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Precision Product-Process Design and Optimization pp 411–434Cite as

Optimum Machines Allocation in a Serial Production Line Using NSGA-II and TOPSIS

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Part of the book series: Lecture Notes on Multidisciplinary Industrial Engineering ((LNMUINEN))

Abstract

In the present scenario, manufacturing industries are witnessing large fluctuation in the product demands. The need of the hour is to have a responsive manufacturing system, which can cope up with these kinds of stochastic events. Reconfigurable Manufacturing System (RMS) is considered as modern manufacturing paradigm that offers customised functionality and capacity as and when required. The key enablers for this customisation in functionality and capacity are the Reconfigurable Machine Tools (RMTs) or simply Reconfigurable Machines (RMs). Selecting these multi-functionality and capacity machines along stations/stages of a product flow line is an important aspect for operating RMSs as it has direct implications on the operational performance of such system. In this paper, optimal configurations are selected under conflicting objectives based on cost, operation capability and reliability of the machines. The objective is to assign those machine configurations that minimises the cost while maximising the operation and reliability of the production line. The problem is framed as a multi-objective optimisation problem and is solved using NSGA-II. The results so obtained are discussed in terms of the performance of the reconfigurable system.

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References

  • Abderrahmane, B., M. Dahane, and L. Benyoucef. 2012. A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment. Computer and Industrial Engineering 66: 519–524.

    Google Scholar 

  • Al-Zaher, A., and W.H. ElMaraghy. 2013. CIRP annals—manufacturing technology design of reconfigurable automotive framing system. CIRP Annals-Manufacturing Technology 62: 491–494.

    Article  Google Scholar 

  • Ashraf, M., M.A. Khan, F. Hasan, M.I. Siddiqui, Q. Murtaza, and S.N. Ali. 2016. Optimum allocation of machines with multiple objectives using NSGA-II. In: 6th international & 27th all india manufacturing technology, design and research conference (AIMTDR-2016), 1723–1728. Pune.

    Google Scholar 

  • Ashraf, M., and F. Hasan. 2015. Product family formation based on multiple product similarities for a reconfigurable manufacturing system. International Journal of Modelling in Operations Management 5: 247–265.

    Article  Google Scholar 

  • Bensmaine, A., M. Dahane, and L. Benyoucef. 2014. Design of reconfigurable manufacturing systems: Optimal machines selection using nondominated sorting genetic algorithm (NSGA-II). In: 41st international conference on computers & industrial engineering, 2014, 110–115.

    Google Scholar 

  • Coello, C.A.C., G.B. Lamont, D.A., Van Veldhuizen. 2007. Evolutionary algorithms for solving multi-objective problems. Springer.

    Google Scholar 

  • Dashchenko, A.I. 2006. Reconfigurable manufacturing systems and transformable factories. Moscow: Springer.

    Book  Google Scholar 

  • Deb, K., S. Agrawal, A. Pratap, T. Meyarivan. 2000. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Kanpur.

    Google Scholar 

  • Deb, K. 2011. Multi-objective optimization using evolutionary algorithms: An introduction. In Multi-objective evolutionary optimisation for product design and manufacturing, ed. L. Wang, A. Ng, and K. Deb, 1–24. Kanpur: Springer.

    Google Scholar 

  • Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6: 182–197.

    Article  Google Scholar 

  • Dou, J., X. Dai, and Z. Meng. 2009. Graph theory-based approach to optimize single-product flow-line configurations of RMS. International Journal of Advanced Manufacturing Technology 41: 916–931.

    Article  Google Scholar 

  • El Maraghy, H.A. 2006. Flexible and reconfigurable manufacturing systems paradigms. International Journal of Flexible Manufacturing Systems 17: 261–276.

    Google Scholar 

  • Elmaraghy, H.A. 2011. Enabling manufacturing competitiveness and economic sustainability. In 4th international conference on changeable, agile, reconfigurable and virtual production, ed. H.A. Elmaraghy, 2–9. Montreal: Springer.

