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Analytical Study on Automobile Engine Line Work Stations and Analyze Their Working

Tushar Khirwadkar, Vivek Babele

Abstract


In an automobile assembly line, a series of stations are arranged along a conveyor belt and an automated guided vehicle performs on tasks at each station. Parallel assembly lines can provide improving line balance, productivity and so on. Combining robotic and parallel assembly lines ensure increasing flexibility of system, capacity and decreasing breakdown sensitivity. Although afore mentioned benefits, balancing of robotic parallel assembly lines is lacking – to the best knowledge of the authors- in the literature. Therefore, an observed study is proposed to define/solve the problem of automobile assembly line. The automobile assembly line also tested on the generated benchmark problems for automated guided vehicle/robotic parallel assembly line balancing problem. The superior performances of the proposed algorithms are verified by using a statistical test. The results show that the algorithms are very competitive and promising tool for further researches in the literature.

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M. Aghajani, R. Ghodsi, B. Javadi. Balancing of robotic mixed-model twosided assembly line with robot setup times, Int J Adv Manuf Technol. 2014; 74(5): 1005e1016p.

J. Arkat, M. Saidi, B. Abbasi. Applying simulated annealing to cellular manufacturing system design, Int J Adv Manuf Technol. 2007; 32(5): 531e536p.

J.J. Bartholdi. Balancing two-sided assembly lines: a case study, Int J Prod Res. 1993; 31(10): 2447e2461p.

Y.A. Bozer, L.F. McGinnis. Kitting versus line stocking: a conceptual framework and a descriptive model, Int J Prod Econ. 1992; 28(1): 1–19p.

H. Brynzér, M.I. Johansson. Design and performance of kitting and order picking systems, Int J Prod Econ. 1995; 41(1–3): 115–25p.

P. Chutima, P. Chimklai. Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimization with negative knowledge, Comput Ind Eng. 2012; 62(1): 39e55p.

A.C. Caputo, P.M. Pelagagge. A methodology for selecting assembly systems feeding policy, Ind Manage Data Syst. 2011; 111(1): 84–112p.

D.-M. Tsai, M.-J. Yao. A line-balanced-base capacity planning procedure for series-type robotic assembly line, Int J Prod Res. 1993; 31(8): 1901e1920p.

B. Cheldelin, K. Ishii. Mixed model assembly quality: an approach to prevent human errors, In: International Mechanical Engineering Congress & Exposition. 2004, 1–11p.

M.A. Corakci. An Evaluation of Kitting System in Lean Production. School of Engineering: University of Boras, 2008.

M. Dai, D. Tang, A. Giret, M.A. Salido, W.D. Li. Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm, Rob Comput Integr Manuf. 2013; 29(5): 418e429p.

K. Deb, A. Pratap, S. Agarwal, T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans Evolut Comput. 2002; 6(2): 182e197p.

R. De Koster, T. Le-Duc, K.J. Roodbergen. Design and control of warehouse order picking: a literature review, Eur J Operat Res. 2007; 182(2): 481–501p.

S. Deechongkit, R. Srinon. Three alternatives approaches of material supply in assembly line: a comparative study, In: Asia Pacific Industrial Engineering & Management System. Kitakyushu, Japan, 2009.

M.L. Fisher, C.D. Ittner. The impact of product variety on automobile assembly operations: Empirical evidence and simulation analysis, Manage Sci. 1999; 45(6): 771–86p.

M. Friedman. The use of ranks to avoid the assumption of normality implicit in the analysis of variance, J Am Stat Assoc. 1937; 32: 675e701p.

A. Fysikopoulos, D. Anagnostakis, K. Salonitis, G. Chryssolouris. An empirical study of the energy consumption in automotive assembly, Procedia CIRP. 2012; 3: 477e482p.

J. Gao, L. Sun, L. Wang, M. Gen. An efficient approach for type II robotic assembly line balancing problems, Comput Ind Eng. 2009; 56(3): 1065e1080p.

J. Golz, R. Gujjula, H.-O. Günther, S. Rinderer, M. Ziegler. Part feeding at high-variant mixed model assembly lines, Flex Services Manuf J. 2012; 24: 119–41p.

