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Multiple-attribute decision-making approach for an energy-efficient facility layout design

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Abstract

Due to the trends of energy shortage and energy price rise, energy efficiency, which was always ignored over the past decades, becomes a worldwide hot issue and also a significant challenge for most factories. Therefore, it is necessary to incorporate energy-relevant criterion as a key criterion with traditional criteria in the layout planning phase. As a multiattribute decision-making (MADM) problem, the evaluation and selection of facility layout alternatives are often difficult and time consuming since the criteria generally have different units and conflicting features. In this article, a MADM approach which incorporates the advantages of rough set theory, analytic hierarchy process (AHP), and technique for order preference by similarity to ideal solution (TOPSIS) is proposed to solve the facility layout design problem with considering both traditional layout criteria and energy relevant criteria. At first, rough set theory is integrated with AHP to determine the weights for each criterion of alternatives. Then, TOPSIS is applied to get the final alternative ranking. Besides, sensitivity analysis for both decision weights and production rates is performed, and a comparison among different decision-making approaches for the same problem is also studied to demonstrate the rationality of the final decision. Finally, a practical expanding case is studied to validate the proposed approach.

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References

  1. Yang L, Deuse J, Droste M (2011) Energy efficiency at energy intensive factory—a facility planning approach. In: IEEE the 18th international conference on industrial engineering and engineering management, pp: 699-703

  2. Ertay T, Ruan D, Tuzkaya UR (2006) Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems. Inform Sci 176:237–262

    Article  Google Scholar 

  3. Yang T, Hang CC (2007) Multiple-attribute decision making methods for plant layout design problem. Robot Comput Integr Manuf 23:126–137

    Article  Google Scholar 

  4. Ye MJ, Zhou GG (2005) The application of genetic algorithm in the bi-criteria layout problem with aisles. Syst Eng Theory Pract 10:101–107, In Chinese

    Google Scholar 

  5. Aiello G, Enea M, Galante G (2006) A multi-objective approach to facility layout problem by genetic search algorithm and Electre method. Robot Comput Integr Manuf 22:447–455

    Article  Google Scholar 

  6. Ye MJ, Zhou GG (2007) A local genetic approach to multi-objective, facility layout problems with fixed aisles. Int J Prod Res 45:5243–5264

    Article  MATH  Google Scholar 

  7. Cambron KE, Evans GW (1991) Layout design using the analytic hierarchy process. Comput Ind Eng 20:211–229

    Article  Google Scholar 

  8. Yang T, Kuo CW (2003) A hierarchical AHP/DEA methodology for the facilities layout design problem. Eur J Oper Res 147:128–136

    Article  MATH  Google Scholar 

  9. Kuo Y, Yang T, Huang GW (2008) The use of grey relational analysis in solving multiple attribute decision-making problems. Comput Ind Eng 55:80–93

    Article  Google Scholar 

  10. Yang, L., Deuse, J. (2012) Multiple-attribute decision making for an energy efficient facility layout design. In: 45th CIRP conference on manufacturing systems, 16-18 May 2012, Athens

  11. Önüt S, Kara SS, Efendigil T (2008) A hybrid fuzzy MCDM approach to machine tool selection. J Intell Manuf 19:443–453

    Article  Google Scholar 

  12. Wang YM, Luo Y, Hua ZS (2008) On the extent analysis method for fuzzy AHP and its application. Eur J Oper Res 186(2008):735–747

    Article  MATH  Google Scholar 

  13. Wen, G. F., Chen, L. W (2010) Construction project bidding risk assessment model based on rough set-TOPSIS. In: 2010 WASE international conference on information engineering: 296-300

  14. Sen L, Felix TS, Chung SH (2011) A study of distribution center location based on the rough sets and interactive multi-objective fuzzy decision theory. Robot Comput Integr Manuf 27:426–433

    Article  Google Scholar 

  15. Rao RV, Davim JP (2008) A decision making framework model for material selection using a combined multiple attribute decision-making method. Int J Adv Manuf Technol 35:751–760

    Article  Google Scholar 

  16. Azadeh A, Nazari-Shirkouhi S (2011) A unique fuzzy multi-criteria decision making: computer simulation approach for productive operators’ assignment in cellular manufacturing systems with uncertainty and vagueness. Int J Adv Manuf Technol 56:329–343

    Article  Google Scholar 

  17. Dağdeviren M (2008) Decision making in equipment selection: an integrated approach with AHP and PROMETHEE. J Intell Manuf 19:397–406

    Article  Google Scholar 

  18. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

    MATH  Google Scholar 

  19. Pawlak Z (1982) Rough sets. Int J Inf Comput Sci 11:345–356

    MathSciNet  Google Scholar 

  20. Zou Z, Tseng TL, Sohn H, Song G, Gutierrez R (2011) A rough set based approach to distributor selection in supply chain management. Expert Syst Appl 38:106–115

    Article  Google Scholar 

  21. Aydogan EK (2011) Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment. Expert Syst Appl 38:3992–3998

    Article  Google Scholar 

  22. Liu S, Chan FTS, Chung SH (2011) A study of distribution center location based on the rough sets and interactive multi-objective fuzzy decision theory. Robot Comput Integr Manuf 27:426–433

    Article  Google Scholar 

  23. Wang Y, Zhen H (2008) A TOPSIS based robust optimization methodology for multivariable quality characteristics. In IEEE international conference on service operations and logistics and informatics: 2558-2561

  24. Chang CW (2010) Collaborative decision making algorithm for selection of optimal wire saw in photovoltaic wafer manufacture. J Intell Manuf. doi:10.1007/s10845-010-0391-6

  25. Wang CY, Shi JC, Niu XS, Jia SJ, Liu SJ, Mu L (2011) Sensitivity analysis on the weights in power system restoration decision-making. In: 2011 4th international conference on electric utility deregulation and restructuring and power technologies, pp 653-656

  26. Geldermann J, Treitz M, Schollenberger H, Ludwig J, Rentz O (2007) Integrated process design for the inter-company plant layout planning of dynamic mass flow networks. Universitätsverlag Karlsruhe, Kalrsruhe

    Google Scholar 

  27. Yang L, Deuse J, Jiang P (2012) Multi-objective optimization of facility planning for energy intensive companies. J Intell Manuf. doi:10.1007/s10845-012-0637-6

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Correspondence to Lei Yang.

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Yang, L., Deuse, J. & Jiang, P. Multiple-attribute decision-making approach for an energy-efficient facility layout design. Int J Adv Manuf Technol 66, 795–807 (2013). https://doi.org/10.1007/s00170-012-4367-x

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  • DOI: https://doi.org/10.1007/s00170-012-4367-x

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