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Proposal for a decision support software for the design of cellular manufacturing systems with multiple routes

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Abstract

This study presents operational decision support software to efficiently solve cell formation problems with multiple routes. The software includes two revised algorithms that assist decision makers in choosing the best cellular layout. These revised algorithms are the multiobjective genetic algorithm that uses two different scalarization methods such as conic scalarization and weighted sum scalarization and the fuzzy c-means algorithm for problems with multiple routes. From these algorithms, the multiobjective genetic algorithm with conic scalarization has important advantages over many multiobjective approaches in the literature for being able to reach all efficient solutions for linear and nonlinear multiobjective models. These revised algorithms and the decision support software were tested on some data sets collected from an engine manufacturing plant and the literature. The test results showed that, in many cases, the revised multiobjective genetic algorithm with conic scalarization results in better performance than the revised fuzzy c-means algorithm and the methods used in the test problems for problems with multiobjective and multiple routes. Practitioners and researchers can use this operational decision support software and the multiobjective genetic algorithm with conic scalarization to obtain higher-quality cellular layout solutions compared with other software applications in the literature.

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References

  1. Franc PM, Gupta JND, Mendes PM, Veltink KJ (2005) Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups. Comput Ind Eng 48:491–506

    Article  Google Scholar 

  2. Aktürk MS, Balköse HO (1996) Part-machine grouping using a multi-objective cluster analysis. Int J Prod Res 34(8):2299–2315

    Article  Google Scholar 

  3. Arkat J, Farahani MH, Hosseini L (2012) Integrating cell formation with cellular layout and operations scheduling. Int J Adv Manuf Technol 61:6637–647

    Article  Google Scholar 

  4. Dimopoulos C, Zalzala AMS (2000) Recent developments in evolutionary computations for manufacturing optimization: problems, solutions, and comparisons. IEEE Trans Evol Comput 4:93–113

    Article  Google Scholar 

  5. Vitanov V, Tjahjono B, Marghalany I (2007) A decision support tool to facilitate the design of cellular manufacturing layouts. Comput Ind Eng 52:380–403

    Article  Google Scholar 

  6. Ehrgott M (2005) Multicriteria optimization. Springer, Berlin

    MATH  Google Scholar 

  7. Gasimov RN (2001) Characterization of the Benson proper efficiency and scalarization in nonconvex vector optimization. In: Koksalan M, Zionts S (eds) Multiple criteria decision making in the new millennium, Lecture notes in economics and mathematical systems. Springer, Berlin, pp 189–198

    Chapter  Google Scholar 

  8. Kasimbeyli R (2013) A conic scalarization method in multi-objective optimization. J Glob Optim 56:279–297

    Article  MATH  MathSciNet  Google Scholar 

  9. Alia OM, Mandava R, Aziz ME (2009) Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images. TENCON 2009–2009 I.E. region 10 conference, 1–6.

  10. Runkler TA, Katz C (2006) Fuzzy clustering by particle swarm optimization. IEEE Int. Conf. on Fuzzy Systems, Vancouver, pp. 601–608

  11. Taha Z, Rotsam S (2012) A hybrid fuzzy AHP-PROMETHEE decision support system for machine tool selection in flexible manufacturing cell. J Intell Manuf 23:2137–2149

    Article  Google Scholar 

  12. Chang CC (2010) Development of a web-based decision support system for cell formation problems considering alternative process routings and machine sequences. J Softw Eng Appl 3:160–166

    Article  Google Scholar 

  13. Mahadevan B, Srinivasan G (2003) Software for manufacturing cell formation: issues and experiences. GT/CM World Symposium, Columbus

  14. Luong L, He J, Abhary K, Qiu L (2002) A decision support system for cellular manufacturing system design. Computers and Industrial Engineering 42(2–4):457–470, 26th international conference on computers and industrial engineering

    Article  Google Scholar 

  15. Chow WS, Kawaleshka O (1991) Expert support system for designing cellular manufacturing. Technol Manag 440–443

  16. Nunkaew W, Phruksaphanrat B (2014b) Novel efficient relaxation reference method for part-machine clustering with assignment of duplicated machines. Paper presented at the International Conference on Information Science, Electronics and Electrical Engineering, Hokkaido, Japan, April 26–28

  17. Hyer NL, Wemmerlov U (2002) Reorganizing the factory: competing through cellular manufacturing. Productivity, Portland

