Production
https://prod.org.br/article/doi/10.1590/0103-6513.20190065
Production
Thematic Section - Sustainability in Transportation and Logistics

Impact of the inclusion of variable CO2 cost in the distribution network design

Rodrigo de Castro Barros; Mauro Sampaio; Jobel Santos Correa

Downloads: 0
Views: 984

Abstract

Abstract: Paper aims: This study aims to evaluate the economic-financial impact that the inclusion of environmental variable CO2 cost attributed to transportation (fuel consumption) and manufacturing activities (electricity consumption) represents in a distribution network optimization design.

Originality: This is the first work carried out in Brazil to present a feasible eco-efficient distribution network in the technical and economic aspects considering the tax aspects of ICMS. In addition, this work addresses the inclusion of CO2 as a cost associated with transportation and manufacturing activities. Finally, for the first time in literature, a reference framework that considers environmental variables is presented.

Research method: This study used the systematic literature review method to review the referential framework and carried out a cost reduction using specialist optimization software.

Main findings: The technical and economical feasibility in achieving reduction of both total logistic and CO2 costs in a distribution network design in Brazil and the inexistence of trade-offs without technical solution to the eco-efficient configuration of the distribution network.

Implications for theory and practice: The results of this paper presents a relevant contribution to logistics professionals and researchers, as it was able to present a framework and demonstrated the feasibility of an eco-efficient network design.

Keywords

Distribution network design, Eco-efficient distribution network, Optimization. Strategic decisions, CO2 cost

References

Ala-Harja, H., & Helo, P. (2015). Reprint of green supply chain decisions: case-based performance analysis from the food industry. Transportation Research Part E, Logistics and Transportation Review, 74, 11-21. http://dx.doi.org/10.1016/j.tre.2014.12.005.

Ameknassi, L., Aït-Kadi, D., & Rezg, N. (2016). Integration of logistics outsourcing decisions in a green supply chain design: a stochastic multi-objective multi-period multi-product programming model. International Journal of Production Economics, 182, 165-184. http://dx.doi.org/10.1016/j.ijpe.2016.08.031.

Banasik, A., Kanellopoulos, A., Claassen, G. D. H., Bloemhof-Ruwaard, J. M., & van der Vorst, J. G. A. (2017). Closing loops in agricultural supply chains using multi-objective optimization: a case study of na industrial mushroom supply chain. International Journal of Production Economics, 183, 409-420. http://dx.doi.org/10.1016/j.ijpe.2016.08.012.

Bing, X., Bloemhof-Ruwaard, J. M., & Van der Vorst, J. G. A. J. (2014). Sustainable reverse logistics network design for household plastic waste. Flexible Services and Manufacturing Journal, 26(1-2), 119-142. http://dx.doi.org/10.1007/s10696-012-9149-0.

Bouchery, Y., Ghaffari, A., Jemai, Z., & Fransoo, J. (2016). Sustainable transportation and order quantity: insights from multiobjective optimization. Flexible Services and Manufacturing Journal, 28(3), 367-396. http://dx.doi.org/10.1007/s10696-016-9240-z.

Brandenburg, M. (2015). Low carbon supply chain configuration for a new product: a goal programming approach. 2015. International Journal of Production Research, 53(21), 6588-6610. http://dx.doi.org/10.1080/00207543.2015.1005761.

Brasil. (1988). Constituição da República Federativa do Brasil 1988 (21. ed., Coleção Saraiva de Legislação). São Paulo: Saraiva.

Brown, J., & Guiffrida, A. (2014). Carbon emissions comparison of last mile delivery versus customer pick-up. International Journal of Logistics Research and Applications, 17(6), 503-521. http://dx.doi.org/10.1080/13675567.2014.907397.

Büyükozkan, G., & Cifci, G. (2012). Evaluation of the green supply chain management practices: a fuzzy ANP approach. Production Planning & Control: The Management of Operations, 23(6), 405-418. http://dx.doi.org/10.1080/09537287.2011.561814.

