A country-level multi-objective optimization model for a sustainable steel supply chain

Bárbara Lara Condé; João Flávio de Freitas Almeida; Douglas Moura Miranda; Samuel Vieira Conceição

doi:10.5585/2024.22996

Authors

Bárbara Lara Condé Universidade Federal de Minas Gerais https://orcid.org/0000-0003-3850-1575
João Flávio de Freitas Almeida Universidade Federal de Minas Gerais https://orcid.org/0000-0002-3884-217X
Douglas Moura Miranda Universidade Federal do Triângulo Mineiro https://orcid.org/0000-0001-9025-9849
Samuel Vieira Conceição Universidade Federal de Minas Gerais https://orcid.org/0000-0002-8090-264X

DOI:

https://doi.org/10.5585/2024.22996

Keywords:

sustainable supply chain design, multi-objective optimization, steel industry, genetic algorithms

Abstract

Steel supply chains have been pushed to consider environmental and social aspects, other than financial, however, in the context of Operational Research, the few papers proposing mathematical formulations and algorithms tackle only few dimensions of the problem. This study proposes a solution for sustainable steel production by formulating a multi-objective, multi-level, multi-modal, multi-product, and multi-period model and also devising an evolutionary algorithm for the problem. The results provide a Pareto front mapping the conflicting nature of economic, environmental and social objectives; show how changes in the production technology and transportation mode impact the objectives, and how locations with social vulnerability influence the decision of where and when to locate facilities. This paper provides a broad-ranging formulation and the results show its potential to help decision makers of the steel supply chain to make decisions considering not only economic factors.

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Author Biographies

Bárbara Lara Condé, Universidade Federal de Minas Gerais

Master in Industrial Engineering and graduated in Metallurgical Engineering from the School of Engineering of the Federal University of Minas Gerais, Condé received the Mário Rennó silver medal for achieving the second-highest overall average among graduates (2018). She was a fellow from the Young Talents for Science Program of CAPES at the Biomechanical Testing Laboratory of the UFMG Department of Metallurgy (2013-2014). Condé was intern and scholarship holder at the department's Metallography and Heat Treatment laboratory (2017-2018), UFMG. She has international experience as a CNPq Fellow at the University of New South Wales, Australia (2015).

João Flávio de Freitas Almeida, Universidade Federal de Minas Gerais

Adjunct Professor at the Department of Industrial Engineering at the Federal University of Minas Gerais, and doctor in Operations Research from UFMG. Prof. Almeida coordinates a project for logistics' development and innovation. (Funder: CPNq. Process 400881/2018-7). The project yielded software registered under the number BR512014000622-0. Prof. Almeida has projects developed with public agencies at the federal and state level, simulating the capacity of health systems, locating medical speciality centres and equipment. He has worked as a senior simulation engineer at Vale, production planning specialist at Usiminas, annual supply chain planning, consultant at Hewlett-Packard, Jabil Circuit, Ausenco. His research focuses on logistics, production planning, facility network design, operational research, mathematical modelling and discrete event simulation. He is a reviewer for indexed international journals.

Douglas Moura Miranda, Universidade Federal do Triângulo Mineiro

Prof. Miranda is a Control and Automation Engineer from the State University of Campinas - UNICAMP (2004) with master degree in Production Engineering from the Federal University of Minas Gerais - UFMG (2011), doctorate in Production Engineering from the Federal University of Minas Gerais - UFMG (2016) and the Warwick Business School (University of Warwick - England). Miranda's areas of interest include productivity, data analysis/data science, operational research, linear and non-linear optimization algorithms.

Samuel Vieira Conceição, Universidade Federal de Minas Gerais

Full Professor at the Department of Production Engineering at the Federal University of Minas Gerais. Post doctorate in Engineering at the University of Warwick-UK. Postdoctoral fellowship funded by CAPES, process BEX-2554 /14-3. Ph.D. in Industrial and Systems Engineering-France. He was professor of operational research and multi-criteria decision at ESSEC, Paris-France (1993-1995) and at ISAB, France (1990-1995). Prof. Conceição researches the areas of operational research, computational intelligence acting in information technology, planning and production control. He was coordinator of the Graduate Program in Production Engineering at UFMG (2002-2004) and head of the Department of Production Engineering at UFMG (2006-2008). He is currently head of the Production Engineering Department. He acts as a reviewer for indexed international journals. Member of several scientific entities such as Euroma, POMS, Informs. He has acted as a consultant for companies such as Honda Motors, Philips, Vale, Usiminas, ArcelorMittal, Hewlett-Packard, Jabil Circuit., and developed projects with public agencies at the federal and state level.

References

ANTT (Brazilian National Agency of Land Transportation), 2012. National Inventory of Atmospheric Emissions from Rail Cargo Transport. Brasilia- Brazil. [in Portuguese].