Y. He, B. Liu, X. Zhang, H. Gao, X Liu. A modeling method of task-oriented energy consumption for machining manufacturing system, J Clean Prod. 2012; 23(1): 167e174p.

X.-F. Hu, E.-F. Wu, J. Ye. A station-oriented enumerative algorithm for twosided assembly line balancing, Eur J Opt Res. 2008; 186(1): 435e440p.

X.-F. Hu, E.-F. Wu, J.-S. Bao, J. Ye. A branch-and-bound algorithm to minimize the line length of a two-sided assembly line, Eur J Opt Res. 2010; 206(3): 703e707p.

Hanson, A. Brolin. A comparison of kitting and continuous supply in inplant materials supply, Int J Prod Res. 2012; 1–14p.

Hanson, Medbo. Kitting and time efficiency in manual assembly, Int J Prod Res. 2011; 1–11p.

Hua, S. Y., & Johnson, D. J. (2008). Research issues on factors influencing the choice of kitting versus line stocking. International Journal of Production Research, 48(3), 779–800p.

Kim, Y.K., Kim, Y., Kim, Y.J., 2000. Two-sided assembly line balancing: a genetic algorithm approach. Prod. Plan. Control 11 (1), 44e53p.

Kim, Y.K., Song,W.S., Kim, J.H., 2009. A mathematical model and a genetic algorithm for two-sided assembly line balancing. Comput. Op. Res. 36 (3), 853e865p.

Kim, H., Park, S., 1995. Strong cutting plane algorithm for the robotic assembly line balancing. Int. Journal of Prod. Res. 33 (8), 2311e2323p.

Kirkpatrick, S., Gelatt, C.D., Veechi, M.P., 1983. Optimization by simulated annealing. Science 220 (4598), 671e679p.

Khorasanian, D., Hejazi, S.R., Moslehi, G., 2013. Two-sided assembly line balancing considering the relationships between tasks. Comput. Ind Eng. 66 (4),1096e1105p.

S. Kulturel-Konak, A.E. Smith, B.A. Norman. Multiobjective tabu search using a multinomial probability mass function, Eur J Opt Res. 2006; 169(3): 918e931p.

T.O. Lee, Y. Kim, Y.K. Kim. Two-sided assembly line balancing to maximize work relatedness and slackness, Comput Ind Eng. 2001; 40(3): 273e292p.

G. Levitin, J. Rubinovitz, B. Shnits. A genetic algorithm for robotic assembly line balancing, Eur J Opt Res. 2006; 168(3): 811e825p.

V. Limère, H.V. Landeghem, M. Goetschalckx, E.-H. Aghezzaf, L.F. McGinnis. Optimising part feeding in the automotive assembly industry: Deciding between kitting and line stocking, Int J Prod Res. 2011, 1–15p.

R.H. Lovgren, M.J. Racer. Algorithms for mixed-model sequencing with due date restrictions, Eur J Operat Res. 2000; 120(2): 408–22p.

F. Luo. Manufacturing execution system design and implementation, In: Second International Conference on Computer Engineering and Technology. 2010; 6: 559–62p.

D.C. Montgomery. Design and Analysis of Experiments. 5th Edn., New York: Wiley; 2000.

T. Marukawa. Chugoku Nihon no Jido¯sha Sangyo¯ Sapuraiya Shisutemu (The parts supplier networks of the Chinese and Japanese automobile industries), Soc Sci Jpn J. 2011; 14(2): 274–7p.

L. Medbo. Assembly work execution and materials kit functionality in parallel flow assembly systems, Int J Ind Ergon. 2003; 31(4): 263–81p.

Y. Monden. Toyota Production System: An Integrated Approach to Just in Time. 4th Edn., New York: CRC Press; 2011.

J.M. Nilakantan, G.Q. Huang, S.G. Ponnambalam. An investigation on minimizing cycle time and total energy consumption in robotic assembly line systems, J Clean Prod. 2015a; 90: 311e325p.