Cheshmehgaz, H. R., Desa, M. I., & Wibowo, A. (2013). A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm. Journal of Intelligent Manufacturing, 24(2), 277-293. http://dx.doi.org/10.1007/s10845-011-0584-7.

Chowdhury, M., & Quaddus, M. (2017). Supply chain resilience: conceptualization and scale development using dynamic capbility theory. International Journal of Production Economics, 188, 185-204. http://dx.doi.org/10.1016/j.ijpe.2017.03.020.

Colicchia, C., Creazza, A., Dallari, F., & Melacini, M. (2015). Eco-efficient supply chain networks: development of a design framework and application to a real case study. Production Planning and Control, 27(3), 157-168. http://dx.doi.org/10.1080/09537287.2015.1090030.

Croxton, K. L., & Zinn, W. (2005). Inventory considerations in network design. Journal of Business Logistics, 26(1), 149-165. http://dx.doi.org/10.1002/j.2158-1592.2005.tb00197.x.

Das, C., & Jharkharia, S. (2018). Low carbon supply chain: a state-of-the-art literature review. Journal of Manufacturing Technology Management, 29, 398-428. http://dx.doi.org/10.1108/JMTM-09-2017-0188.

Elhedhli, S., & Merrick, R. (2012). Green supply chain network design to reduce carbon emissions. Transportation Research Part D, Transport and Environment, 17(5), 370-379. http://dx.doi.org/10.1016/j.trd.2012.02.002.

Fahimnia, B., & Jabbarzadeh, A. (2016). Marrying supply chain sustainability and resilience: a match made in heaven. Transportation Research Part E, Logistics and Transportation Review, 91, 306-324. http://dx.doi.org/10.1016/j.tre.2016.02.007.

Fahimnia, B., Jabbarzadeh, A., Ghavamifar, A., & Bell, M. (2016). Supply chain design for efficient and effective blood supply in disasters. International Journal of Production Economics, 183, 700-709. http://dx.doi.org/10.1016/j.ijpe.2015.11.007.

Frias, L. F. M., Farias, I. A., & Wanke, P. F. (2013). Planejamento de redes logísticas: um estudo de caso na indústria petroquímica brasileira. Revista de Administração Mackenzie, 14(4), 222-250. http://dx.doi.org/10.1590/S1678-69712013000400009.

Fundação Getúlio Vargas – FGV, Escola de Administração de Empresas – EAESP, Centro de Estudo em Sustentabilidade – GVCES. (2018). Gestão e precificação de carbono: riscos e oportunidades para instituições financeiras. São Paulo.

Glock, C., & Kim, T. (2015). Coordinating a supply chain with a heterogeneous vehicle fleet under greenhouse gas emissions. International Journal of Logistics Management, 26(3), 494-516. http://dx.doi.org/10.1108/IJLM-09-2013-0107.

Greenstone, M., Kopits, E., & Wolverton, A. (2013). Developing a social cost of carbon for US regulatory analysis: a methodology and interpretation. Review of Environmental Economics and Policy, 7(1), 23-46. http://dx.doi.org/10.1093/reep/res015.

Hoen, K., Tan, T., Fransoo, J., & Van Houtum, G. (2014). Effect of carbon emission regulations on transport mode selection under stochastic demand. Flexible Services and Manufacturing Journal, 26(1), 170-195. http://dx.doi.org/10.1007/s10696-012-9151-6.

Huang, G., Lau, J., & Mak, K. L. (2003). The impacts of sharing production information on supply chain dynamics: a review of the literature. International Journal of Production Research, 41(7), 1483-1517. http://dx.doi.org/10.1080/0020754031000069625.

Jain, V., & Grossmann, I. (2011). Algorithms for hybrid MILP/CP models for a class of optimization problems. INFORMS Journal on Computing, 13(4), 258-276. http://dx.doi.org/10.1287/ijoc.13.4.258.9733.