    Google Scholar 

  • Fonseca, C.M., and P.J. Fleming. 1995. An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3: 1–16.

    Article  Google Scholar 

  • Galan, R., J. Racero, I. Eguia, and J.M. Garcia. 2007. A systematic approach for product families formation in reconfigurable manufacturing systems. Robotics and Computer-Integrated Manufacturing 23: 489–502.

    Article  Google Scholar 

  • Gola, A., and A. Swic. 2012. Directions of manufacturing systems evolution from the flexibility level point of view. In: Innovations in management and production engineering, 226–238. Oficyna Wydawnicza, Opole.

    Google Scholar 

  • Goyal, K.K., P.K. Jain, and M. Jain. 2012. Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS. International Journal of Production Research 50: 4175–4191.

    Article  Google Scholar 

  • Haddou-Benderbal, H., M. Dahane, and L. Benyoucef. 2016. Hybrid Heuristic to minimize machine’s unavailability impact on reconfigurable manufacturing system using reconfigurable process plan. In 8th IFAC conference on manufacturing Modelling, management and control MIM 2016—Troyes, France, 28–30 June 2016. 1626–1631. Elsevier B.V.

    Article  Google Scholar 

  • Hasan, F., P.K. Jain, and D. Kumar. 2014a. Performance issues in reconfigurable manufacturing system. In: DAAAM international scientific book, 295–310.

    Google Scholar 

  • Hasan, F., and P.K. Jain. 2015. Genetic Modelling for selecting optimal machine configurations in reconfigurable manufacturing system. Applied Mechanics and Materials 789–790: 1229–1239.

    Article  Google Scholar 

  • Hasan, F., S. Jain, and S.N. Ali. 2014b. Configuration selection for optimal throughput from a reconfigurable product line using genetic algorithm. Som 2014: 669–674.

    Google Scholar 

  • Hasan, F., P.K. Jain, and D. Kumar. 2014c. Optimum configuration selection in reconfigurable manufacturing system involving multiple part families. Opsearch. 51: 297–311.

    Article  Google Scholar 

  • Hwang, C.-L., and K. Yoon. 2011. Multiple attribute decision making. London: CRC Press.

    Google Scholar 

  • Koren, Y. 1983. Computer control of manufacturing systems. New york: McGraw-Hill Inc.

    Google Scholar 

  • Koren, Y., and A.G. Ulsoy. 1997. Reconfigurable manufacturing systems. Ann Arbor (1997).

    Google Scholar 

  • Koren, Y., and S. Kota. 1997. Reconfigurable machine tool. https://www.google.com/patents/US5943750.

  • Koren, Y. 1999. Reconfigurable machining systems vision with examples. Ann Arbor (1999).

    Google Scholar 

  • Koren, Y. 2006. General RMS characteristics. Comparison with dedicated and flexible systems. In Reconfigurable manufacturing systems and transformable factories, ed. Dashchenko, A.I., 27–45. Berlin, Heidelberg: Springer.

    Google Scholar 

  • Koren, Y. 2013. The rapid responsiveness of RMS. International Journal of Production Research 51: 6817–6827.

    Article  Google Scholar 

  • Koren, Y., and M. Shpitalni. 2010. Design of reconfigurable manufacturing systems. Journal of Manufacturing Systems 29: 130–141.

    Article  Google Scholar 

  • Landers, R.G., B.K. Min, and Y. Koren. 2001. Reconfigurable machine tools. CIRP Annals-Manufacturing Technology 50: 269–274.

    Google Scholar 

  • Maniraj, M., V. Pakkirisamy, and R. Jeyapaul. 2015. An ant colony optimization-based approach for a single-product flow-line reconfigurable manufacturing systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 1–8.

