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Optimization of assembly line balancing using genetic algorithm

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Abstract

In a manufacturing industry, mixed model assembly line (MMAL) is preferred in order to meet the variety in product demand. MMAL balancing helps in assembling products with similar characteristics in a random fashion. The objective of this work aims in reducing the number of workstations, work load index between stations and within each station. As manual contribution of workers in final assembly line is more, ergonomics is taken as an additional objective function. Ergonomic risk level of a workstation is evaluated using a parameter called accumulated risk posture (ARP), which is calculated using rapid upper limb assessment (RULA) check sheet. This work is based on the case study of an MMAL problem in Rane (Madras) Ltd. (India), in which a problem based genetic algorithm (GA) has been proposed to minimize the mentioned objectives. The working of the genetic operators such as selection, crossover and mutation has been modified with respect to the addressed MMAL problem. The results show that there is a significant impact over productivity and the process time of the final assembled product, i.e., the rate of production is increased by 39.5% and the assembly time for one particular model is reduced to 13 min from existing 18 min. Also, the space required using the proposed assembly line is only 200 m2 against existing 350 m2. Further, the algorithm helps in reducing workers fatigue (i.e., ergonomic friendly).

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Authors and Affiliations

  1. School of Mechanical Engineering, SASTRA University, 613401, Tamilnadu, Thanjavur, India

    N. Barathwaj & P. Raja

  2. Rane (Madras) Ltd., Deputy Manager (Manufacturing Engineering System Design Department), 600042, Chennai, Tamil Nadu, Velachery, India

    S. Gokulraj

Authors
  1. N. Barathwaj
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  2. P. Raja
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  3. S. Gokulraj
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Correspondence to N. Barathwaj.

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Barathwaj, N., Raja, P. & Gokulraj, S. Optimization of assembly line balancing using genetic algorithm. J. Cent. South Univ. 22, 3957–3969 (2015). https://doi.org/10.1007/s11771-015-2940-9

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  • DOI: https://doi.org/10.1007/s11771-015-2940-9

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