Soft Computing

, Volume 21, Issue 22, pp 6881–6894 | Cite as

A modified ant colony optimization algorithm for multi-objective assembly line balancing

Methodologies and Application

Abstract

In this paper, a novel ant colony optimization algorithm called modified ant colony optimization algorithm (MACO) is proposed for multi-objective single-model assembly line balancing problem (SALBP). The proposed MACO presents a novel heuristic information combined with subsequent task number and deviation time that can guide ants to find better solutions for SALBP. The proposed MACO also adopts three assignment methods (i.e., forward, backward and local rebalancing assignment methods) and stratified sequential algorithm combined with Pareto-optimal front as multi-objective decision. The objectives of SALBP are to minimize the number of workstations, maximize assembly line efficiency and minimize workload variation among workstations. In the latter part of the paper, the proposed MACO has been applied to solve Scholl benchmark problems which include both small-size and large-size problems. The performance of the proposed MACO has been compared with the multi-objective genetic algorithm and the multiple assignment genetic algorithm and has obtained improved results in many test problems.

Keywords

Ant colony optimization algorithm Assembly line balancing Multi-objective Heuristic information 

References

  1. Al-Hawari T, Ali M, Al-Araidah O, Mumani A (2015) Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach. Int J Adv Manuf Technol 77:1419–1432. doi:10.1007/s00170-014-6545-5 CrossRefGoogle Scholar
  2. Bautista J, Pereira J (2007) Ant algorithms for a time and space constrained assembly line balancing problem. Eur J Oper Res 177:2016–2032CrossRefMATHGoogle Scholar
  3. Baybars I (1986) A survey of exact algorithms for the simple assembly line balancing problem. Manag Sci 32:909–932MathSciNetCrossRefMATHGoogle Scholar
  4. Becker C, Scholl A (2006) A survey on problems and methods in generalized assembly line balancing. Eur J Oper Res 168:694–715. doi:10.1016/j.ejor.2004.07.023 MathSciNetCrossRefMATHGoogle Scholar
  5. Bowman EH (1960) Assembly-line balancing by linear programming. Oper Res 8:385–389CrossRefMATHGoogle Scholar
  6. Boysen N, Fliedner M, Scholl A (2007) A classification of assembly line balancing problems. Eur J Oper Res 183:674–693CrossRefMATHGoogle Scholar
  7. Dou J, Li J, Su C (2013) A novel feasible task sequence-oriented discrete particle swarm algorithm for simple assembly line balancing problem of type 1. Int J Adv Manuf Technol 69:2445–2457. doi:10.1007/s00170-013-5216-2 CrossRefGoogle Scholar
  8. Gutiérrez C, García-Magariño I (2011) Modular design of a hybrid genetic algorithm for a flexible job-shop scheduling problem. Knowl Based Syst 24:102–112CrossRefGoogle Scholar
  9. Hamta N, Ghomi SMTF, Jolai F, Shirazi MA (2013) A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect. Int J Prod Econ 141:99–111CrossRefGoogle Scholar
  10. Held M, Karp RM, Shareshian R (1963) Assembly-line balancing-dynamic programming with precedence constraints. Oper Res 11:442–459CrossRefMATHGoogle Scholar
  11. Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann ArborGoogle Scholar
  12. Hou L, Wu YM, Lai RS, Tsai CT (2014) Product family assembly line balancing based on an improved genetic algorithm. Int J Adv Manuf Technol 70:1775–1786CrossRefGoogle Scholar
  13. Hwang RK, Katayama H, Gen M (2008) U-shaped assembly line balancing problem with genetic algorithm. Int J Prod Res 46:4637–4649. doi:10.1080/00207540701247906 CrossRefMATHGoogle Scholar
  14. Jackson JR (1956) A computing procedure for a line balancing problem. Manag Sci 2:261–271CrossRefGoogle Scholar
  15. Karp RM (1972) Reducibility among combinatorial problems. In: Miller RE, Thatcher JW (eds) Complexity of computer applications. Plenum Press, New York, pp 85–104CrossRefGoogle Scholar
  16. Kilincci O (2011) Firing sequences backward algorithm for simple assembly line balancing problem of type 1. Comput Ind Eng 60:830–839. doi:10.1016/j.cie.2011.02.001 CrossRefGoogle Scholar
  17. Li D, Zhang C, Shao X, Lin W (2014) A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints. J Intell Manuf. doi:10.1007/s10845-014-0919-2 Google Scholar
  18. Lv Q (2011) Simple assembly line balancing using particle swarm optimization algorithm. Int J Digit Content Technol Appl 5:297–304. doi:10.4156/jdcta.vol5.issue6.36 Google Scholar
  19. Ogiela L, Ogiela MR (2009) Cognitive techniques in visual data interpretation. In: Studies in computational intelligence, vol 228. Springer, Berlin, HeidelbergGoogle Scholar
  20. Ogiela L, Ogiela MR (2014) Cognitive systems for intelligent business information management in cognitive economy. Int J Inf Manag 34:751–760CrossRefMATHGoogle Scholar
  21. Petropoulos DI, Nearchou AC (2011) A particle swarm optimization algorithm for balancing assembly lines. Assem Autom 31:118–129Google Scholar
  22. Salveson ME (1955) The assembly line balancing problem. J Ind Eng 6:18–25MathSciNetGoogle Scholar
  23. Scholl A (1993) Data of assembly line balancing problems. Publications of Darmstadt Technical University Institute for Business StudiesGoogle Scholar
  24. Scholl A, Klein R (2007) Assembly line balancing. http://alb.mansci.de/
  25. Sfrent A, Pop F (2015) Asymptotic scheduling for many task computing in big data platforms. Inf Sci 319:71–91MathSciNetCrossRefGoogle Scholar
  26. Vasile MA, Pop F, Tutueanu RI, Cristea V, Kolodziej J (2015) Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing. Future Gener Comput Syst 51:61–71CrossRefGoogle Scholar
  27. Yagmahan B (2011) Mixed-model assembly line balancing using a multi-objective ant colony optimization approach. Expert Syst Appl 38:12453–12461. doi:10.1016/j.eswa.2011.04.026 CrossRefGoogle Scholar
  28. Yu J, Yin Y (2010) Assembly line balancing based on an adaptive genetic algorithm. Int J Adv Manuf Technol 48:347–354. doi:10.1007/s00170-009-2281-7 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.College of Mechanical and Electrical EngineeringHarbin Engineering UniversityHarbinChina

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