International Journal of Civil Engineering

, Volume 15, Issue 2, pp 333–353 | Cite as

A Decision Making System for Construction Temporary Facilities Layout Planning in Large-Scale Construction Projects

  • Xiaoling Song
  • Jiuping XuEmail author
  • Charles Shen
  • Feniosky Peña-Mora
  • Ziqiang Zeng
Research Paper


The construction temporary facilities layout planning (CTFLP) requires an identification of necessary construction temporary facilities (CTFs), candidate locations and a layout of CTFs at candidate locations. This study proposes a decision making system to decide on an appropriate CTFLP in large-scale construction projects to improve the operation safety and efficiency. The system is composed of the input, CTF and candidate location identification, layout optimization, evaluation and selection, as well as output stages. The fuzzy logic is employed to address uncertain factors in real-world situations. In the input stage, the knowledge bases for identifying CTFs and candidate locations are determined. Then, CTFs and candidate locations are identified in the following two stages. Furthermore, a multiobjective mathematical optimization model with fuzzy parameters is established and fuzzy simulation-based Genetic Algorithm is proposed to obtain alternative CTFLPs. The intuitionistic fuzzy TOPSIS method is used to evaluate and select the most satisfactory CTFLP in the last stage. Finally, a large-scale hydropower dam project is used as a practical application to demonstrate the effectiveness and efficacy of the proposed system.


CTFLP CTFs Decision making system CTF identification Location identification Fuzzy logic 



This research was supported by the Key Program of National Natural Science Foundation of China (Grant No. 70831005), “985” Program of Sichuan University (Innovative Research Base for Economic Development and Management), the Research Foundation of Ministry of Education for the Doctoral Program of Higher Education of China (Grant No. 20130181110063), and the Program of China Scholarships Council (Grant No. 201506240179), the Youth Program of National Natural Science Foundation of China (Grant No. 71501137), the General Program of China Postdoctoral Science Foundation (Grant No. 2015M572480), the International Postdoctoral Exchange Fellowship Program of China Postdoctoral Council (Grant No. 20150028), and Sichuan University (Grant No. skqy201647).


