Optimization of assembly line balancing using genetic algorithm

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).

Keywords

optimization / line balancing / genetic algorithm / product family / assembly line

References

[1] HouL, WuY M, LaiR S, TsaiC T. Product family assembly line balancing based on an improved genetic algorithm [J]. International Journal of Advanced Manufacturing Technology, 2014, 70: 1775-1786

[2] KimY K, SongW S, KimJ H. A mathematical model and a genetic algorithm for two-sided assembly line balancing [J]. Computers and Operations Research, 2009, 36(3): 853-865

[3] SimariaA S, VilarinhoP M. A genetic algorithm based approach to the mixed model assembly line balancing problem of type II [J]. Computers and Industrial Engineering, 2004, 47(4): 391-407

[4] RubinovitzJ, LevitinG. Genetic algorithm for assembly line balancing [J]. International Journal of Production Economics, 1995, 41: 343-354

[5] SchollA, FliednerM, BoysenN. ABSALOM: Balancing assembly lines with assignment restrictions [J]. European Journal of Operational Research, 2010, 200(3): 688-701

[6] OttoA, SchollA. Incorporating ergonomic risk into assembly line balancing [J]. European Journal of Operational Research, 2011, 212(2): 277-286

[7] CheshmehgazH R, HaronH, KazemipourF, DesaM I. Accumulated risk of body postures in assembly line balancing problem and modeling through a multi-criteria fuzzy-genetic algorithm [J]. Computers & Industrial Engineering, 2012, 63(2): 503-512

[8] BridgerR SIntroduction to ergonomics [M], 2003LondonTaylor & Francis1-186

[9] RashidM F F, HutabaratW, TiwariA. A review on assembly sequence planning and assembly line balancing optimization using soft computing approaches [J]. International Journal of Advanced Manufacturing Technology, 2012, 59(1/2/3/4): 335-349

[10] ZhuX, HuS J, KorenY, HuangN. A complexity model for sequence planning in mixed-model assembly lines [J]. Journal of Manufacturing Systems, 2011, 31(2): 121-130

[11] KalayciC B, PolatO, GuptaS M. A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem [J]. Annals of Operations Research, 2014

[12] BattiniD, FaccioM, FerrariE, PersonaA, SgarbossaF. Design configuration for a mixed-model assembly system in case of low product demand [J]. International Journal of Advanced Manufacturing Technology, 2007, 34(1/2): 188-200

[13] SandanayakeY G, OduozaC F. Dynamic simulation for performance optimization in just-in-time-enabled manufacturing processes [J]. International Journal of Advanced Manufacturing Technology, 2009, 42(3/4): 372-380

[14] AkpinarS, BaykasogluA. Modeling and solving mixed-model assembly line balancing problem with setups. Part-1: A mixed integer linear programming model [J]. Journal of Manufacturing System, 2014, 33(1): 177-187

[15] MoradiH, ZandiehM. An imperialist competitive algorithm for a mixed-model assembly line sequencing problem [J]. Journal of Manufacturing Systems, 2013, 32(1): 46-54

[16] ChenJ C, WuC W, ThaoT, DD, SuL H, HsiehW H, ChenT. Hybrid genetic algorithm for solving assembly line balancing problem in footwear industry [J]. Advanced Materials Research, 2014, 939: 621-629

[17] www.osha.gov

[18] www.rula.co.uk

[19] Al-HawariT, AliM, Al-AraidahO, MumaniA. Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach [J]. International Journal of Advanced Manufacturing Technology, 2015, 77: 1419-1432

[20] RajaP, AbhilashM, ShankarK R, AdarshA. Quadrant based incremental planning for mobile robots [J]. Journal of Central South University, 2014, 21(5): 1792-1803

[21] KumarM, DM, RajaP. Design and simulation of radial control in continuous variable transmission pulley [J]. International Journal of Applied Engineering Research, 2014, 9(22): 14019-14039

[22] RajaP, PugazhenthiS. Online path planning for mobile robots in dynamic environments [J]. Neural Network World, 2012, 22(1): 67-83

[23] RajaP, PugazhenthiS. Path planning for a mobile robot in dynamic environments [J]. International Journal of Physical Sciences, 2011, 6(20): 4721-4731

[24] RajaP, PugazhenthiS. Path planning for mobile robots in dynamic environments using particle swarm optimization [C]. roc 2009 Int Conf on Advances in Recent Technologies in Communication and Computing. Kottayam, 2009401-405