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.

(Source: IEA 2017)











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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
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
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
Atli O, Kahraman C (2012) Fuzzy resource-constrained project scheduling using taboo search algorithm. Int J Intell Syst 27(10):873–907
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
Blazewicz J, Lenstra JK, Kan AR (1983) Scheduling subject to resource constraints: classification and complexity. Discret Appl Math 5(1):11–24
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
Burgelman J, Vanhoucke M (2018) Maximising the weighted number of activity execution modes in project planning. Eur J Oper Res 270(3):999–1013
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
Chen SJ, Chen SM (2003) Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. IEEE Trans Fuzzy Syst 11(1):45–56
Creemers S (2015) Minimizing the expected makespan of a project with stochastic activity durations under resource constraints. J Sched 18(3):263–273
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
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
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
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
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
Elloumi S, Loukil T, Fortemps P (2021) Reactive heuristics for disrupted multi-mode resource-constrained project scheduling problem. Expert Syst Appl 167:114132
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
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
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
Gorzalczany MB (1987) A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets Syst 21(1):1–17
Grattan-Guinness I (1976) Fuzzy membership mapped onto intervals and many-valued quantities. Math Log Q 22(1):149–160
Hall NG, Posner ME (2004) Sensitivity analysis for scheduling problems. J Sched 7(1):49–83
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
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
Herroelen W, Leus R (2005) Project scheduling under uncertainty: survey and research potentials. Eur J Oper Res 165(2):289–306
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
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
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
Kolisch R, Hartmann S (2006) Experimental investigation of heuristics for resource-constrained project scheduling: an update. Eur J Oper Res 174(1):23–37
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
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
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
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
Manzoor D, Aryanpur V (2017) Power sector development in Iran: a retrospective optimization approach. Energy 140:330–339
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
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
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
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
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
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
Słowiński R (1980) Two approaches to problems of resource allocation among project activities—a comparative study. J Oper Res Soc 31(8):711–723
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
Turner JR, Keegan A (1999) The versatile project-based organization: governance and operational control. Eur Manag J 17(3):296–309
Van de Vonder S, Ballestin F, Demeulemeester E, Herroelen W (2007) Heuristic procedures for reactive project scheduling. Comput Ind Eng 52(1):11–28
Weglarz J (ed) (2012) Project scheduling: recent models, algorithms and applications, vol 14. Springer, Berlin
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
Wu X, Sun Y (2018) A green scheduling algorithm for flexible job shop with energy-saving measures. J Clean Prod 172:3249–3264
Yao JS, Lin FT (2002) Constructing a fuzzy flow-shop sequencing model based on statistical data. Int J Approx Reason 29(3):215–234
Yazdan GF, Behzad V, Shiva M (2012) Energy consumption in Iran: past trends and future directions. Procedia Soc Behav Sci 62:12–17
Zadeh LA (1968) Probability measures of fuzzy events. J Math Anal Appl 23(2):421–427
Zadeh LA (1976) A fuzzy-algorithmic approach to the definition of complex or imprecise concepts. Int J Man Mach Stud 8(3):249–291
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
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
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
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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.
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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
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DOI: https://doi.org/10.1007/s13762-022-04377-4


