Advertisement

Annals of Operations Research

, Volume 143, Issue 1, pp 157–170 | Cite as

Multi-objectives for incremental cell formation problem

  • O. MaheshEmail author
  • G. Srinivasan
Article

Abstract

Over the past three decades considerable amount of research work has been reported in the literature of Group Technology (GT). Most of the research work is concerned with formation of machine cells and part families. This is because cell formation is considered to be the most complex and the most important aspect of Cellular Manufacturing System (CMS). Due to NP completeness of cell formation problem, many heuristics have been developed. These heuristics are developed for both single as well as multiple objectives for the comprehensive cell formation. Here all part types and machine types are considered at a time for cell conversion and that all cells are designed at a single point in time. But planning and implementation of most cell conversions in industry are incremental ones, and not comprehensive. This issue has not been addressed in GT literature adequately. In this paper we consider multiple objectives for incremental cell formation and develop, a lexicographic based simulated annealing algorithm. The performance of the algorithm is tested over several data sets by taking different initial feasible solutions generated using different heuristics.

Keywords

Multi-objectives Group technology Cellular manufacturing system Incremental cell 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. BAYKASOGLU, A. and N.N.Z. GINDY. (2000). MOCACEF 1.0: Multiple objective capability based approach to form part-machine groups for cellular manufacturing applications. International Journal of Production Research, 38, 1133–1161.CrossRefGoogle Scholar
  2. CHANDRASEKHARAN, M.P. and R. RAJAGOPALAN. (1986). MODROC: an extension of rank order clustering for group technology. International Journal of Production Research 24, 1221–1233.Google Scholar
  3. CHANDRASEKHARAN, M.P. and R. RAJAGOPALAN. (1987). ZODIAC-an algorithm for concurrent formation of part families and machine cells. International Journal of Production Research 25, 835–850.Google Scholar
  4. CHOOBINEH, F. (1988). A framework for the design of cellular manufacturing systems. International Journal of Production Research 26, 1161–1172.Google Scholar
  5. GUPTA, T. and H. SEIFODDINI. (1990). Production data based similarity coefficient for machine component grouping decisions in the design of cellular manufacturing systems. International Journal of Production Research 28, 1247–1269.Google Scholar
  6. GUPTA, Y., GUPTA, M., KUMAR, A. and C. SUNDARAM. (1996). A genetic algorithm-based approach to cell composition and layout design problems.International Journal of Production Research 34, 447–482.Google Scholar
  7. HARHALAKIS, G., NAGI, R., and J.M. PROTH. (1990). An efficient heuristic in manufacturing cell formation for group technology applications. International Journal of Production Research 28, 185–198.Google Scholar
  8. J.R. KING. (1980). Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm formation in group technology. International Journal of Production Research 18, 213–232.Google Scholar
  9. KIRKPATRICK, S., GELATT, C.D. Jr. and M.P. VECCHI. (1983). Optimization by simulated annealing, Science 220, 671–680.Google Scholar
  10. LEEPOST, A. (2000). Part family identification using a simple genetic algorithm. International Journal of Production Research 38, 793–810.CrossRefGoogle Scholar
  11. LOGENDRAN, R., P. RAMAKRISHNA, and C. SRIKANDARAJAH. (1994). Tabu search based heuristics for cellular manufacturing system in the presence of alternative process plans, International Journal of Production Research 32, 273–297.Google Scholar
  12. MAHESH, O. and G. SRINIVASN. (2002a). Incremental cell formation considering alternative machines.International Journal of Production Research 40, 3291–3310.CrossRefGoogle Scholar
  13. MAHESH, O. and G. SRINIVASN. (2002b). Algorithms for incremental cell formation problem. Proceedings of Symposium on manufacturing excellence (SYMAX) IIT Madras,183–184Google Scholar
  14. MARSH, R.F., SHAFER, S.M. and J.R. MEREDITH. (1999). A comparison of cellular manufacturing research presumptions with practice. International Journal of Production Research 37, 3119–3138.CrossRefGoogle Scholar
  15. MCAULEY, J. (1972). Machine grouping for efficient production. Production Engineer 51, 53–57.CrossRefGoogle Scholar
  16. METROPOLIS, N., ROSENBLUTH, A., ROSENBLUTH, M., TELLER, A. and E., TELLER. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics 21, 1087–1092.CrossRefGoogle Scholar
  17. NAIR, G., JAYAKRISHNAN and T.T. NARENDRAN. (1998). CASE: A clustering algorithm for cell formation with sequence data. International Journal of Production Research 36, 157–179.CrossRefGoogle Scholar
  18. NAIR, G., JAYAKRISHNAN and T.T. NARENDRAN. (1999). ACCORD: A bi-criterion algorithm for cell formation using ordinal and ratio-level data. International Journal of Production Research 37, 539–556.CrossRefGoogle Scholar
  19. SOLEJA, V.B. and S.M. UROSEVIC. (1969). Optimization of Group Technology lines by methods developed in IAMA, Belgrade. Proceedings of the International Conference on GT, ILO, Turin. Google Scholar
  20. S. SOFIANOPOLOU. (1999). Manufacturing cells design with alternative process plans and/or replicate machines. International Journal of Production Research 37,707–720.CrossRefGoogle Scholar
  21. SRINIVASAN, G., NARENDRAN, T.T. and B. MAHADEVAN. (1990). An assignment model for the part-families problem in group technology. International Journal of Production Research 28, 145–152.Google Scholar
  22. Su, C.T. and Hsu, C.M. (1998). Multi-objective machine-part cell formation through parallel simulated annealing. International Journal of Production Research 36, 2185–2207.CrossRefGoogle Scholar
  23. VENUGOPAL, V. and T. T. NARENDRAN. (1992a). Cell formation in manufacturing systems through simulated annealing: an experimental evaluation. European Journal of Operational Research 63, 409–422.CrossRefGoogle Scholar
  24. VENUGOPAL, V. and T. T. NARENDRAN. (1992b). A genetic algorithm approach to the machine component grouping problem with multiple objectives. Computers and Industrial Engineering 22, 469–480.CrossRefGoogle Scholar
  25. WEMMERLOV, U. and N.L. HYER. (1989). Cellular manufacturing in the US: a survey of users. International Journal of Production Research 27,1511–1530.Google Scholar
  26. WEMMERLOV, U. and D. J. JOHNSON. (2000). Empirical findings on manufacturing cell design. International Journal of Production Research 38, 481–507.CrossRefGoogle Scholar
  27. ZHAO, C. and Z. Wu. (2000). A genetic algorithm for manufacturing cell formation with multiple routes and multiple objectives. International Journal of Production Research 38, 385–395.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Priyadarshini College of EngineeringNelloreIndia
  2. 2.Department of Management StudiesIndian Institute of Technology MadrasChennaiIndia

Personalised recommendations