    Google Scholar 

  18. Gupta T, Seifoddini H (1990) Production data based similarity coefficient for machine-component grouping decisions in the design of cellular manufacturing systems. Int J Prod Res 28:1247–1269

    Article  Google Scholar 

  19. Won Y, Lee KC (2001) Group technology cell formation considering operation sequences and production volumes. Int J Prod Res 39:2755–2768

    Article  MATH  Google Scholar 

  20. Wynn MT, Dumas M, Fidge CF, Hofstede AHM, Van der Aalst WMP (2007) Business process simulation for operational decision support. In: Castellanos M, Mendling J, Weber B (eds) Informal proceedings of the international workshop on business process intelligence (BPI 2007), pp. 55–66. QUT, Brisbane

  21. Brown EC, Sumichrast RT (2001) CF-GGA: a grouping genetic algorithm for the cell formation problem. Int J Prod Res 39(16):3651–3669

    Article  MATH  Google Scholar 

  22. Marler RT, Arora SJ (2009) The weighted sum method for multiobjective optimization: new insights. Struc Multidiscip Optim 41(6):853–862

    Article  MathSciNet  Google Scholar 

  23. Kasimbeyli R (2011) A conic scalarization method in multi-objective optimization. Journal of Global Optimization 56:279–297

    Article  MathSciNet  Google Scholar 

  24. Kasimbeyli R (2011) Computing efficient solutions of nonconvex multi-objective problems via scalarization. In: Lazard M, Bukis A, Shmaliy Y et al (eds) Recent advances in signal processing, computational geometry and systems theory. WSEAS, Florence, pp 193–198

    Google Scholar 

  25. Kasimbeyli RA (2010) Nonlinear cone separation theorem and scalarization in nonconvex vector optimization. SIAM J Optim 20:1591–619

    Article  MathSciNet  Google Scholar 

  26. Wang J (2013) An approach to determining the optimal cell number of manufacturing cell formation. Telkomnika 11(4):1781–1786

    Google Scholar 

  27. Wierzbicki AP (1980) The use of reference objectives in multiobjective optimization. In: Fandel G, Gal T (eds) Multiple criteria decision making: theory and applications, Lecture notes in economics and mathematical systems, vol. 177. Springer, Berlin, pp 468–486

    Chapter  Google Scholar 

  28. Wu X, Chu C-H, Wang Y, Yue D (2007) Genetic algorithms for integrating cell formation with machine layout and scheduling. Comput Ind Eng 53(2):277–289

  29. Wu T-H, Chung S-H, Chang C-C (2009) Hybrid simulated annealing algorithm with mutation operator to the cell formation problem with alternative process routings. Expert Syst Appl 36(2):3652–3661

  30. Sofianopoulou S (1999) Manufacturing cells design with alternative process plans and/or replicate machines. Int J Prod Res 37(3):707–720

  31. Won Y, Kim S (1997) Multiple criteria clustering algorithm for solving the group technology problem with multiple process routings. Comput Ind Eng 32(1):207–220

  32. Kusiak A (1987) The generalized group technology concept. Int J Prod Res 25:561–569

  33. Sankaran S, Kasilingam, RG (1990) An integrated approach to cell formation and part routing in group technology manufacturing systems. Eng Optim 16:235–245

  34. Logendran R, Ramakrishna P, Sriskandarajah C (1994) Tabu search-based heuristics for cellular manufacturing systems in the presence of alternative process plans. Int J Prod Res 32:273–297

    Article  MATH  Google Scholar 

  35. Gasimov RN, Sipahioglu A, Sarac T (2007) A multi-objective programming approach to 1.5-dimensional assortment problem. Eur J Oper Res 179:64–79

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Dumlupınar University, Kütahya, Turkey

    İhsan Erozan & Ozden Ustun

  2. Sakarya Üniversity, Sakarya, Turkey

    Orhan Torkul

Authors
  1. İhsan Erozan
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  2. Orhan Torkul
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  3. Ozden Ustun
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Corresponding author

Correspondence to İhsan Erozan.

Appendix

Appendix

Table 5 Use of software based on the problem
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Erozan, İ., Torkul, O. & Ustun, O. Proposal for a decision support software for the design of cellular manufacturing systems with multiple routes. Int J Adv Manuf Technol 76, 2027–2041 (2015). https://doi.org/10.1007/s00170-014-6397-z

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