John, S., Sridharan, R., & Kumar, P. (2017). Multi-period reverse logistics network design with emission cost. International Journal of Logistics Management, 28(1), 127-149. http://dx.doi.org/10.1108/IJLM-08-2015-0143.

Junqueira, R. A. B., & Morabito, R. (2008). Planejamento otimização da produção em logística de empresas produtoras de sementes de milho: um estudo de caso. Gestão & Produção, 15(2), 367-380. http://dx.doi.org/10.1590/S0104-530X2008000200012.

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

Kumar, R., Kondapaneni, K., Dixit, V., Goswami, A., Thakur, L., & Tiwari, M. (2016). Multi-objective modeling of production and pollution routing problem with time window: a self-learning particle swarm optimization approach. Computers & Industrial Engineering, 99, 29-40. http://dx.doi.org/10.1016/j.cie.2015.07.003.

Lam, H., Varbanov, P., & Klemes, J. (2010). Optimization of regional energy supply chains utilizing renewables: p-graph approach. Computers & Chemical Engineering, 34(5), 782-792. http://dx.doi.org/10.1016/j.compchemeng.2009.11.020.

Li, J., Su, Q., & Ma, L. (2017). Production and transportation outsourcing decisions in the supply chain under single and multiple carbon policies. Journal of Cleaner Production, 141, 1109-1122. http://dx.doi.org/10.1016/j.jclepro.2016.09.157.

Macchion, L., Moretto, A., Caniato, F., Caridi, M., Danese, P., & Vinelli, A. (2015). Production and supply network strategies within the fashion industry. International Journal of Production Economics, 185, 173-188. http://dx.doi.org/10.1016/j.ijpe.2014.09.006.

Mangiaracina, R., Song, G., & Perego, A. (2015). Distribution network design: a literature review and a research agenda. International Journal of Physical Distribution & Logistics Management, 45(5), 506-531. http://dx.doi.org/10.1108/IJPDLM-02-2014-0035.

Mohammed, A., & Wang, Q. (2017). The fuzzy multi-objective distribution planner for a green meat supply chain. International Journal of Production Economics, 184, 47-58. http://dx.doi.org/10.1016/j.ijpe.2016.11.016.

Müller, E., Stock, T., & Schillig, R. (2014). A method to generate energy value-streams in production and logistics in respect of time-and energy-consumption. Production Engineering, 8(1-2), 243-251. http://dx.doi.org/10.1007/s11740-013-0516-9.

Musavi, M., & Bozorgi-Amiri, A. (2017). A multi-objective sustainable hub location-scheduling problem for perishable food supply chain. Computers & Industrial Engineering, 113, 766-778. http://dx.doi.org/10.1016/j.cie.2017.07.039.

Nordhaus, W. (1993). Rolling the ‘DICE’: an optimal transition path for controlling greenhouse gases. Resource and Energy Economics, 15(1), 27-50. http://dx.doi.org/10.1016/0928-7655(93)90017-O.

Nurjanni, K., Carvalho, M., & Costa, L. (2017). Green supply chain design: a mathematical modeling approach based on a multi-objective optimization model. International Journal of Production Economics, 183, 421-432. http://dx.doi.org/10.1016/j.ijpe.2016.08.028.

Paksoy, T., Bektaş, T., & Özceylan, E. (2011). Operational and environmental performance measures in a multi-product closed-loop supply chain. Transportation Research Part E, Logistics and Transportation Review, 47(4), 532-546. http://dx.doi.org/10.1016/j.tre.2010.12.001.

Perez-Franco, R., Phadnis, S., Caplice, C., & Sheffi, Y. (2016). Rethinking supply chain strategy as a conceptual system. International Journal of Production Economics, 182, 384-396. http://dx.doi.org/10.1016/j.ijpe.2016.09.012.

Pessôa, L. C., Silva, M. M., & Campanário, M. A. (2011). Custo tributário em projetos de investimento: o caso dos créditos de ICMS. Revista Brasileira de Gestão de Negócios, 13(38), 21-40. http://dx.doi.org/10.7819/rbgn.v13i38.752.

