Optimal Earthmoving-Equipment Combination Considering Carbon Emissions with an Indicator-Based Multiobjective Optimizer
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 1
Abstract
Reasonable earthmoving-equipment combinations have helped to reduce duration and cost in the construction industry traditionally. However, reducing carbon emissions in earthmoving operations is highly desirable because earthmoving operations involve a large number of heavy equipment, which produce high carbon emissions. It is necessary to study a novel equipment combination optimization model considering decreasing carbon emissions in addition to duration and cost. Hence, a triobjective equipment combination optimization (ECO) model is developed, minimizing carbon emissions as well as project duration and cost. In this model, the excavation and transportation processes of earthwork are regarded as a queuing system, and queuing theory is integrated directly into the model to compute the involved parameters instead of a simulation. On the other hand, for obtaining the optimal solution set of the multiobjective problems with more objectives, a novel two-archive multiobjective particle swarm algorithm is proposed, which updates individuals in archives by not only Pareto domination but also an indicator that can assess solution quality. A real case study demonstrated that the model can efficiently provide a reasonable earthmoving-equipment combination considering the trade-off among carbon emissions, duration, and cost for managers.
Practical Applications
A multiobjective model for obtaining the optimal earthmoving-equipment combination is proposed in this paper. In addition to traditional reductions in duration and cost, the model also considers sustainability requirements by reducing carbon emissions in the construction industry. In other words, this model can offer the solutions of equipment combinations in terms of not only reducing the cost and duration to construction managers, but also reducing carbon emissions. Instead of one combination of earthmoving equipment, a set of nondominated solutions is offered in this model, and this allows managers to make choices based on specific site conditions. In addition, the authors have improved the traditional algorithm to obtain the solutions to better meet the needs of users. This new earthmoving-equipment combination optimization model can quickly get reasonable equipment combinations and helps minimize carbon emissions as well as duration and cost. This model has been applied to a real earthwork project in China and gave the optimal excavator-truck configuration that satisfied the contractor.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The data generated and analyzed during the study are available from the corresponding author on request.
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Project No. 51478480, and in part by the Natural Science Foundation of Hunan Province (CN) under Grant No. 12JJ3040.
References
Alshboul, O., A. Shehadeh, O. Tatari, G. Almasabha, and E. Saleh. 2022. “Multiobjective and multivariable optimization for earthmoving equipment.” J. Facil. Manage. https://doi.org/10.1108/JFM-10-2021-0129.
Barth, M., and K. Boriboonsomsin. 2008. “Real-world carbon dioxide impacts of traffic congestion.” Transp. Res. Rec. 2058 (1): 163–171. https://doi.org/10.3141/2058-20.
Barth, M., T. Younglove, and G. Scora. 2005. Development of a heavy-duty diesel modal emissions and fuel consumption model. Technical Rep., 1431–1440. Berkeley, CA: Univ. of California, Berkeley.
Brockhoff, D., T. Wagner, and H. Trautmann. 2012. “On the properties of the R2 indicator.” In Proc., GECCO’12—Proc. of the 14th Annual Conf. on Genetic and Evolutionary Computation, 465–472. New York: Association for Computing Machinery. https://doi.org/10.1145/2330163.2330230.
Brockhoff, D., T. Wagner, and H. Trautmann. 2015. “R2 indicator-based multiobjective search.” Evol. Comput. 23 (3): 369–395. https://doi.org/10.1162/EVCO_a_00135.
Cheng, T. M., C. W. Feng, and Y. L. Chen. 2005. “A hybrid mechanism for optimizing construction simulation models.” Autom. Constr. 14 (1): 85–98. https://doi.org/10.1016/j.autcon.2004.07.014.
Chugh, T., Y. Jin, K. Miettinen, J. Hakanen, and K. Sindhya. 2018. “A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization.” IEEE Trans. Evol. Comput. 22 (1): 129–142. https://doi.org/10.1109/TEVC.2016.2622301.
Coello, C. A. C., and M. S. Lechuga. 2002. “MOPSO: A proposal for multiple objective particle swarm optimization.” In Vol. 2 of Proc., 2002 Congress on Evolutionary Computation. CEC′02, 1051–1056. New York: IEEE.
Correia, A. G., M. Parente, and P. Cortez. 2015. “Earthwork optimization system for sustainable highway construction.” In Advances in soil mechanics and geotechnical engineering: Geotechnical Synergy in Buenos Aires 2015, 121–138. Amsterdam, Netherlands: IOS Press. https://doi.org/10.3233/978-1-61499-599-9-121.
