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Developing a fuzzy-set-based shortcut layout approach for a semiconductor inter-bay handling system


Designing optimal material flow into a semiconductor inter-bay handling’s layout boosts production efficiency, increases yield and throughput, and cuts work-in-process (WIP) as well as cycle time. Intelligent layout of production plants is vital for modern manufacturing, and particularly so for 450-mm wafer-size semiconductor plants. In light of the particular bay nature of a semiconductor plant’s inter-bay, its shortcut design that results on more efficient flow movement become challenging differed from those of a manufacturing system. Additionally, a semiconductor bay’s shortcut layout considerations must address not just how to group stockers, but also how to determine the type of shortcut points and sequence of shortcut locations. Prior to mass production, the assessment expressed by designers’ artificial language, linguistic variable, for the shortcut design phase remains ambiguous and subjective. The need for efficient ways to take designers’ linguistic variables into account in the layout decision increases. The present study suggests a method based on fuzzy set theory to design the shortcut location for a semiconductor inter-bay equipped with a multiple-zone overhead shuttle (OHS) handling system. Unlike traditional layout approaches which can only handle quantitative data with Boolean logic problems, fuzzy-set-based methods offer a way to incorporate the attitude of designers using linguistic variables to represent a problem in decision making when designing semiconductor bay’s shortcut layout. The paper also develops an intelligent hybrid heuristic algorithm incorporating with the goal of maximizing in-sequence movements and minimizing total flow distance to search a better shortcut layout design. An illustrative example from a wafer foundry company is used to demonstrate that the fuzzy-set-based layout design found by the proposed layout method outperforms by other approaches in the OHS travel time, throughput, and flow time performance. The method suggested here can assist semiconductor facility designers in qualitatively and quantitatively improving the material flow and increasing production efficiency by solving their semiconductor bay’s shortcut layout problems.

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  1. 1.

    SEMI (2019) Global semiconductor equipment sales forecast—2020 rebound, 2021 record high. Accessed 22 May 2020

  2. 2.

    Rifai AP, Dawal SZM, Zuhdi A, Aoyama H, Case K (2016) Reentrant FMS scheduling in loop layout with consideration of multi loading-unloading stations and shortcuts. Int J Adv Manuf Technol 82(9–12):1527–1545.

  3. 3.

    Satheesh Kumar RM, Asokan P, Kumanan S (2009) Artificial immune system-based algorithm for the unidirectional loop layout problem in a flexible manufacturing system. Int J Adv Manuf Technol 40(5–6):553–565.

  4. 4.

    Tompkins JA, White JA, Bozer YA, Tanchoco JMA (2010) Facilities planning. John Wiley & Sons

  5. 5.

    Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall PTR, Upper Saddle River, NJ

  6. 6.

    Tam KY (1992) A simulated annealing algorithm for allocating space to manufacturing cells. Int J Prod Res 30(1):63–87.

  7. 7.

    Bozer YA, Meller RD, Erlebacher SJ (1994) Improvement-type layout algorithm for single and multiple-floor facilities. Manag Sci 40(7):918–932.

  8. 8.

    Solimanpur M, Vrat P, Shankar R (2005) An ant algorithm for the single row layout problem in flexible manufacturing systems. Comput Oper Res 32(3):583–598.

  9. 9.

    McKendall AR Jr, Shang J, Kuppusamy S (2006) Simulated annealing heuristics for the dynamic facility layout problem. Comput Oper Res 33(8):2431–2444.

  10. 10.

    Singh SP, Sharma RRK (2006) A review of different approaches to the facility layout problems. Int J Adv Manuf Technol 30(5–6):425–433.

  11. 11.

    Bhattacharya R, Bandyopadhyay S (2010) Solving conflicting bi-objective facility location problem by NSGA II evolutionary algorithm. Int J Adv Manuf Technol 51(1–4):397–414.

  12. 12.

    Moslemipour G, Lee TS, Rilling D (2012) A review of intelligent approaches for designing dynamic and robust layouts in flexible manufacturing systems. Int J Adv Manuf Technol 60(1–4):11–27.

  13. 13.

    Langevin A, Montreuil B, Riopel D (1994) Spine layout design. Int J Prod Res 32(2):429–442.

  14. 14.

    Banerjee P, Zhou Y (1995) Facilities layout design optimization with single loop material flow path configuration. Int J Prod Res 33(1):183–203.

  15. 15.

    Kim JG, Kim YD (2000) Layout planning for facilities with fixed shapes and input and output points. Int J Prod Res 38(18):4635–4653.

  16. 16.

    Yang T, Peters BA, Tu M (2005) Layout design for flexible manufacturing systems considering single-loop directional flow patterns. Eur J Oper Res 164(2):440–455.

  17. 17.

    Chae J, Peters BA (2006) A simulated annealing algorithm based on a closed loop layout for facility layout design in flexible manufacturing systems. Int J Prod Res 44(13):2561–2572.

  18. 18.

    Bock S, Hoberg K (2006) Detailed layout planning for irregularly-shaped machines with transportation path design. Eur J Oper Res 177(2):693–718.

  19. 19.

    Drira A, Pierreval H, Hajri-Gabouj S (2007) Facility layout problems: a survey. Annu Rev Control 31(2):255–267.

  20. 20.

    Chung J, Jang J (2007) The integrated room layout for a semiconductor facility plan. IEEE Trans Semicond Manuf 20(4):517–527.