    Google Scholar 

  • Matt, D.T., and E. Rauch. 2013. Design of a network of scalable modular manufacturing systems to support geographically distributed production of mass customized goods. Procedia CIRP 12: 438–443.

    Article  Google Scholar 

  • Mehrabi, M.G., A.G. Ulsoy, and Y. Koren. 2000. Reconfigurable manufacturing systems: Key to future manufacturing. Journal of Intelligent Manufacturing 11: 403–419.

    Article  Google Scholar 

  • Mitrofanov, S.P. 1966. Scientific Principles of Group Technology.

    Google Scholar 

  • Moon, Y.-M., and S. Kota. 2002. Design of reconfigurable machine tools. Journal of Manufacturing Science and Engineering 124: 480–483.

    Article  Google Scholar 

  • Opitz, H., W. Eversheim, and H.P. Wiendahl. 1969. Workpiece classification and its industrial application. International Journal of Machine Tool Design and Research 9: 39–50.

    Article  Google Scholar 

  • Pattanaik, L.N., P.K. Jain, and N.K. Mehta. 2007. Cell formation in the presence of reconfigurable machines. The International Journal of Advanced Manufacturing Technology 34: 335–345.

    Article  Google Scholar 

  • Reisman, A., A. Kumar, J. Motwani, and C.H. Cheng. 1997. Cellular manufacturing: A statistical review of the literature (1965–1995). Operations Research 45: 508–520.

    Article  Google Scholar 

  • Shannon, C.E. 1948. A mathematical theory of communication. Bell System Technical Journal 27: 379–423.

    Article  MathSciNet  Google Scholar 

  • Stecke, K.E., and J.J. Solberg. 1981. Loading and control policies for a flexible manufacturing system. The International Journal of Production Research 19: 481–490.

    Article  Google Scholar 

  • Xiaobo, Z., J. Wang, and Z. Luo. 2000. A stochastic model of a reconfigurable manufacturing system Part 2: Optimal configurations. International Journal of Production Research 38: 2829–2842.

    Article  Google Scholar 

  • Yin, Y., and K. Yasuda. 2006. Similarity coefficient methods applied to the cell formation problem: A taxonomy and review. International Journal of Production Economics 101: 329–352.

    Article  Google Scholar 

  • Youssef, A.M.A. 2006. Optimal configuration selection for reconfigurable manufacturing systems. http://link.springer.com/10.1007/s10696-007-9020-x%5Cn. https://www.infona.pl//resource/bwmeta1.element.springer-92540f5e-f764-3369-b248-7dac20aab687

  • Youssef, A.M.A., and H.A. ElMaraghy. 2007. Optimal configuration selection for reconfigurable manufacturing systems. International Journal of Flexible Manufacturing Systems 19: 67–106.

    Article  Google Scholar 

  • Zeleny, M., and J.L. Cochrane. 1973. Multiple criteria decision making. University of South Carolina Press.

    Google Scholar 

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Acknowledgements

The contribution from the Council of scientific and Industrial research (CSIR), Human resource development group (HRDG), India under file no. 09/112 (0552)2K17 EMP-I in support of this research is greatly acknowledged.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Z.H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, 202002, Uttar Pradesh, India

    Masood Ashraf, Faisal Hasan & Qasim Murtaza

Authors
  1. Masood Ashraf
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  2. Faisal Hasan
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  3. Qasim Murtaza
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Correspondence to Masood Ashraf .

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Editors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Bombay , Mumbai, Maharashtra, India

    Sanjay S. Pande

  2. Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Uday S. Dixit

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Ashraf, M., Hasan, F., Murtaza, Q. (2018). Optimum Machines Allocation in a Serial Production Line Using NSGA-II and TOPSIS. In: Pande, S., Dixit, U. (eds) Precision Product-Process Design and Optimization. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-8767-7_16

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  • DOI: https://doi.org/10.1007/978-981-10-8767-7_16

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  • Online ISBN: 978-981-10-8767-7

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