Deb, K., and H. Jain. 2014. “An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints.” IEEE Trans. Evol. Comput. 18 (4): 577–601. https://doi.org/10.1109/TEVC.2013.2281535.
Jassim, H. S. H., J. Krantz, W. Lu, and T. Olofsson. 2020. “A model to reduce earthmoving impacts.” J. Civ. Eng. Manage. 26 (6): 490–512. https://doi.org/10.3846/jcem.2020.12641.
Jiang, S., and S. Yang. 2017. “A strength Pareto evolutionary algorithm based on reference direction for multiobjective and many-objective optimization.” IEEE Trans. Evol. Comput. 21 (3): 329–346. https://doi.org/10.1109/TEVC.2016.2592479.
Kim, B. S., and Y. W. Kim. 2016. “Configuration of earthwork equipment considering environmental impacts, cost and schedule.” J. Civ. Eng. Manage. 22 (1): 73–85. https://doi.org/10.3846/13923730.2014.897964.
Knowles, J. 2006. “ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems.” IEEE Trans. Evol. Comput. 10 (1): 50–66. https://doi.org/10.1109/TEVC.2005.851274.
Li, F., J. Liu, S. Tan, and X. Yu. 2015. “R2-MOPSO: A multi-objective particle swarm optimizer based on R2-indicator and decomposition.” In Proc., 2015 IEEE Congress on Evolutionary Computation (CEC), 3148–3155. New York: IEEE.
Martinez, J. C. 2001. “EZStrobe—General-purpose simulation system based on activity cycle diagrams.” In Vol. 2 of Proc., 2001 Winter Simulation Conf., 1556–1564. New York: IEEE.
Marzouk, M., and O. Moselhi. 2002. “Simulation optimization for earthmoving operations using genetic algorithms.” Construct. Manage. Econ. 20 (6): 535–543. https://doi.org/10.1080/01446190210156064.
Marzouk, M., and O. Moselhi. 2004. “Multiobjective optimization of earthmoving operations.” J. Constr. Eng. Manage. 130 (1): 105–113. https://doi.org/10.1061/(ASCE)0733-9364(2004)130:1(105).
Nie, X., and J. Luo. 2021. “The hybrid intelligent optimization algorithm and multi-objective optimization based on big data.” J. Phys.: Conf. Ser. 1757 (1): 012132. https://doi.org/10.1088/1742-6596/1757/1/012132.
Noland, R. B., and C. S. Hanson. 2015. “Life-cycle greenhouse gas emissions associated with a highway reconstruction: A New Jersey case study.” J. Cleaner Prod. 107 (Nov): 731–740. https://doi.org/10.1016/j.jclepro.2015.05.064.
Oh, B. K., S. W. Choi, and H. S. Park. 2017. “Influence of variations in emission data upon environmental impact of building construction.” J. Cleaner Prod. 140 (Jan): 1194–1203. https://doi.org/10.1016/j.jclepro.2016.10.041.
Osaki, S. 1992. Applied stochastic system modeling. Berlin: Springer.
Ozcan-Deniz, G., and Y. Zhu. 2017. “Multi-objective optimization of greenhouse gas emissions in highway construction projects.” Sustainable Cities Soc. 28 (Jan): 162–171. https://doi.org/10.1016/j.scs.2016.09.009.
Parente, M., A. G. Correia, and P. Cortez. 2016. “A novel integrated optimization system for earthwork tasks.” Transp. Res. Procedia 14 (Jan): 3601–3610. https://doi.org/10.1016/j.trpro.2016.05.428.
Parente, M., P. Cortez, and A. G. Correia. 2015. “An evolutionary multi-objective optimization system for earthworks.” Expert Syst. Appl. 42 (19): 6674–6685. https://doi.org/10.1016/j.eswa.2015.04.051.
Patcharachavalit, N., C. Limsawasd, and N. Athigakunagorn. 2023. “Multiobjective optimization for improving sustainable equipment options in road construction projects.” J. Constr. Eng. Manage. 149 (1): 04022160. https://doi.org/10.1061/JCEMD4.COENG-12544.
Penman, J., M. Gytarsky, T. Hiraishi, W. Irving, and T. Krug. 2006. 2006 IPCC—Guidelines for national greenhouse gas inventories. Hayamal, Japan: Institute for Global Environmental Strategies.
