Skip to main content

Advertisement

Log in

An optimization model for construction project scheduling by considering CO2 emissions with multi-mode resource constraints under interval-valued fuzzy uncertainty

  • Original Paper
  • Published:
International Journal of Environmental Science and Technology Aims and scope Submit manuscript

Abstract

In Iran, the entire energy system relies on fossil fuels, which imposes significant greenhouse gas emissions. Besides, among different greenhouse gases, carbon dioxide (CO2) has the most considerable portion, and Iran is known as one of the top ten CO2 emitting countries, especially in the industry section. In this paper, a new multi-mode resource-constrained project scheduling problem is presented regarding the emitted CO2 as a greenness index. The proposed mathematical model has four objective functions, which are, minimizing the project completion time, project costs, and emitted CO2, and the fourth objective function maximizes the project quality. The time lag and reworking are regarded in the mathematical model. Reworking not only causes an increase in the project quality and more CO2 emissions but also increases the complexity of the problem. Uncertainty is an essential part of any construction project in real situations, so activities duration and cost of non-renewable and renewable resources are under interval-valued fuzzy uncertainty. To solve the mathematical model with uncertainty, a new extended solving method is proposed. Furthermore, a case study in Iran is presented to show the impact of the given model in real projects, and related results along with the analyses are conducted on this case. Additionally, Pareto front solutions are presented to show the trade-off between objectives. The results illustrate that considering CO2 emissions as a greenness index can reduce project costs and improve quality. On the other hand, this index increases the project completion time. This paper has practical implications for project managers and companies to reach their fundamental goals (i.e., time, cost, and quality) alongside minimizing emitted CO2 in an uncertain environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Fig. 1

(Source: IEA 2017)

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Agency, International Energy (2018) International energy agency. www.iea.org.

  • Aramesh S, Mousavi SM, Mohagheghi V (2021a) A new comprehensive project scheduling, monitoring, and management framework based on the critical chain under interval type-2 fuzzy uncertainty. Iran J Fuzzy Syst 18(1):151–170

    Google Scholar 

  • Aramesh S, Mousavi SM, Mohagheghi V, Zavadskas EK, Antucheviciene J (2021b) A soft computing approach based on critical chain for project planning and control in real-world applications with interval data. Appl Soft Comput 98:106915

    Google Scholar 

  • Arık OA, Toksarı MD (2018) Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects. Int J Prod Res 56(7):2488–2505

    Google Scholar 

  • Atli O, Kahraman C (2012) Fuzzy resource-constrained project scheduling using taboo search algorithm. Int J Intell Syst 27(10):873–907

    Google Scholar 

  • Banihashemi SA, Khalilzadeh M, Shahraki A, Malkhalifeh MR, Ahmadizadeh SSR (2021) Optimization of environmental impacts of construction projects: a time–cost–quality trade-off approach. Int J Environ Sci Technol 18(3):631–646

    Google Scholar 

  • Blazewicz J, Lenstra JK, Kan AR (1983) Scheduling subject to resource constraints: classification and complexity. Discret Appl Math 5(1):11–24

    Google Scholar 

  • BP (2017) Statistical Review of World Energy 2017. https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-worldenergy. Accessed 1 Nov 2017

  • Brucker P, Drexl A, Möhring R, Neumann K, Pesch E (1999) Resource-constrained project scheduling: notation, classification, models, and methods. Eur J Oper Res 112(1):3–41

    Google Scholar 

  • Burgelman J, Vanhoucke M (2018) Maximising the weighted number of activity execution modes in project planning. Eur J Oper Res 270(3):999–1013

    Google Scholar 

  • Chen S (2009) Engine or drag: Can high energy consumption and CO2 emission drive the sustainable development of Chinese industry? Front Econ China 4(4):548–571

    Google Scholar 

  • Chen SJ, Chen SM (2003) Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. IEEE Trans Fuzzy Syst 11(1):45–56

    Google Scholar 

  • Creemers S (2015) Minimizing the expected makespan of a project with stochastic activity durations under resource constraints. J Sched 18(3):263–273

    Google Scholar 

  • Dorfeshan Y, Mousavi SM, Zavadskas EK, Antucheviciene J (2021) A new enhanced ARAS method for critical path selection of engineering projects with interval type-2 fuzzy sets. Int J Inf Technol Decis Making 20(1):37–65

    Google Scholar 

  • Davoudabadi R, Mousavi SM, Mohagheghi V (2021) A new decision model based on DEA and simulation to evaluate renewable energy projects under interval-valued intuitionistic fuzzy uncertainty. Renew Energy 164:1588–1601

    Google Scholar 

  • Demeulemeester EL, Herroelen WS (2002) Scope and relevance of project scheduling. Project Scheduling: A Research Handbook, vol 49. Springer, Boston, MA, pp 1–11. https://doi.org/10.1007/0-306-48142-1_1

