Meta Heuristic Based Simulated Aneling Approach for Design of U-shaped Manufacturing Assembly Line Balancing

Meta Heuristic Based Simulated Aneling Approach for Design of U-shaped Manufacturing Assembly Line Balancing

V. Thangadurai

Department of Mechanical Engineering, Thiagarajar College of Engineering, Madurai – 625015, Tamil Nadu, India

Corresponding Author Email: 
thangadurai.phd@rediffmail.com
Page: 
171-178
|
DOI: 
https://doi.org/10.14447/jnmes.v21i3.a07
Received: 
March 10, 2018
| |
Accepted: 
June 5, 2018
| | Citation
Abstract: 

The objective of this study is to resolve the balancing problem comprising a shared U-shaped assembly line which is exclusive-ly designed by a Simulated Annealing Algorithm. The proposed algorithm may be utilized in medium and large scale problems. This ap-proach envisages an efficient mechanism which associates a large solution space search for revealing an optimal solution. The existing balancing problem is just a conventional straight assembly line, limits its application to production line where every tasks are grouped into workstations. Line balancing is a process that balances the tasks among various workstations that is based upon precedence relation. The concept of shared principle enhances its efficiency by reducing the number of workstations. When compared with the conventional assem-bly line, the U-shaped assembly line clearly stresses on the balancing problem by allocating the tasks in forward, backward, or in both directions with respect to the precedence relation. The efficiency revealed by SAA for the shared U-shaped assembly line has proved better when compared to existing lines. The Simulated Annealing Algorithm (SAA) heuristic approach is projected to solve the medium and large sized problems by suggesting two objectives concurrently (i) To reveal the optimal number of work stations and (ii) to find the unbalance time among workstations for a fixed cycle time. The proposed approach is elaborated with a model problem and its performance is scruti-nized with a set of problems after comparing the results of SAA with model test problems available in the already published literature. The results of the experiments have revealed that the proposed SA-based algorithm outperforms with great effectiveness. The Future research scope and a comprehensive bibliography are also given.

Highlights:

· Line balancing is a process that balances the tasks among various workstations that is based upon precedence relation.

· Shared principle enhances its efficiency by reducing the number of workstations.

· U-shaped assembly line clearly stresses on the balancing problem by allocating the tasks in forward, backward, or in both directions with respect to the precedence relation.

· Simulated Annealing Algorithm (SAA) heuristic approach is projected to solve the medium and large sized problems.

· SA-based algorithm outperforms with great effectiveness.

Keywords: 

U-Shaped Assembly Line, Line Balancing, Sharing, Multi-Objective, Simulated Annealing Algorithm

1. Introduction
2. Literature Review
3. Problem Descriptions
4. Proposed Methodology
5. Result and Discussions
6. Conclusion
  References

[1] Cheng G.J. Miltenburg J and Motwani k., IEEE Transactions on Engineering Management, 47, 321 (2000).

[2] Debora A. Ajenblit, Roger L., Wainwright in Proceedings of IEEE International conference on Evolutionary Computation. Anchorage, AK: pp.96 (1998).

[3] Miltenburg G. J.and Wijngaard J., Management Science, 40, 1378 (1994).

[4] Groover M.P., Prentice Hall of India, Second Edition, 2002.

[5] Miltenburg G. J., International Journal of Flexible Manufactur-ing Systems, 14, 119 (2002).

[6] Miltenburg J., IIE Transactions, 33, 303 (2001).

[7] Kanagaraj. G and Jawahar. N., International Journals of Pro-curement Management, 2, 244 (2009).

[8] Kim. J.H., Computer and Operations Research, 36, 853 (2009).

[9] Krikpatrick. S., Science, 220, 671 (1983).

[10]Lee. T.O., Computers and Industrial Engineering, 40, 273 (2001).

[11]Miltenburg, J., International Journal of Production Economics, 70, 201 (2001).

[12]Ozcan U. and Toklu B., Computer and Operations Research, 36, 1955 (2009).

[13]Ozcan U., European Journal of Operation Research, 205, 81 (2010).

[14]Purnomo. H.D., Mathematical and Computer Modelling, 57, 189 (2013).

[15]Scholl A. and Klein R., International Journal of Production Research, 37, 721 (1999).

[16]Simaria A.S. and Vilarinho P.M., Computers and Industrial Engineering, 47, 391 (2004).

[17]Simaria A.S. and Vilarinho P.M., Computers and Industrial Engineering, 56, 489 (2009).

[18]Van Laarhoven P.J.M. and Aarts E.H.L., Dordrecht, Holland: D. Reidel Publishing Company, 1987.

[19]Yeo Keun, Production Planning and Control: The Management of Operations, 11, 44 (2000).