Phan, D. H., and J. Suzuki. 2013. “R2-IBEA: R2 indicator based evolutionary algorithm for multiobjective optimization.” In Proc., 2013 IEEE Congress on Evolutionary Computation, CEC 2013, 1836–1845. New York: IEEE.
Praditwong, K., and X. Yao. 2006. “A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm.” In Vol. 1 of Proc., 2006 Int. Conf. on Computational Intelligence and Security, ICCIAS 2006, 286–291. New York: IEEE. https://doi.org/10.1109/ICCIAS.2006.294139.
Sawhney, A., S. M. AbouRizk, and D. W. Halpin. 1998. “Construction project simulation using CYCLONE.” Can. J. Civ. Eng. 25 (1): 16–25. https://doi.org/10.1139/l97-047.
Shawki, K. M., K. Kilani, and M. A. Gomaa. 2015. “Analysis of earth-moving systems using discrete-event simulation.” Alexandria Eng. J. 54 (3): 533–540. https://doi.org/10.1016/j.aej.2015.03.034.
Shehadeh, A., O. Alshboul, O. Tatari, M. A. Alzubaidi, and A. Hamed El-Sayed Salama. 2022. “Selection of heavy machinery for earthwork activities: A multi-objective optimization approach using a genetic algorithm.” Alexandria Eng. J. 61 (10): 7555–7569. https://doi.org/10.1016/j.aej.2022.01.010.
Trani, M. L., B. Bossi, M. Gangolells, and M. Casals. 2016. “Predicting fuel energy consumption during earthworks.” J. Cleaner Prod. 112 (Jan): 3798–3809. https://doi.org/10.1016/j.jclepro.2015.08.027.
Wang, H., L. Jiao, and X. Yao. 2015. “Two_Arch2: An improved two-archive algorithm for many-objective optimization.” IEEE Trans. Evol. Comput. 19 (4): 524–541. https://doi.org/10.1109/TEVC.2014.2350987.
Wu, X. 1997. “Determining the number of dump trucks matched with excavators by queuing theory.” [In Chinese.] Yangtze River Water Conserv. Educ. 14 (3): 64–65.
Yang, H., Z. Xu, M. Fan, R. Gupta, R. B. Slimane, A. E. Bland, and I. Wright. 2008. “Progress in carbon dioxide separation and capture: A review.” J. Environ. Sci. 20 (1): 14–27. https://doi.org/10.1016/S1001-0742(08)60002-9.
Yang, S., M. Li, X. Liu, and J. Zheng. 2013. “A grid-based evolutionary algorithm for many-objective optimization.” IEEE Trans. Evol. Comput. 17 (5): 721–736. https://doi.org/10.1109/TEVC.2012.2227145.
Zhang, H. 2008. “Multi-objective simulation-optimization for earthmoving operations.” Autom. Constr. 18 (1): 79–86. https://doi.org/10.1016/j.autcon.2008.05.002.
Zhang, Q., and H. Li. 2007. “MOEA/D: A multiobjective evolutionary algorithm based on decomposition.” IEEE Trans. Evol. Comput. 11 (6): 712–731. https://doi.org/10.1109/TEVC.2007.892759.
Zitzler, E., J. Knowles, and L. Thiele. 2008. “Quality assessment of Pareto set approximations.” In Multiobjective optimization: Interactive and evolutionary approaches. Lecture notes in computer science, 373–404. Berlin: Springer.
Zitzler, E., and S. Künzli. 2004. “Indicator-based selection in multiobjective search.” In Vol. 3242 of Parallel problem solving from nature—PPSN VIII. PPSN 2004. Lecture notes in computer science, 832–842. Berlin: Springer.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 150 • Issue 1 • January 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jan 2, 2023
Accepted: Aug 30, 2023
Published online: Nov 3, 2023
Published in print: Jan 1, 2024
Discussion open until: Apr 3, 2024
ASCE Technical Topics:
- Air pollution
- Benefit cost ratios
- Business management
- Computer models
- Construction costs
- Construction engineering
- Construction management
- Construction sites
- Earthmoving
- Emissions
- Engineering fundamentals
- Engineering materials (by type)
- Environmental engineering
- Financial management
- Materials engineering
- Models (by type)
- Optimization models
- Particles
- Pollution
- Practice and Profession
- Project management
- Simulation models
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.