Assembly Line Balancing As A Key For Organizational Productivity

Assembly line balancing is a key for organizational productivity which was first stated by Helgeson et al (1954). But it’s mathematical form and linear programming was first suggested by Salveson (1955). Salveson (1955) used linear programming to solve single model ALB. Further integer programming, was used by Bowmen (1960); Klein (1963); Patterson and Albracht (1975).

Dynamic programming for single model ALB was developed by Jackson (1988); Held et al (1963); Scharge and Baker (1978). Then heuristic approaches was developed to take ALB a step ahead and it was studied by Dar-El’s maLB (1973), Baybers (1986) Tonge’s (1965), Moodie and Young (1965) and Nevin (1972). Genetic algorithm in ALB was used by Holland (1975). Further application was discovered by Goldberg (1989). Anderson and Ferris(1994) came up with more advanced use of GA in ALB. GA was used to solve a SMS problem by Suresh et al (1996). Ponnambalam et al (2000) developed a multi-objective genetic algorithm for solving the single model assembly line balancing problem for a given cycle time. The objectives include reducing the number of workstations.Another genetic algorithm was developed by Sabuncuoglu et al(2000) to find the minimum number of workstations.To maximize the balancing efficiency Goncalves and Almeida(2002)developed a hybrid genetic algorithm.

Assembly line balancing was further introduced to mixed model from single model and it was done by Thomopoulos (1970). Non-linear integer programming was proposed by Aslein and Zhou (1997) for mixed model ALB. Then comes the stochastic task time introduced by McMullen and Frazier (1997). Gokcen and Erel (1997) proposed binary goal programming method for mixed model line balancing. Further Gokcen and Erel (1998) developed a binary integer programming. In order to minimize the rate of incomplete jobs and to reduce work in process, Merengo Nava and Pozzetti (1999) came up with a new line balancing sequencing method. Kim and Kim (2000) suggested an algorithm to solve mixed model assembly line balancing problem. A new model was introduced by Matanachai and Yana (2001) which was heuristic filtered beam search algorithm. Jin and Wu (2002) further developed heuristic algorithm which was known as a “Variance algorithm”.

Genetic algorithm was used to solve mixed model line balancing problem which was introduced by Ponnambalam et al (2003).To minimize number of workstation and total idle time Chutima and Iammi (2003) developed a genetic algorithm. Further Haq et al (2006) proposed hybrid genetic algorithm for mixed model line balancing problem. Su and Lu (2007) designed mixed model line balancing to smooth workload balance within each workstation using genetic algorithm. Another method for solving mixed model line balancing is simulated annealing algorithm which was discovered by Vilarinho and simaria (2002). Fattahi and Salehi (2009) developed simulated annealing algorithm to minimize idle cost.

To minimize the no of workstations Ozcan et al (2010) used simulated annealing algorithm. Ozcan et al (2010) again considered another new model to assemble one or more product lines and it was named Tabu search algorithm. Vilarinho and Simaria (2009) proposed an Ant colony optimization to minimize the number of workstations in mixed model assembly line balancing problem. Further Yagmahan (2011) developed a multi objective ACO algorithm to minimize the number of workstation. Akpinar et al (2013) suggested a hybrid ant colony optimization algorithm based on minimizing the workstation. We are looking for a hybrid algorithm using ACO and GA to solve mixed model single objective assembly line balancing problems.