Multiple Objective Social Group Optimization for Time–Cost–Quality–Carbon Dioxide in Generalized Construction Projects


This study presents a novel approach named as “multiple objective social group optimization” (MOSGO) to tradeoff time, cost, quality, and carbon dioxide emission (TCQC) factors in generalized construction projects. The proposed algorithm modifies the operation mechanism to balance the exploration and exploitation abilities of the optimization process. The TCQC tradeoff problem considers all types of logical relationships between project activities. Two practical case studies demonstrate the ability of MOSGO-generated, non-dominated solutions. In addition, evidential reasoning is applied to select a compromise solution for project implementation. Comparisons between the MOSGO and four well-known algorithms (MODE, MOABC, MOPSO, and NSGA-II) to verify the efficiency and effectiveness of the developed algorithm. According to the statistical analysis, the proposed algorithm generated the highest values of diversification measurement (DM) of 26.113 and 40.27; the highest values of hyper-volume (HV) of 0.875 and 0.881 in case 1 and case 2, respectively. The proposed algorithm also found solutions with lowest mean ideal distance (MID) and spread (SP) values of 0.872 and 0.462 in the first case and of 0.754 and 0.689 in the second case. MOSGO showed had better diversify and convergence, gained wider spread, and yielded higher uniformity of solutions than the compared multiple objective evolutionary algorithms.

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  1. 1.

    Ghoddousi P, Eshtehardian E, Jooybanpour S, Javanmardi A (2013) Multi-mode resource-constrained discrete time–cost-resource optimization in project scheduling using non-dominated sorting genetic algorithm. Automation Construction 30:216–227.

    Article  Google Scholar 

  2. 2.

    Alavipour SMR, Arditi D (2019) Time-cost tradeoff analysis with minimized project financing cost. Automation Construction 98:110–121.

    Article  Google Scholar 

  3. 3.

    Cheng M-Y, Tran D-H, Cao M-T (2016) Chaotic initialized multiple objective differential evolution with adaptive mutation strategy (CA-MODE) for construction project time–cost–quality trade-off. J Civ Eng Manag 22(2):210–223.

    Article  Google Scholar 

  4. 4.

    Tran D-H, Cheng M-Y (2014) Two-phase differential evolution for the multiobjective optimization of time-cost tradeoffs in resource-constrained construction projects. IEEE Trans Eng Manage 61(3):450–461.

    Article  Google Scholar 

  5. 5.

    Monghasemi S, Nikoo MR, Khaksar Fasaee MA, Adamowski J (2015) A novel multi criteria decision making model for optimizing time–cost–quality trade-off problems in construction projects. Expert Syst Appl 42(6):3089–3104.

    Article  Google Scholar 

  6. 6.

    Babu AJG, Suresh N (1996) Project management with time, cost, and quality considerations. Eur J Oper Res 88(2):320–327.

    Article  MATH  Google Scholar 

  7. 7.

    El-Rayes K, Kandil A (2005) Time–Cost–Quality trade-off analysis for highway construction. J Construction Eng Manag 131(4):477–486.

    Article  Google Scholar 

  8. 8.

    Zhang H, Xing F (2010) Fuzzy-multi-objective particle swarm optimization for time–cost–quality tradeoff in construction. Automation Construction 19(8):1067–1075.

    Article  Google Scholar 

  9. 9.

    Zhang L, Du J, Zhang S (2014) Solution to the Time–Cost–Quality trade-off problem in construction projects based on immune genetic particle swarm optimization. J Manag Eng 30(2):163–172.

    Article  Google Scholar 

  10. 10.

    Mungle S, Benyoucef L, Son Y-J, Tiwari MK (2013) A fuzzy clustering-based genetic algorithm approach for time–cost–quality trade-off problems: a case study of highway construction project. Eng Appl Artif Intell 26(8):1953–1966.

    Article  Google Scholar 

  11. 11.

    Farahnaz R-P, Kaveh K-D (2017) A new stochastic time-cost-quality trade-off project scheduling problem considering multiple-execution modes, preemption, and generalized precedence relations. Ind Eng Manag Syst 16(3):271–287

    Google Scholar 

  12. 12.

    González MJ, García Navarro J (2006) Assessment of the decrease of CO2 emissions in the construction field through the selection of materials: practical case study of three houses of low environmental impact. Build Environ 41(7):902–909.

    Article  Google Scholar 

  13. 13.

    Yan H, Shen Q, Fan LCH, Wang Y, Zhang L (2010) Greenhouse gas emissions in building construction: a case study of one peking in Hong Kong. Build Environ 45(4):949–955.