H. Noguchi. A new mixed flow production line for multiple automotive models at Tsutsumi plant, Fact Manage. 2005; 51(1): 16–33p [in Japanese].

T. Nomura. Set parts supply system in Guangzhou Toyota, Bull Kagoshima Prefect Jr Coll Cultural Soc Sci. 2008; 59: 17–29p [in Japanese].

J.M. Nilakantan, S.G. Ponnambalam, G.Q. Huang. Minimizing energy consumption in a U-shaped robotic assembly line, In: 2015 International Conference on Advanced Mechatronic Systems. Beijing, 2015b, 119e124p.

J.M. Nilakantan, S.G. Ponnambalam, N. Jawahar. Design of energy efficient RAL system using evolutionary algorithms, Eng Comput. 2016; 33(2): 580e602p.

L. €Ozbakir, P. Tapkan. Bee colony intelligence in zone constrained two-sided assembly line balancing problem, Expert Syst Appl. 2011; 38(9): 11947e11957p.

U. €Ozcan. Balancing stochastic two-sided assembly lines: a chanceconstrained, piecewise-linear, mixed integer program and a simulated annealing algorithm, Eur J Opt Res. 2010; 205(1): 81e97p.

U. €Ozcan, B. Toklu. Balancing two-sided assembly lines with sequence dependent setup times, Int J Prod Res. 2010; 48(18): 5363e5383p.

U. €Ozcan, B. Toklu. A tabu search algorithm for two-sided assembly line balancing, Int J Adv Manuf Technol. 2009a; 43(7): 822e829p.

U. €Ozcan, B. Toklu. Balancing of mixed-model two-sided assembly lines, Comput Ind Eng. 2009b; 57: 217e227p.

H.D. Purnomo, H.-M. Wee. Maximizing production rate and workload balancing in a two-sided assembly line using harmony search, Comput Ind Eng. 2014; 76: 222e230p.

H.D. Purnomo, H.-M. Wee, H. Rau. Two-sided assembly lines balancing with assignment restrictions, Math Comput Model. 2013; 57(1e2): 189e199p.

J. Rubinovitz, J. Bukchin. Design and balancing of robotic assembly lines, In: Proceedings of the Fourth World Conference on Robotics Research. Pittsburgh, PA, 1991.

J. Rubinovitz, J. Bukchin, E. Lenz. RALBda heuristic algorithm for design and balancing of robotic assembly line, CIRP Ann. 1993; 42(1): 497e500p.

A. Smalley. Toyota’s New Material-Handling System Shows TPS’s Flexibility. Lean Management Institute, 2009.

T. Tamura, H. Long, K. Ohno. A sequencing problem to level part usage rates and workloads for a mixed-model assembly line with a bypass subline, Int J Prod Econ. 1999; 60–61: 557–64p.

Q.-H. Tang, Z.-X. Li, L.-P. Zhang, C.A. Floudas, X.-J. Cao. Effective hybrid teaching-learning-based optimization algorithm for balancing two-sided assembly lines with multiple constraints. 2015.

E.-F. Wu, J. Ye, J.-S. Bao, X.-F. Hu. A branch-and-bound algorithm for twosided assembly line balancing, Int J Adv Manuf Technol. 2008; 39(9): 1009e1015p.

Y.-C. Wang, L.-F. Wang, Z.-Y. Xu, G. Yang. IE is the accelerator of economic growth mode transformation of autonomous vehicle, In: 17th International Conference Industrial Engineering and Engineering Management. 2010, 759–65p.

A. Yoosefelahi, M. Aminnayeri, H. Mosadegh, H.D. Ardakani. Type II robotic assembly line balancing problem: an evolution strategies algorithm for a multio bjective model, J Manuf Syst. 2012; 31(2): 139e151p.

B. Yuan, C.-Y. Zhang, X.-Y. Shao. A late acceptance hill-climbing algorithm for balancing two-sided assembly lines with multiple restrictions, J Intel Manuf. 2015; 26(1): 159e168p.

N. Boysen, A. Scholl, N. Wopperer. Resequencing of mixed-model assembly lines: Survey and research agenda, Eur J Operat Res. 2012; 216(3): 594–604p.


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