  1. 1.
    Horvath A (2004) Construction materials and the environment. Annu Rev Environ Resour 29:181–204CrossRefGoogle Scholar
  2. 2.
    Water AT, Protection BW (2013) Construction facilities and temporary controls. The University of Massachusetts Amherst will be posting all addenda to the procurement web site 1990Google Scholar
  3. 3.
    Chau KW, Anson M (2002) A knowledge-based system for construction site level facilities layout. Developments in applied artificial intelligence. Springer, Berlin Heidelberg, pp 393–402CrossRefzbMATHGoogle Scholar
  4. 4.
    Ning X, Lam KC, Lam MCK (2011) A decision-making system for construction site layout planning. Autom Constr 20(4):459–473CrossRefGoogle Scholar
  5. 5.
    Cheng MY, O’Connor JT (1996) ArcSite: enhanced GIS for construction site layout. J Constr Eng Manag 122(4):329–336CrossRefGoogle Scholar
  6. 6.
    Elbeltagi E, Hegazy T (2001) A hybrid AL-based system for site layout planning in construction. Comput Aided Civil Infrastruct Eng 16(2):79–93CrossRefGoogle Scholar
  7. 7.
    Zhang H, Wang JY (2008) Particle swarm optimization for construction site unequal-area layout. J Constr Eng Manag 134(9):739–748CrossRefGoogle Scholar
  8. 8.
    El-Rayes K, Khalafallah A (2005) Trade-off between safety and cost in planning construction site layouts. J Constr Eng Manag 131(11):1186–1195CrossRefGoogle Scholar
  9. 9.
    Cheng MY, O’Connor JT (1994) Site layout of construction temporary facilities using an enhanced-geographic information system (GIS). Autom Constr 3(1):11–19CrossRefGoogle Scholar
  10. 10.
    Osman HM, Georgy ME, Ibrahim ME (2003) A hybrid CAD-based construction site layout planning system using genetic algorithms. Autom Constr 12(6):749–764CrossRefGoogle Scholar
  11. 11.
    Sanad HM, Ammar MA, Ibrahim ME (2008) Optimal construction site layout considering safety and environmental aspects. J Constr Eng Manag 134(7):536–544CrossRefGoogle Scholar
  12. 12.
    Samdani SA, Bhakal L, Singh AK (2006) Site layout of temporary construction facilities using ant colony optimization. In: Los Angeles Section International Committee 4th International Engineering and Construction ConferenceGoogle Scholar
  13. 13.
    Dweiri F, Meier FA (1996) Application of fuzzy decision-making in facilities layout planning. Int J Prod Res 34(11):3207–3225CrossRefzbMATHGoogle Scholar
  14. 14.
    Hegazy T, Elbeltagi E (1999) EvoSite: evolution-based model for site layout planning. J Comput Civ Eng 13(3):198–206CrossRefGoogle Scholar
  15. 15.
    Karray F, Zaneldin E, Hegazy T, Shabeeb AH, Elbeltagi E (2000) Tools of soft computing as applied to the problem of facilities layout planning. IEEE Trans Fuzzy Syst 8(4):367–379CrossRefGoogle Scholar
  16. 16.
    Ning X, Lam KC, Lam MCK (2010) Dynamic construction site layout planning using max-min ant system. Autom Constr 19(1):55–65CrossRefGoogle Scholar
  17. 17.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefzbMATHGoogle Scholar
  18. 18.
    Lin CJ, Wen UP (2004) A labeling algorithm for the fuzzy assignment problem. Fuzzy Sets Syst 142(3):373–391MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Yao JS, Lin FT (2002) Constructing a fuzzy flow-shop sequencing model based on statistical data. Int J Approx Reason 29(3):215–234MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Yao JS, Wu K (1999) Consumer surplus and producer surplus for fuzzy demand and fuzzy supply. Fuzzy Sets Syst 103(3):421–426MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Malczewski J (1999) GIS and multicriteria decision analysis. Wiley, New YorkGoogle Scholar
  22. 22.
    Valadan Zoej MJ, Mesgari MS, Beheshtifar S, Karimi M, Samadi R, Yousefi H, Sohrab T (2005) Thermal power plant site selection using GIS.
  23. 23.
    Murray AT (2010) Advances in location modeling: GIS linkages and contributions. J Geogr Syst 12(3):335–354CrossRefGoogle Scholar
  24. 24.
    Roig-Tierno N, Baviera-Puig A, Buitrago-Vera J, Mas-Verdu F (2013) The retail site location decision process using GIS and the analytical hierarchy process. Appl Geogr 40:191–198CrossRefGoogle Scholar
  25. 25.
    Robbins KL (2013) Location–allocation using GIS to improve emergency response. Doctoral dissertation, Northern Illinois UniversityGoogle Scholar
  26. 26.
    Bell N, Boscoe FP (2013) Geographic health data: fundamental techniques for analysis. CABI Press, WallingfordGoogle Scholar
  27. 27.