Arampantzi, C., & Minis, I. (2017). A new model for designing sustainable supply chain networks and its application to a global manufacturer. Journal of Cleaner Production, 156, 276-292. Doi: 10.1016/j.jclepro.2017.03.164

Ates, S. A. (2015). Energy efficiency and CO2 mitigation potential of the Turkish iron and steel industry using the LEAP (long-range energy alternatives planning) system. Energy, 90, 417-428. Doi: 10.1016/j.energy.2015.07.059

Barbosa-Póvoa, A. P., da Silva, C., & Carvalho, A. (2018). Opportunities and challenges in sustainable supply chain: An operations research perspective. European Journal of Operational Research, 268(2), 399-431. Doi: 10.1016/j.ejor.2017.10.036

Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European journal of operational research, 233(2), 299-312. Doi: 10.1016/j.ejor.2013.09.032

Castro Vivas, R., Sant’Anna, A. M. O., Esquerre, K. P. O., & Freires, F. G. M. (2019). Integrated method combining analytical and mathematical models for the evaluation and optimization of sustainable supply chains: A Brazilian case study. Computers & Industrial Engineering, 139, 105670. Doi: 10.1016/j.cie.2019.01.044

Chalmardi, M. K., & Camacho-Vallejo, J. F. (2019). A bi-level programming model for sustainable supply chain network design that considers incentives for using cleaner technologies. Journal of Cleaner Production, 213, 1035-1050. Doi: 10.1016/j.jclepro.2018.12.197

Climate Observatory, 2020. System for estimating greenhouse gas emissions. URL http://plataforma.seeg.eco.br/ (accessed 5.11.20). [in Portuguese].

Climate Watch, 2020. Climate Watch Data. Url: https://www.climatewatchdata.org/ghg-emissions (accessed 30.12.21).

CNT (National Confederation of Transport), 2019. Statistical Bulletin. Brasilia. [in Portuguese].

Conejo, A. N., Birat, J. P., & Dutta, A. (2020). A review of the current environmental challenges of the steel industry and its value chain. Journal of environmental management, 259, 109782. Doi: 10.1016/j.jenvman.2019.109782

Devika, K., Jafarian, A., & Nourbakhsh, V. (2014). Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques. European Journal of Operational Research, 235(3), 594-615.

Elkington, J. (2013). Enter the triple bottom line. In The triple bottom line (pp. 23-38). Routledge.

EPL (Planning and Logistics Company), 2018. National Logistics Plan Report – 2025. Brasilia. [in Portuguese]

Eskandarpour, M., Dejax, P., Miemczyk, J., & Péton, O. (2015). Sustainable supply chain network design: An optimization-oriented review. Omega, (United Kingdom) 54, 11-32. 54, Doi: 10.1016/j.omega.2015.01.006

Gen, M., Altiparmak, F., & Lin, L. (2006). A genetic algorithm for two-stage transportation problem using priority-based encoding. OR spectrum, 28(3), 337-354. Doi: 10.1007/s00291-005-0029-9

Hasanbeigi, A., Arens, M., Cardenas, J. C. R., Price, L., & Triolo, R. (2016). Comparison of carbon dioxide emissions intensity of steel production in China, Germany, Mexico, and the United States. Resources, Conservation and Recycling, 113, 127-139. Doi: 10.1016/j.resconrec.2016.06.008

IABr (Brazil Steel Institute), 2018. Sustainability Report. Rio de Janeiro. [in Portuguese].

IABr (Brazil Steel Institute), 2019. The Steel Industry in Numbers. Rio de Janeiro. [in Portuguese].

IABr (Brazil Steel Institute), 2021. URL: http://www.acobrasil.org.br [in Portuguese].

IPEA (Institute for Applied Economic Research), 2020. Atlas of Social Vulnerability. URL http://ivs.ipea.gov.br (accessed 3.27.20). [in Portuguese].

Kadziński, M., Tervonen, T., Tomczyk, M.K., Dekker, R., 2017. Evaluation of multi-objective optimization approaches for solving green supply chain design problems. Omega (United Kingdom) 68, 168–184. Doi: 10.1016/j.omega.2016.07.003

Khan, A.S., Khalid, Q.S., Naeem, K., Ahmad, R., Khan, R., Saleem, W., Pruncu, C.I., 2021. Application of exact and multi-heuristic approaches to a sustainable closed loop supply chain network design. Sustain. 13, 1–25. Doi: 10.3390/su13052433

Khorasani, S. T., & Almasifard, M. (2018). The development of a green supply chain dual-objective facility by considering different levels of uncertainty. Journal of Industrial Engineering International, 14, 593-602.

Khoshfarman Borji, M., Sayadi, A. R., & Nikbakhsh, E. (2023). A Novel Sustainable Multi-objective Optimization Model for Steel Supply Chain Design Considering Technical and Managerial Issues: a Case Study. Journal of Mining and Environment, 14(1), 295-319.