    Article  Google Scholar 

  14. 14.

    Liu S, Tao R, Tam CM (2013) Optimizing cost and CO2 emission for construction projects using particle swarm optimization. Habitat Int 37:155–162.

    Article  Google Scholar 

  15. 15.

    Farazmand N, Beheshtinia M (2018) Multi-objective optimization of time-cost-quality-carbon dioxide emission-plan robustness in construction projects. J Ind Syst Eng 11 (3):102–125.

  16. 16.

    Azevedo ARG, Vieira CMF, Ferreira WM, Faria KCP, Pedroti LG, Mendes BC (2020) Potential use of ceramic waste as precursor in the geopolymerization reaction for the production of ceramic roof tiles. J Building Eng 29:101156.

    Article  Google Scholar 

  17. 17.

    de Azevedo ARG, Alexandre J, Marvila MT, Xavier GdC, Monteiro SN, Pedroti LG (2020) Technological and environmental comparative of the processing of primary sludge waste from paper industry for mortar. J Clea Prod 249:119336.

    Article  Google Scholar 

  18. 18.

    Azevedo ARG, Cecchin D, Carmo DF, Silva FC, Campos CMO, Shtrucka TG, Marvila MT, Monteiro SN (2020) Analysis of the compactness and properties of the hardened state of mortars with recycling of construction and demolition waste (CDW). J Materials Res Technol 9(3):5942–5952.

    Article  Google Scholar 

  19. 19.

    de Azevedo ARG, Marvila MT, Tayeh BA, Cecchin D, Pereira AC, Monteiro SN (2021) Technological performance of açaí natural fibre reinforced cement-based mortars. J Build Eng 33:101675.

    Article  Google Scholar 

  20. 20.

    Khalili-Damghani K, Tavana M, Abtahi A-R, Santos Arteaga FJ (2015) Solving multi-mode time–cost–quality trade-off problems under generalized precedence relations. Opt Methods Softw 30(5):965–1001.

    MathSciNet  Article  MATH  Google Scholar 

  21. 21.

    Sakellaropoulos S, Chassiakos AP (2004) Project time–cost analysis under generalised precedence relations. Adv Eng Softw 35(10):715–724.

    Article  MATH  Google Scholar 

  22. 22.

    Satapathy S, Naik A (2016) Social group optimization (SGO): a new population evolutionary optimization technique. Complex Intell Syst 2(3):173–203.

    Article  Google Scholar 

  23. 23.

    Naik A, Satapathy SC, Ashour AS, Dey N (2018) Social group optimization for global optimization of multimodal functions and data clustering problems. Neural Comput Appl 30(1):271–287.

    Article  Google Scholar 

  24. 24.

    Nagireddy V, Parwekar P, Mishra TK Comparative analysis of PSO-SGO algorithms for localization in wireless sensor networks. In, Singapore, 2019. information systems design and intelligent applications. Springer Singapore, pp 401–409. doi: 10.1007/978-981-13-3329-3_37

  25. 25.

    Rajinikanth V, Satapathy SC (2018) Segmentation of ischemic stroke lesion in brain MRI based on social group optimization and fuzzy-tsallis entropy. Arab J Sci Eng 43(8):4365–4378.

    Article  Google Scholar 

  26. 26.

    James E. Kelley J, Walker MR (1959) Critical-path planning and scheduling. Paper presented at the Eastern joint IRE-AIEE-ACM computer conference, Boston, Massachusetts, pp 160–173. doi:

  27. 27.

    Kelley JE (1961) Critical-path planning and scheduling: mathematical basis. Oper Res 9(3):296–320.

    MathSciNet  Article  MATH  Google Scholar 

  28. 28.

    Robinson DR (1975) A dynamic programming solution to cost-time tradeoff for CPM. Manage Sci 22(2):158–166.

    Article  MATH  Google Scholar 

  29. 29.

    Deckro RF, Hebert JE, Verdini WA, Grimsrud PH, Venkateshwar S (1995) Nonlinear time/cost tradeoff models in project management. Comput Ind Eng 28(2):219–229.

    Article  Google Scholar 

  30. 30.

    Khang DB, Myint YM (1999) Time, cost and quality trade-off in project management: a case study. Int J Project Manage 17(4):249–256.

    Article  Google Scholar 

  31. 31.

    Tareghian HR, Taheri SH (2006) On the discrete time, cost and quality trade-off problem. Appl Math Comput 181(2):1305–1312.

    Article  MATH  Google Scholar 

  32. 32.

