American Journal of Industrial and Business Management, 2016, 6, 674-696 Published Online May 2016 in SciRes. http://www.scirp.org/journal/ajibm http://dx.doi.org/10.4236/ajibm.2016.65063 How to cite this paper: Sivasankaran, P. and Shahabudeen, P.M. (2016) Design and Comparison of Genetic Algorithms for Mixed-Model Assembly Line Balancing Problem with Original Task Times of Models. American Journal of Industrial and Business Management, 6, 674-696. http://dx.doi.org/10.4236/ajibm.2016.65063 Design and Comparison of Genetic Algorithms for Mixed-Model Assembly Line Balancing Problem with Original Task Times of Models Panneerselvam Sivasankaran 1 , Peer Mohamed Shahabudeen 2 1 Department of Mechanical Engineering, Manakula Vinayagar Institute of Technology, Pondicherry, India 2 Department of Industrial Engineering, College of Engineering, Anna University, Chennai, India Received 30 April 2016; accepted 28 May 2016; published 31 May 2016 Copyright © 2016 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Assembly line balancing is a key for organizational productivity in terms of reduced number of workstations for a given production volume per shift. Mixed-model assembly line balancing is a reality in many organizations. The mixed-model assembly line balancing problem comes under combinatorial category. So, in this paper, an attempt has been made to develop three genetic algo- rithms for the mixed-model assembly line balancing problem such that the combined balancing efficiency is maximized, where the combined balancing efficiency is the average of the balancing efficiencies of the individual models. At the end, these three algorithms and another algorithm in literature are compared in terms of balancing efficiency using a randomly generated set of prob- lems through a complete factorial experiment, in which “Algorithm”, “Problem Size” and “Cycle Time” are used as factors with two replications under each of the experimental combinations to draw inferences and to identify the best of the four algorithms. Then, through another set of ran- domly generated small and medium size data, the results of the best algorithm are compared with the optimal results obtained using a mathematical model. It is found that best algorithm gives the optimal solution for all the problems in the second set of data, except for one problem which can- not be solved using the model. This observation supports the fact that the best algorithm identi- fied in this paper gives superior results. Keywords Assembly Line Balancing, Cycle Time, Genetic Algorithm, Crossover Operation, Mixed-Model, Mathematical Model