Solving Two-Sided Assembly Line Balancing Problems Using an Integrated Evolution and Swarm Intelligence

  • Hindriyanto Dwi PurnomoEmail author
  • Hui-Ming Wee
  • Yugowati Praharsi
Conference paper


Assembly line balancing problem (ALBP) is an important problem in manufacturing due to its high investment cost. The objective of the assembly line balancing problem is to assign tasks to workstations in order to minimize the assembly cost, fulfill the demand and satisfy the constraints of the assembly process. In this study, a novel optimization method which integrates the evolution and swarm intelligence algorithms is proposed to solve the two-sided assembly line balancing problems. The proposed method mimics the basic soccer player movement where there are two main movements, the move off and the move forward. In this paper, the move off and the move forward are designed based on the specific features of two-sided assembly line balancing problems. Prioritize tasks and critical tasks are implemented in the move off and move forward respectively. The performance of the proposed method is compared to the heuristic and ant colony based method mentioned in the literature.


Move off Move forward Two-sided assembly lines 


  1. Arcus A (1966) COMSOAL: a computer method of sequencing operations for assembly lines. Int J Prod Res 4:259–277CrossRefGoogle Scholar
  2. Askin RG, Standridge CR (1993) Modeling and analysis of manufacturing systems. Wiley, FloridazbMATHGoogle Scholar
  3. Bartholdi JJ (1983) Balancing two-sided assembly lines: a case study. Int J Prod Res 31:2447–2461CrossRefGoogle Scholar
  4. Boysen N, Fliedner M, Scholl A (2007) A classification of assembly line balancing problems. Eur J Oper Res 183(2):674–693MathSciNetzbMATHCrossRefGoogle Scholar
  5. Gamberini R, Grassi A, Rimini B (2006) A new multi-objective heuristic algorithm for solving the stochastic assembly line re-balancing problem. Int J Prod Econ 102:226–243CrossRefGoogle Scholar
  6. Gutjahr AL, Nemhauser GL (1964) An algorithm for the line balancing problem. Manage Sci 11(2):308–315MathSciNetzbMATHCrossRefGoogle Scholar
  7. Kim YK, Kim Y, Kim YJ (2000) Two-sided assembly line balancing: a genetic algorithm approach. Prod Plann Control 11:44–53CrossRefGoogle Scholar
  8. Kim YK, Song WS, Kim JH (2009) A mathematical model and a genetic algorithm for two-sided assembly line balancing. Comput Oper Res 36(3):853–865zbMATHCrossRefGoogle Scholar
  9. Lee TO, Kim Y, Kim YK (2001) Two-sided assembly line balancing to maximize work relatedness and slackness. Comput Ind Eng 40(3):273–292CrossRefGoogle Scholar
  10. Nearchou AC (2011) Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization. Int J Prod Econ 129(2):242–250CrossRefGoogle Scholar
  11. Özbakır L, Tapkan P (2011) Bees colony intelligence in zone constrained two-sided assembly line balancing problem. Expert Syst Appl 38(9):11947–11957CrossRefGoogle Scholar
  12. Özcan U (2010) (2010) Balancing stochastic two-sided assembly lines: a chance-constrained, piecewise-linear, mixed integer program and a simulated annealing algorithm. Eur J Oper Res 205(1):81–97zbMATHCrossRefGoogle Scholar
  13. Purnomo HD, Wee HM (2012) Soccer game optimization: an innovative integration of evolutionary algorithm and swarm intelligence algorithm. In: Vasant P (eds) Meta-Heuristics optimization algorithms in engineering, business, economics, and finance. IGI Global, PennsylvaniaGoogle Scholar
  14. Simaria AS, Vilarinho PM (2009) 2-ANTBAL: an ant colony optimization algorithm for balancing two-sided assembly lines. Comput Ind Eng 56(2):489–506CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2013

Authors and Affiliations

  • Hindriyanto Dwi Purnomo
    • 1
    Email author
  • Hui-Ming Wee
    • 2
  • Yugowati Praharsi
    • 1
  1. 1.Department of Information TechnologySatya Wacana Christian UniversitySalatigaIndonesia
  2. 2.Department of Industrial and System EngineeringChung Yuan Christian UniversityChungliTaiwan, Republic of China

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