    Kim J, Kang C, Hwang I (2012) A practical approach to project scheduling: considering the potential quality loss cost in the time–cost tradeoff problem. Int J Project Manage 30(2):264–272.

    Article  Google Scholar 

  33. 33.

    Agdas D, Warne DJ, Osio-Norgaard J, Masters FJ (2018) Utility of genetic algorithms for solving large-scale construction time-cost trade-off problems. J Comput Civ Eng 32(1):04017072.

    Article  Google Scholar 

  34. 34.

    Prager W (1963) A structural method of computing project cost polygons. Manage Sci 9(3):394–404.

    Article  Google Scholar 

  35. 35.

    Siemens N (1971) A simple CPM time-cost tradeoff algorithm. Manag Sci 17 (6):B-354-B-363. doi: 10.1287/mnsc.17.6.B354

  36. 36.

    Sonmez R, Iranagh MA, Uysal F (2016) Critical sequence crashing heuristic for resource-constrained discrete time-cost trade-off problem. J Construction Eng Manag 142(3):04015090.

    Article  Google Scholar 

  37. 37.

    Sonmez R, Aminbakhsh S, Atan T (2020) Activity uncrashing heuristic with noncritical activity rescheduling method for the discrete time-cost trade-off problem. J Construction Eng Manag 146(8):04020084.

    Article  Google Scholar 

  38. 38.

    Tareghian HR, Taheri SH (2007) A solution procedure for the discrete time, cost and quality tradeoff problem using electromagnetic scatter search. Appl Math Comput 190(2):1136–1145.

    MathSciNet  Article  MATH  Google Scholar 

  39. 39.

    Menesi W, Golzarpoor B, Hegazy T (2013) Fast and near-optimum schedule optimization for large-scale projects. J Construction Eng Manag 139(9):1117–1124.

    Article  Google Scholar 

  40. 40.

    Liu D, Li H, Wang H, Qi C, Rose T (2020) Discrete symbiotic organisms search method for solving large-scale time-cost trade-off problem in construction scheduling. Expert Syst Appl 148:113230.

    Article  Google Scholar 

  41. 41.

    Albayrak G (2020) Novel hybrid method in time-cost trade-off for resource-constrained construction projects. Iran J Sci Technol Trans Civ Eng.

    Article  Google Scholar 

  42. 42.

    Kalhor E, Khanzadi M, Eshtehardian E, Afshar A (2011) Stochastic time–cost optimization using non-dominated archiving ant colony approach. Automation Construction 20(8):1193–1203.

    Article  Google Scholar 

  43. 43.

    Afshar A, Ziaraty AK, Kaveh A, Sharifi F (2009) Nondominated archiving multicolony ant algorithm in time-cost trade-off optimization. J Construction Eng Manag 135(7):668–674.

    Article  Google Scholar 

  44. 44.

    Toğan V, Eirgash MA (2019) Time-cost trade-off optimization of construction projects using teaching learning based optimization. KSCE J Civ Eng 23(1):10–20.

    Article  Google Scholar 

  45. 45.

    Tavana M, Abtahi A-R, Khalili-Damghani K (2014) A new multi-objective multi-mode model for solving preemptive time–cost–quality trade-off project scheduling problems. Expert Systems with Applications 41 (4, Part 2):1830–1846. Doi: 10.1016/j.eswa.2013.08.081

  46. 46.

    Wood DA (2017) Gas and oil project time-cost-quality tradeoff: integrated stochastic and fuzzy multi-objective optimization applying a memetic, nondominated, sorting algorithm. J Nat Gas Sci Eng 45:143–164.

    Article  Google Scholar 

  47. 47.

    Banihashemi SA, Khalilzadeh M, Shahraki A, Malkhalifeh MR, Ahmadizadeh SSR (2020) Optimization of environmental impacts of construction projects: a time–cost–quality trade-off approach. Int J Environ Sci Technol.

    Article  Google Scholar 

  48. 48.

    Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley.

  49. 49.

    Zhou A, Qu B-Y, Li H, Zhao S-Z, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol Comput 1(1):32–49.

    Article  Google Scholar 

  50. 50.

    Laokhongthavorn L, U-tapao C (2017) A multi-objective optimization model for solid waste disposal under uncertainty: a case study of Bangkok. Thailand Int J Civ Eng 15(2):205–212.

    Article  Google Scholar 

  51. 51.

    Son J, Hong T, Lee S (2013) A mixed (continuous + discrete) time-cost trade-off model considering four different relationships with lag time. KSCE J Civ Eng 17(2):281–291.

    Article  Google Scholar 

  52. 52.