  21. 21.

    Solimanpur M, Jafari A (2008) Optimal solution for the two-dimensional facility layout problem using a branch-and-bound algorithm. Comput Ind Eng 55(3):606–619.

  22. 22.

    Bozer YA, Wang CT (2012) A graph-pair representation and MIP-model-based heuristic for the unequal-area facility layout problem. Eur J Oper Res 218(2):382–391.

  23. 23.

    Ho YC, Su TS (2012) The machine layout within a TFT-LCD bay with a multiple-stacker crane in-line stocker. Int J Prod Res 50(18):5152–5172.

  24. 24.

    Bellman RE, Zadeh LA (1970) Decision-making in a fuzzy environment. Manag Sci 17(4):b-141–b-164

  25. 25.

    Dweiri F, Meier FA (1996) Application of fuzzy decision-making in facilities layout planning. Int J Prod Res 34(11):3207–3225.

  26. 26.

    Deb SK, Bhattacharyya B (2003) Facilities layout planning based on fuzzy multiple criteria decision-making methodology. Int J Prod Res 41(18):4487–4504.

  27. 27.

    Deb SK, Bhattacharyya B (2005) Fuzzy decision support system for manufacturing facilities layout planning. Decis Support Syst 40(2):305–314.

  28. 28.

    Ertay T, Ruan D, Tuzkaya UR (2006) Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems. Inf Sci 176(3):237–262.

  29. 29.

    Yang T, Hung CC (2007) Multiple-attribute decision making methods for plant layout design problem. Robot Comput Integr Manuf 23(1):126–137.

  30. 30.

    Ertuǧrul I, Karakaşoǧlu N (2008) Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection. Int J Adv Manuf Technol 39(7–8):783–795.

  31. 31.

    Tarkesh H, Atighehchian A, Nookabadi AS (2009) Facility layout design using virtual multi-agent system. J Intell Manuf 20(4):347–357.

  32. 32.

    Azadeh A, Moghaddam M, Asadzadeh SM, Negahban A (2011) An integrated fuzzy simulation-fuzzy data envelopment analysis algorithm for job-shop layout optimization: the case of injection process with ambiguous data. Eur J Oper Res 214(3):768–779.

  33. 33.

    Yang T, Chang YC, Yang YH (2012) Fuzzy multiple attribute decision-making method for a large 300-mm fab layout design. Int J Prod Res 50(1):119–132.

  34. 34.

    Mohamadghasemi A, Hadi-Vencheh A (2012) An integrated synthetic value of fuzzy judgments and nonlinear programming methodology for ranking the facility layout patterns. Comput Ind Eng 62(1):342–348.

  35. 35.

    Hosseini Nasab H (2014) A hybrid fuzzy-GA algorithm for the integrated machine allocation problem with fuzzy demands. Appl Soft Comput J 23:417–431.

  36. 36.

    Grobelny J (2016) Fuzzy-based linguistic patterns as a tool for the flexible assessment of a priority vector obtained by pairwise comparisons. Fuzzy Sets Syst 296:1–20.

  37. 37.

    Su TS, Hwang MH (2017) Efficient machine layout design method with a fuzzy set theory within a bay in a TFT-LCD plant. Procedia Manuf 11:1863–1870.

  38. 38.

    Lee CC (1990) Fuzzy logic in control systems: fuzzy logic controller, part II. IEEE Trans Syst Man Cybern 20(2):419–435.

  39. 39.

    Braae M, Rutherford DA (1978) Fuzzy relations in a control setting. Kybernetes 7(3):185–188.

  40. 40.

    Ross TJ (2010) Fuzzy logic with engineering applications: third edition. John Wiley and Sons.

  41. 41.

    Yager RR (1980) On a general class of fuzzy connectives. Fuzzy Sets Syst 4(3):235–242.

  42. 42.

    Ho YC, Moodie CL (1998) Machine layout with a linear single-row flow path in an automated manufacturing system. J Manuf Syst 17(1):1–22.

  43. 43.

    Rardin RL (2016) Optimization in operations research, 2nd edn. Prentice Hall, Upper Saddle River

  44. 44.

    Law AM, Kelton WD (2000) Simulation modeling and analysis, vol 3. McGraw-Hill, New York

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The authors thank the anonymous referees for their valuable comments that led to the improved quality of this paper.


The authors received financial support from the Ministry of Science and Technology of Taiwan (MOST 105-2218-E-324-002).

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Correspondence to Teng-Sheng Su.

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Appendix. Simulation results

Appendix. Simulation results

Table 23 The performance indices of simulation results in the shortcut layouts found by fuzzy set-based method
Table 24 The performance indices of simulation results in the shortcut layouts found by crisp value-I method
Table 25 The performance indices of simulation results in the shortcut layouts found by crisp value-II method
Table 26 The performance indices of simulation results in the shortcut layouts found by crisp value-III method
Table 27 The performance indices of simulation results in the shortcut layouts found by greedy strategy-I method
Table 28 The performance indices of simulation results in the shortcut layouts found by greedy strategy-II method

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Su, TS., Hsiao, HH. Developing a fuzzy-set-based shortcut layout approach for a semiconductor inter-bay handling system. Int J Adv Manuf Technol 115, 889–913 (2021).

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  • Fuzzy set theory
  • Shortcut layout problem
  • Semiconductor fab
  • Inter-bay handling system