inform Sci 292:57–74. MathSciNetCrossRefzbMATHGoogle Scholar
  22. Pham DT, Ghanbarzadeh A, Ko E et al. (2006) The bees algorithm a novel tool for Complex optimisation problems. Intel Prod Mach Syst.
  23. Pishvaee MS, Rabbani M, Torabi SA (2011) A robust optimization approach to closed-loop supply chain network design under uncertainty. Appl Math Model 35:637–649. MathSciNetCrossRefzbMATHGoogle Scholar
  24. Schiezaro M, Pedrini H (2013) Data feature selection based on artificial bee colony algorithm. Eurasip J Image Vid 1:1–8. CrossRefGoogle Scholar
  25. Schtz P, Tomasgard A, Ahmed S (2009) Supply chain design under uncertainty using sample average approximation and dual decomposition. Eur J Oper Res 199:409–419. CrossRefzbMATHGoogle Scholar
  26. Seifert RW, Zequeira RI, Liao S et al (2012) A three-echelon supply chain with price-only contracts and sub-supply chain coordination. Int J Prod Econ 138:345–353. CrossRefGoogle Scholar
  27. Shaw K, Shankar R, Yadav SS et al (2012) Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Syst Appl 39:8182–8192. CrossRefGoogle Scholar
  28. Sharma S, Bhambu P (2016) Artificial bee colony algorithm: a survey. Int J Comput Appl 149:11–19. CrossRefGoogle Scholar
  29. Shen L, Olfat L, Govindan K et al (2013) A fuzzy multi criteria approach for evaluating green suppliers performance in green supply chain with linguistic preferences. Resour Conserv Recycl 74:170–179. CrossRefGoogle Scholar
  30. Shu T, Gao X, Chen S et al (2016) Weighing efficiency-robustness in supply chain disruption by multi-objective firefly algorithm. Sustainability 8:1–27. CrossRefGoogle Scholar
  31. Shukla A, Lalit VA, Venkatasubramanian V et al (2013) Optimizing efficiency-robustness trade-offs in supply chain design under uncertainty due to disruptions. Int J Phys Distrib Logist Manag., pp 623–647. CrossRefGoogle Scholar
  32. Tan KC (2001) A framework of supply chain management literature. Eur J Med Chem 7:39–48. CrossRefGoogle Scholar
  33. Wang L, Tian F, Soong BH et al. (2011) Solving combinatorial optimization problems using augmented lagrange chaotic simulated annealing. Differ Equ Dyn Syst.
  34. Wu X, Kumar V, Quinlan JR et al (2007) Top 10 algorithms in data mining. Knowel Inf Syst 14:1–37. CrossRefGoogle Scholar
  35. Yang W, Pei Z (2013) Hybrid ABC/PSO to solve travelling salesman problem. Int J Comput Sci Math 4:214–221. MathSciNetCrossRefGoogle Scholar
  36. Yuce B, Packianather MS, Mastrocinque E et al (2013) Honey bees inspired optimization method: the bees algorithm. Insects 4(2013):646–662CrossRefGoogle Scholar
  37. Yuce B, Mastrocinque E, Lambiase A et al (2014) A multi-objective supply chain optimisation using enhanced bees algorithm with adaptive neighbourhood search and site abandonment strategy. Swarm Evol Comput 18:71–82CrossRefGoogle Scholar
  38. Yuce B, Mastrocinque E, Packianather MS et al (2015) The bees algorithm and its application. In: Vasant PM (ed) Handbook of research on artificial intelligence techniques and algorithms, chap 4, pp 122–151.
  39. Zhang S, Lee CKM, Yu KM, Lau HCW (2017) Design and development of a unified framework towards swarm intelligence. Artif Intell Rev 47:253–277CrossRefGoogle Scholar
  40. Zhang LL, Lee C, Zhang S (2016) An integrated model for strategic supply chain design: formulation and ABC-based solution approach. Expert Syst with Appl 52:39–49CrossRefGoogle Scholar
  41. Zhou XY, Tu Y, Han J et al (2017) A class of Level-2 Fuzzy decision-making model with expected objectives and chance constraints: application to supply chain network design. Int J Inf Tech Decis 16:907–938CrossRefGoogle Scholar
  42. Zhang C, Yang Y, Du Z et al (2016) Particle swarm optimization algorithm based on ontology model to support cloud computing applications. J Amb Intel Hum Comp 7:633–638. CrossRefGoogle Scholar

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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