This paper aims to develop a hybrid genetic algorithm (HGA) to solve the resource constrained assembly line balancing problem (RCALBP) in the sewing line of a footwear manufacturing plant. Sewing, which is the most critical process in footwear manufacturing, has a series of processes, such as punching, trimming, attaching shoelaces. RCALBP in the sewing line considers not only the precedence constraints of product assembly but also the resource constraints, such as operators and equipment. A novel HGA that includes two stages is proposed to optimize the resources in the sewing line. The first stage uses the priority rule-based method (PRBM) to determine the feasible solutions of assigning tasks and machines to workstations. The solutions of PRBM are used to construct the initial population of genetic algorithm (GA) in the second stage. To ensure that the solution of GA is feasible, a two-point-order crossover with the new technique of searching feasible solution patterns is proposed. Moreover, the mutation procedure of GA is modified to avoid the building block from breaking, which may cause unfeasible solutions in RCALBP. A self-tuning method is also applied recursively to exclude unfeasible solutions. The proposed HGA is compared with the manual procedure adopted practically in factories, the existing heuristic model in the literature, and the traditional GA. Based on actual data from a footwear factory, computational results demonstrate that the proposed HGA can achieve better results than the other algorithms.
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This study was supported by the National Science Council of Taiwan, ROC (Contract No. NSC 102-2221-E-007-123-MY3), and Pou Chen International Group.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Ağpak K, Gökçen H (2005) Assembly line balancing: two resource constrained cases. Int J Prod Econ 96:129–140CrossRefGoogle Scholar
Azadeh A, Sangari MS, Sangari E, Fatehi S (2015) A particle swarm algorithm for optimising inspection policies in serial multistage production processes with uncertain inspection costs. Int J Comput Integr Manuf 28:766–780CrossRefGoogle Scholar
Bautista J, Pereira J (2009) A dynamic programming based heuristic for the assembly line balancing problem. Eur J Oper Res 194:787–794CrossRefzbMATHGoogle Scholar
Ghosh S, Gagnon RJ (1989) A comprehensive review and analysis of the design, balancing and scheduling of assembly systems. Int J Prod Res 27:637–670CrossRefGoogle Scholar
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Publishing Company Inc, BostonzbMATHGoogle Scholar
Helgeson WB, Birnie DP (1961) Assembly line balancing using the ranked positional weight technique. J Ind Eng 12:394–398Google Scholar
Johnson RV (1988) Optimally balancing large assembly lines with FABLE. Manag Sci 34:240–253CrossRefGoogle Scholar
Kao H-H, Yeh D-H, Wang Y-H (2011) Resource constrained assembly line balancing problem solved with ranked positional weight rule. Rev Econ Finance 1:71–80Google Scholar
Kim YK, Kim YJ, Kim Y (1996) Genetic algorithms for assembly line balancing with various objectives. Comput Ind Eng 30:397–409CrossRefGoogle Scholar
Levitin G, Rubinovitz J, Shnits B (1995) Genetic algorithm for assembly line balancing. Int J Prod Econ 41:343–354CrossRefzbMATHGoogle Scholar
Marketline (2014) Footwear: global industry guide. Research and Markets, DublinGoogle Scholar
Otto A, Otto C (2014) How to design effective priority rules: example of simple assembly line balancing. Comput Ind Eng 69:43–52CrossRefGoogle Scholar
Özcan U, Toklu B (2009) A tabu search algorithm for two-sided assembly line balancing. Int J Adv Manuf Technol 43:822–829CrossRefzbMATHGoogle Scholar
Rada-Vilela J, Chica M, Cordón Ó, Damas S (2013) A comparative study of multi-objective ant colony optimization algorithms for the time and space assembly line balancing problem. Appl Soft Comput 13:4370–4382Google Scholar
Rahimi-Vahed AR, Mirghorbani SM, Rabbani M (2007) A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem. Soft Comput 11:997–1012CrossRefzbMATHGoogle Scholar
Rekiek B, De Lit P, Pellichero F, L’Eglise T, Fouda P, Falkenauer E, Delchambre A (2001) A multiple objective grouping genetic algorithm for assembly line design. J Intell Manuf 12:467–485CrossRefGoogle Scholar
Roshani A, Fattahi P, Roshani A, Salehi M, Roshani A (2012) Cost-oriented two-sided assembly line balancing problem: a simulated annealing approach. Int J Comput Integr Manuf 25:689–715CrossRefGoogle Scholar
Roshani A, Roshani A, Roshani A, Salehi M, Esfandyari A (2013) A simulated annealing algorithm for multi-manned assembly line balancing problem. J Manuf Syst 32:238–247CrossRefGoogle Scholar
Sabuncuoglu I, Erel E, Tanyer M (2000) Assembly line balancing using genetic algorithms. J Intell Manuf 11:295–310CrossRefGoogle Scholar
Scholl A (1999) Balancing and sequencing of assembly lines. Publications of Darmstadt Technical University, Institute for Business Studies (BWL), DarmstadtCrossRefGoogle Scholar
Scholl A, Fliedner M, Boysen N (2010) Absalom: Balancing assembly lines with assignment restrictions. Eur J Oper Res 200:688–701CrossRefzbMATHGoogle Scholar
Scholl A, Klein R (1997) SALOME: a bidirectional branch and bound procedure for assembly line balancing. Informs J Comput 9:319–334CrossRefzbMATHGoogle Scholar
Sprecher A (1999) Competitive branch-and-bound algorithm for the simple assembly line balancing problem. Int J Prod Res 37:1787–1816CrossRefzbMATHGoogle Scholar
Tasan SO, Tunali S (2008) A review of the current applications of genetic algorithms in assembly line balancing. J Intell Manuf 19:49–69CrossRefGoogle Scholar
Triki H, Mellouli A, Masmoudi F (2014) A multi-objective genetic algorithm for assembly line resource assignment and balancing problem of type 2 (ALRABP-2). J Intell Manuf. doi:10.1007/s10845-014-0984-6Google Scholar
Zha J, Yu J-J (2014) A hybrid ant colony algorithm for U-line balancing and rebalancing in just-in-time production environment. J Manuf Syst 33:93–102CrossRefGoogle Scholar