Macowski, D.H., Bonfim-Rocha, L., Orgeda, R., Camilo, R., Ravagnani, M.A.S.S., 2020. Multi-objective optimization of the Brazilian industrial sugarcane scenario: a profitable and ecological approach. Clean Technol. Environ. Policy. Doi: 10.1007/s10098-019-01802-0

Mc Loughlin, K., Lewis, K., Lascelles, D., & Nudurupati, S. (2021). Sustainability in supply chains: reappraising business process management. Production Planning & Control, 1-34. Doi: 10.1080/09537287.2021.1884764

Ministry of Environment, Brazil, 2013. National Inventory of Atmospheric Emissions by Road Motor Vehicles: Base year 2012.

Brasília. [PtBr].

Moreno-Camacho, C. A., Montoya-Torres, J. R., Jaegler, A., & Gondran, N. (2019). Sustainability metrics for real case applications of the supply chain network design problem: A systematic literature review. Journal of cleaner production, 231, 600-618. Doi : 10.1016/J.JCLEPRO.2019.05.278

Mota, B., Gomes, M.I., Carvalho, A., Barbosa-Povoa, A.P., 2018. Sustainable supply chains: An integrated modeling approach under uncertainty. Omega (United Kingdom) 77, 32–57. Doi: 10.1016/j.omega.2017.05.006

Nielsen, H. (2017). Productive efficiency in the iron and steel sector under state planning: The case of China and former Czechoslovakia in a comparative perspective. Applied Energy, 185, 1732-1743. Doi: 10.1016/j.apenergy.2015.12.125

Noh, J., & Kim, J. S. (2019). Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand. Journal of Cleaner Production, 208, 1421-1435. Doi: 10.1016/j.jclepro.2018.10.124

Resat, H. G., & Unsal, B. (2019). A novel multi-objective optimization approach for sustainable supply chain: A case study in packaging industry. Sustainable production and consumption, 20, 29-39.

Rezapour, S., Zanjirani Farahani, R., & Drezner, T. (2017). Strategic design of competing supply chain networks for inelastic

demand. Journal of the Operational Research Society, 62(10), 1784-1795. Doi: 10.1057/jors.2010.146

Sahebjamnia, N., Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M., 2018. Sustainable tire closed-loop supply chain network design: Hybrid metaheuristic algorithms for large-scale networks. J. Clean. Prod. 196, 273–296. Doi: 10.1016/j.jclepro.2018.05.245

Sarkar, B., Omair, M., & Choi, S. B. (2018). A multi-objective optimization of energy, economic, and carbon emission in a production model under sustainable supply chain management. Applied Sciences, 8(10), 1744.

Seydanlou, P., Jolai, F., Tavakkoli-Moghaddam, R., & Fathollahi-Fard, A. M. (2022). A multi-objective optimization framework for a sustainable closed-loop supply chain network in the olive industry: Hybrid meta-heuristic algorithms. Expert Systems with Applications, 203, 117566.

SICETEL (National Union of Ferrous Metals Drawing and Rolling Industries), 2019. Steel Market Analysis. São Paulo. [in Portuguese].

Sindifer (Sindicato da Indústria do Ferro no Estado de Minas Gerais), 2019. Annual Statistics. Belo Horizonte. Brazil.

Tosarkani, B. M., & Amin, S. H. (2018). A possibilistic solution to configure a battery closed-loop supply chain: multi-objective approach. Expert systems with applications, 92, 12-26. Doi: 10.1016/j.eswa.2017.09.039

Tseng, M. L., Islam, M. S., Karia, N., Fauzi, F. A., & Afrin, S. (2019). A literature review on green supply chain management: Trends and future challenges. Resources, Conservation and Recycling, 141, 145-162. Doi: 10.1016/j.resconrec.2018.10.009

UN (United Nations), 2015. Transforming Our World: the 2030 Agenda for Sustainable Development.

Usiminas, 2020. 4Q19 Earnings Release. [in Portuguese].

Vafaeenezhad, T., Tavakkoli-Moghaddam, R., & Cheikhrouhou, N. (2019). Multi-objective mathematical modeling for sustainable supply chain management in the paper industry. Computers & Industrial Engineering, 135, 1092-1102.

Yadav, V. S., Tripathi, S., & Singh, A. R. (2019). Bi-objective optimization for sustainable supply chain network design in omnichannel. Journal of Manufacturing Technology Management, 30(6), 972-986.

Zarbakhshnia, N., Soleimani, H., Goh, M., & Razavi, S. S. (2019). A novel multi-objective model for green forward and reverse logistics network design. Journal of Cleaner Production, 208, 1304-1316. Doi: 10.1016/j.jclepro.2018.10.138

Zhang, S., Lee, C. K. M., Wu, K., & Choy, K. L. (2016). Multi-objective optimization for sustainable supply chain network design

considering multiple distribution channels. Expert Systems with Applications, 65, 87-99.

Zhang, X., Zhao, G., Qi, Y., & Li, B. (2019). A robust fuzzy optimization model for closed-loop supply chain networks considering sustainability. Sustainability, 11(20), 5726. Doi: 10.3390/su11205726