Piecyk, M., & McKinnon, A. (2010). Forecasting the carbon footprint of road freight transport in 2020. International Journal of Production Economics, 128(1), 31-42. http://dx.doi.org/10.1016/j.ijpe.2009.08.027.

Pishvaee, M. S., Torabi, S., & Razmi, J. (2012). Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Computers & Industrial Engineering, 62(2), 624-632. http://dx.doi.org/10.1016/j.cie.2011.11.028.

Qiu, Y., Qiao, J., & Pardalos, P. (2017). A branch-and-price algorithm for production routing problems with carbon cap-and-trade. Omega, 68, 49-61. http://dx.doi.org/10.1016/j.omega.2016.06.001.

Rao, C., Goh, M., Zhao, Y., & Zheng, J. (2015). Location selection of city logistics centers under sustainability. Transportation Research Part D, Transport and Environment, 36, 29-44. http://dx.doi.org/10.1016/j.trd.2015.02.008.

Rudi, A., Frohling, M., Zimmer, K., & Schultmann, F. (2016). Freight transportation planning considering carbon emissions and in-transit holding costs: a capacitated multi-commodity network flow model. EURO Journal on Transportation and Logistics, 5(2), 123-160. http://dx.doi.org/10.1007/s13676-014-0062-4.

Seuring, S., & Gold, S. (2012). Conducting content-analysis based literature reviews in supply chain management. Supply Chain Management, 17(5), 544-555. http://dx.doi.org/10.1108/13598541211258609.

Shaw, K., Shankar, R., Yadav, S. S., & Thakur, L. S. (2013). Modeling a low-carbon garment supply chain. Production Planning & Control: The Management, 24(8-9), 851-865. http://dx.doi.org/10.1080/09537287.2012.666878.

Srivastava, S. (2007). Green supply-chain management: a state-of-the-art literature review. International Journal of Management Reviews, 9(1), 53-80. http://dx.doi.org/10.1111/j.1468-2370.2007.00202.x.

Suzuki, Y. (2016). A dual-objective metaheuristic approach to solve practical pollution routing problem. International Journal of Production Economics, 176, 143-153. http://dx.doi.org/10.1016/j.ijpe.2016.03.008.

United Nations Framework Convention on Climate Change – UNFCCC. (2010). The Cancun agreements. In 16th Conference of the Parties. Cancun: UNFCCC.

United Nations Framework Convention on Climate Change – UNFCCC. (2015). Paris agreement. In 21st Conference of the Parties. Paris: UNFCCC.

Validi, S., Bhattacharya, A., & Byrne, P. (2014). Integrated low-carbon distribution system for the demand side of a product distribution supply chain: a DoE-guided MOPSO optimiser-based solution approach. International Journal of Production Research, 52(10), 3074-3096. http://dx.doi.org/10.1080/00207543.2013.864054.

Van Loon, P., Deketele, L., Dewaele, J., McKinnon, A., & Rutherford, C. (2015). A comparative analysis of carbon emissions from online retailing of fast moving consumer goods. Journal of Cleaner Production, 106, 478-486. http://dx.doi.org/10.1016/j.jclepro.2014.06.060.

Wang, Y., Zhu, X., Lu, T., & Jeeva, A. S. (2013). Eco-efficient based logistics network design in hybrid manufacturing remanufacturing system in low-carbon economy. Journal of Industrial Engineering and Management, 6(1), 200-214. http://dx.doi.org/10.3926/jiem.665.

Zakeri, A., Dehghanian, F., Fahimnia, B., & Sarkis, J. (2015). Carbon pricing versus emission trading: a supply chain planning perspective. International Journal of Production Economics, 164, 197-205. http://dx.doi.org/10.1016/j.ijpe.2014.11.012.
 

5d8129930e8825187bbbec00 production Articles
Links & Downloads

Production

Share this page
Page Sections