    Book  Google Scholar 

  • Dodin B (2006) A practical and accurate alternative to PERT. Perspectives in modern project scheduling. Springer, Boston, pp 3–23. https://link.springer.com/chapter/10.1007/978-0-387-33768-5_1

    Google Scholar 

  • Duflou JR, Sutherland JW, Dornfeld D, Herrmann C, Jeswiet J, Kara S, Kellens K (2012) Towards energy and resource efficient manufacturing: a processes and systems approach. CIRP Ann 61(2):587–609

    Google Scholar 

  • Elloumi S, Loukil T, Fortemps P (2021) Reactive heuristics for disrupted multi-mode resource-constrained project scheduling problem. Expert Syst Appl 167:114132

    Google Scholar 

  • Faridzad A, Banouei AA, Banouei J, Golestan Z (2020) Identifying energy-intensive key sectors in Iran: evidence from decomposed input-output multipliers. J Clean Prod 243:118653

    Google Scholar 

  • Gahm C, Denz F, Dirr M, Tuma A (2016) Energy-efficient scheduling in manufacturing companies: a review and research framework. Eur J Oper Res 248(3):744–757

    Google Scholar 

  • Ghasemi M, Mousavi SM, Aramesh S (2020) A new combination of multi-mode resource-constrained project scheduling and group decision-making process with interval-fuzzy information. J Ind Syst Eng 13(1):216–239

    Google Scholar 

  • Gorzalczany MB (1987) A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets Syst 21(1):1–17

    Google Scholar 

  • Grattan-Guinness I (1976) Fuzzy membership mapped onto intervals and many-valued quantities. Math Log Q 22(1):149–160

    Google Scholar 

  • Hall NG, Posner ME (2004) Sensitivity analysis for scheduling problems. J Sched 7(1):49–83

    Google Scholar 

  • Hartmann S, Briskorn D (2010) A survey of variants and extensions of the resource-constrained project scheduling problem. Eur J Oper Res 207(1):1–14

    Google Scholar 

  • Hartmann S, Kolisch R (2000) Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem. Eur J Oper Res 127(2):394–407

    Google Scholar 

  • Herroelen W, Leus R (2005) Project scheduling under uncertainty: survey and research potentials. Eur J Oper Res 165(2):289–306

    Google Scholar 

  • Huang X, Dai W, Du B (2016) Resource-constrained project scheduling problem for large complex equipment: a hybrid approach using pareto genetic algorithm and interval-valued intuitionistic fuzzy sets. Acad J Manuf Eng 14(1):12–21

    Google Scholar 

  • IEA P (2016) CO2 Emissions from fuel combustion 2016. IEA

  • Javanmard S, Afshar-Nadjafi B, Niaki STA (2021) A bi-objective model for scheduling of multiple projects under multi-skilled workforce for distributed load energy usage. Oper Res 22(3):2245–2280

    Google Scholar 

  • Jiménez M, Arenas M, Bilbao A, Rodrı MV (2007) Linear programming with fuzzy parameters: an interactive method resolution. Eur J Oper Res 177(3):1599–1609

    Google Scholar 

  • Kolisch R, Hartmann S (2006) Experimental investigation of heuristics for resource-constrained project scheduling: an update. Eur J Oper Res 174(1):23–37

    Google Scholar 

  • Li H, Womer NK (2015) Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming. Eur J Oper Res 246(1):20–33

    Google Scholar 

  • Long LD, Ohsato A (2008) Fuzzy critical chain method for project scheduling under resource constraints and uncertainty. Int J Project Manag 26(6):688–698

    Google Scholar 

  • Luong DL, Tran DH, Nguyen PT (2021) Optimizing multi-mode time-cost-quality trade-off of construction project using opposition multiple objective difference evolution. Int J Constr Manag 21(3):271–283

    Google Scholar 

  • Mansouri SA, Aktas E, Besikci U (2016) Green scheduling of a two-machine flowshop: trade-off between makespan and energy consumption. Eur J Oper Res 248(3):772–788

    Google Scholar 

  • Manzoor D, Aryanpur V (2017) Power sector development in Iran: a retrospective optimization approach. Energy 140:330–339

    Google Scholar 

  • Mohagheghi V, Mousavi SM, Mojtahedi M, Newton S (2019) Evaluating large, high-technology project portfolios using a novel interval-valued Pythagorean fuzzy set framework: an automated crane project case study. Expert Syst Appl 162:113007

    Google Scholar 

  • Mousavi B, Lopez NSA, Biona JBM, Chiu AS, Blesl M (2017) Driving forces of Iran’s CO2 emissions from energy consumption: an LMDI decomposition approach. Appl Energy 206:804–814