    Burrough PA (1996) Opportunities and limitations of GIS-based modeling of solute transport at the regional scale. Application of GIS to the modeling of non-point source pollutants in the Vadose Zone, SSSA Special Publication 48. Soil Science Society of America, Madison, pp 19–37Google Scholar
  28. 28.
    Jiang H, Eastman JR (2000) Application of fuzzy measures in multi-criteria evaluation in GIS. Int J Geogr Inf Sci 14(2):173–184CrossRefGoogle Scholar
  29. 29.
    Wang F, Hall GB (1996) Fuzzy representation of geographical boundaries in GIS. International Journal of Geographical Information Systems 10(5):573–590CrossRefGoogle Scholar
  30. 30.
    Eastman RJ (1997) IDRISI for windows: user’s guide. Clark University, Graduate School of GeographyGoogle Scholar
  31. 31.
    Bezdek JC (1993) Fuzzy models—what are they, and why? IEEE Trans Fuzzy Syst 1(1):1–6CrossRefGoogle Scholar
  32. 32.
    Dubois D, Prade H (1980) Fuzzy sets and systems. Academic Press, CambridgezbMATHGoogle Scholar
  33. 33.
    Thole U, Zimmermann HJ, Zysno P (1979) On the suitability of minimum and product operators for the intersection of fuzzy sets. Fuzzy Sets Syst 2:167–180CrossRefzbMATHGoogle Scholar
  34. 34.
    Yao L, Xu J, Guo F (2012) A stone resource assignment model under the fuzzy environment. Math Probl Eng 2012:265837. doi: 10.1155/2012/265837 MathSciNetzbMATHGoogle Scholar
  35. 35.
    Xu J, Zhou X (2011) Fuzzy-like multiple objective decision making. Springer, BerlinzbMATHGoogle Scholar
  36. 36.
    Dubois D, Prade H (1978) Operations on fuzzy numbers. Int J Syst Sci 9:613–626MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Kaufmann A (1985) Introduction to fuzzy arithmetic: theory and applications. Van Nostrand Reinhold, New YorkzbMATHGoogle Scholar
  38. 38.
    Datta D, Amaral AR, Figueira JR (2011) Single row facility layout problem using a permutation-based genetic algorithm. Eur J Oper Res 213(2):388–394MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Aiello G, La Scalia G, Enea M (2012) A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding. Expert Syst Appl 39(12):10352–10358CrossRefGoogle Scholar
  40. 40.
    García-Hernández L, Pierreval H, Salas-Morera L, Arauzo-Azofra A (2013) Handling qualitative aspects in Unequal area facility layout problem: an interactive genetic algorithm. Appl Soft Comput 13(4):1718–1727CrossRefGoogle Scholar
  41. 41.
    Sakawa M, Shibano T (1996) Interactive fuzzy programming for multiobjective 0-1 programming problems through genetic algorithms with double strings. In: Ruan D (ed) Fuzzy logic foundations and industrial applications. Kluwer Academic Publishers, Boston, pp 111–128CrossRefGoogle Scholar
  42. 42.
    Sakawa M, Kato K, Sunada H, Shibano T (1997) Fuzzy programming for multiobjective 0-1 programming problems through revised genetic algorithms. Eur J Oper Res 97(1):149–158CrossRefzbMATHGoogle Scholar
  43. 43.
    Sakawa M, Nishizaki I (2009) Cooperative and noncooperative multi-level programming [electronic resource], vol 48. Springer, BerlinGoogle Scholar
  44. 44.
    Houck CR, Joines JA, Kay MG (1995) A genetic algorithm for function optimization: a Matlab implementation. Technical Report: NCSU-IE-TR-95-09. North Carolina State University, Raleigh, NCGoogle Scholar
  45. 45.
    Sakawa IM (2000) Computational methods through genetic algorithms for obtaining Stackelberg solutions to two-level mixed zero-one programming problems. Cybern Syst 31(2):203–221CrossRefzbMATHGoogle Scholar
  46. 46.
    Li DM (2005) Multi-attribute decision making models and methods using intuitionistic fuzzy sets. J Comput Syst Sci 70(1):73–85CrossRefzbMATHGoogle Scholar
  47. 47.
    Tan CQ, Zhang Q (2006) Intuitionistic fuzzy set method for fuzzy multiple attribute decision making. Syst Math 20(5):71–76Google Scholar

Copyright information

© Iran University of Science and Technology 2016

Authors and Affiliations

  • Xiaoling Song
    • 1
    • 2
  • Jiuping Xu
    • 1
    • 3
    Email author
  • Charles Shen
    • 2
  • Feniosky Peña-Mora
    • 2
  • Ziqiang Zeng
    • 1
    • 4
  1. 1.Uncertainty Decision-Making LaboratorySichuan UniversityChengduPeople’s Republic of China
  2. 2.Advanced ConsTruction and InfOrmation techNology (ACTION) Laboratory, Civil Engineering and Engineering MechanicsColumbia UniversityNew YorkUSA
  3. 3.State Key Laboratory of Hydraulics and Mountain River EngineeringSichuan UniversityChengduPeople’s Republic of China
  4. 4.Department of Civil and Environmental EngineeringUniversity of WashingtonSeattleUSA

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