    Su Z-X, Wei H-Y, Zou X, Qi J-X (2019) Zero-one formulation for a partial resource-constrained project scheduling problem with generalized precedence relations. J Construction Eng Manag 145(3):04018142.

    Article  Google Scholar 

  53. 53.

    Tran D-H, Cheng M-Y, Cao M-T (2015) Hybrid multiple objective artificial bee colony with differential evolution for the time–cost–quality tradeoff problem. Knowl-Based Syst 74:176–186.

    Article  Google Scholar 

  54. 54.

    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197.

    Article  Google Scholar 

  55. 55.

    Wang Y-N, Wu L-H, Yuan X-F (2010) Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure. Soft Comput 14(3):193–209.

    Article  Google Scholar 

  56. 56.

    Ali M, Siarry P, Pant M (2012) An efficient differential evolution based algorithm for solving multi-objective optimization problems. Eur J Oper Res 217(2):404–416.

    MathSciNet  Article  MATH  Google Scholar 

  57. 57.

    Li Y, Huang W, Wu R, Guo K (2020) An improved artificial bee colony algorithm for solving multi-objective low-carbon flexible job shop scheduling problem. Appl Soft Comput 95:106544.

    Article  Google Scholar 

  58. 58.

    Aminbakhsh S, Sonmez R (2016) Discrete particle swarm optimization method for the large-scale discrete time–cost trade-off problem. Expert Syst Appl 51:177–185.

    Article  Google Scholar 

  59. 59.

    Zitzler E, Thiele L, Laumanns M, Fonseca CM, Fonseca VGd (2003) Performance assessment of multiobjective optimizers: an analysis and review. Trans Evol Comp 7(2):117–132.

    Article  Google Scholar 

  60. 60.

    Senouci AB, Mubarak SA (2016) Multiobjective optimization model for scheduling of construction projects under extreme weather. J Civ Eng Manag 22(3):373–381.

    Article  Google Scholar 

  61. 61.

    Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3(4):257–271.

    Article  Google Scholar 

  62. 62.

    Wang L, Singh C (2009) Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search. Eng Appl Artif Intell 22(2):298–307.

    Article  Google Scholar 

  63. 63.

    Maghsoudlou H, Afshar-Nadjafi B, Niaki STA (2016) A multi-objective invasive weeds optimization algorithm for solving multi-skill multi-mode resource constrained project scheduling problem. Comput Chem Eng 88:157–169.

    Article  Google Scholar 

  64. 64.

    Maghsoudlou H, Afshar-Nadjafi B, Akhavan Niaki ST (2017) Multi-skilled project scheduling with level-dependent rework risk; three multi-objective mechanisms based on cuckoo search. Appl Soft Comput 54:46–61.

    Article  Google Scholar 

  65. 65.

    Wu LH, Wang YN, Yuan XF, Zhou SW (2010) Environmental/economic power dispatch problem using multi-objective differential evolution algorithm. Electric Power Syst Res 80(9):1171–1181.

    Article  Google Scholar 

  66. 66.

    Gajzler M, Zima K (2017) Evaluation of Planned Construction Projects Using Fuzzy Logic. International Journal of Civil Engineering 15(4):641–652.

    Article  Google Scholar 

  67. 67.

    Song X, Xu J, Shen C, Peña-Mora F, Zeng Z (2017) A decision making system for construction temporary facilities layout planning in large-scale construction projects. Int J Civ Eng 15(2):333–353.

    Article  Google Scholar 

  68. 68.

    Bazargan-Lari MR (2014) An evidential reasoning approach to optimal monitoring of drinking water distribution systems for detecting deliberate contamination events. J Clean Prod 78:1–14.

    Article  Google Scholar 

  69. 69.

    Yang JB, Wang YM, Xu DL, Chin KS (2006) The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties. Eur J Oper Res 171(1):309–343.

    MathSciNet  Article  MATH  Google Scholar 

  70. 70.

    Wang T-C, Lee H-D (2009) Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Syst Appl 36(5):8980–8985.

    Article  Google Scholar 

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This research was fully funded by Tra Vinh University under grant contract number 205/HĐ-HĐKH.ĐHTV.

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Correspondence to Hai Chien Pham.

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Huynh, VH., Nguyen, TH., Pham, H.C. et al. Multiple Objective Social Group Optimization for Time–Cost–Quality–Carbon Dioxide in Generalized Construction Projects. Int J Civ Eng (2021).

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  • Multi-objective analysis
  • Social group optimization
  • Multi-criteria decision
  • Project resource tradeoffs
  • Scheduling