    Google Scholar 

  • Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, Dubash NK (2014) Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Ipcc, p 151

  • Reuters (2015) Europe to draw up energy crisis contingency plans. http://uk.reuters.com/article/2015/04/16/eu-energy-crisis-idUKL5N0XD3GH20150416. Accessed 23 Sept 2015

  • Selim H, Ozkarahan I (2008) A supply chain distribution network design model: an interactive fuzzy goal programming-based solution approach. Int J Adv Manuf Technol 36(3–4):401–418

    Google Scholar 

  • Servranckx T, Vanhoucke M (2019) A tabu search procedure for the resource-constrained project scheduling problem with alternative subgraphs. Eur J Oper Res 273(3):841–860

    Google Scholar 

  • Shabani ZD, Shahnazi R (2019) Energy consumption, carbon dioxide emissions, information and communications technology, and gross domestic product in Iranian economic sectors: a panel causality analysis. Energy 169:1064–1078

    Google Scholar 

  • Shiyi CHEN (2009) Engine or drag: can high energy consumption and CO2 emission drive the sustainable development of Chinese industry? Front Econ China 4(4):548–571

    Google Scholar 

  • Słowiński R (1980) Two approaches to problems of resource allocation among project activities—a comparative study. J Oper Res Soc 31(8):711–723

    Google Scholar 

  • Stork FR (2000) Branch-and-bound algorithms for stochastic resource-constrained project scheduling. Technical Rep, 702–2000

  • Sun A (2013) The establishment of the green tax policy in China-To accelerate the construction of circular economy experimental zone in Qaidam basin of Qinghai Province as an example. Asian Soc Sci 9(3):148

    Google Scholar 

  • Turner JR, Keegan A (1999) The versatile project-based organization: governance and operational control. Eur Manag J 17(3):296–309

    Google Scholar 

  • Van de Vonder S, Ballestin F, Demeulemeester E, Herroelen W (2007) Heuristic procedures for reactive project scheduling. Comput Ind Eng 52(1):11–28

    Google Scholar 

  • Weglarz J (ed) (2012) Project scheduling: recent models, algorithms and applications, vol 14. Springer, Berlin

    Google Scholar 

  • Weglarz J, Józefowska J, Mika M, Waligóra G (2011) Project scheduling with finite or infinite number of activity processing modes–a survey. Eur J Oper Res 208(3):177–205

    Google Scholar 

  • Wu X, Sun Y (2018) A green scheduling algorithm for flexible job shop with energy-saving measures. J Clean Prod 172:3249–3264

    Google Scholar 

  • Yao JS, Lin FT (2002) Constructing a fuzzy flow-shop sequencing model based on statistical data. Int J Approx Reason 29(3):215–234

    Google Scholar 

  • Yazdan GF, Behzad V, Shiva M (2012) Energy consumption in Iran: past trends and future directions. Procedia Soc Behav Sci 62:12–17

    Google Scholar 

  • Zadeh LA (1968) Probability measures of fuzzy events. J Math Anal Appl 23(2):421–427

    Google Scholar 

  • Zadeh LA (1976) A fuzzy-algorithmic approach to the definition of complex or imprecise concepts. Int J Man Mach Stud 8(3):249–291

    Google Scholar 

  • Zavadskas EK, Antucheviciene J, Kar S (2019) Multi-objective and multi-attribute optimization for sustainable development decision aiding. Sustainability 11(11):3069. https://doi.org/10.3390/su11113069

    Article  Google Scholar 

  • Zolfaghari S, Mousavi SM, Antuchevičienė J (2021) A type-2 fuzzy optimization model for project portfolio selection and scheduling by incorporating project interdependency and splitting. Technol Econ Dev Econ 27(2):493–510

    Google Scholar 

  • Zhang Z, Zhong X (2018) Time-cost trade-off resource-constrained project scheduling problem with stochastic duration and time crashing. Int J Appl Decis Sci 11(4):390–419

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank anonymous referees for their valuable comments and recommendations on the study. The authors did not receive support from any organization for the study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. M. Mousavi.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in the study.

Consent for publication

This study does not contain any studies with human participants or animals performed by any authors.

Ethical approval

The manuscript is original. It has not been published previously by any of the authors and even not under consideration in any other journal at the time of submission.

Additional information

Editorial responsibility: S. R. Sabbagh-Yazdi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aramesh, S., Mousavi, S.M., Ghasemi, M. et al. An optimization model for construction project scheduling by considering CO2 emissions with multi-mode resource constraints under interval-valued fuzzy uncertainty. Int. J. Environ. Sci. Technol. 20, 87–102 (2023). https://doi.org/10.1007/s13762-022-04377-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13762-022-04377-4

Keywords

Navigation