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Designing a Green Supply Chain Transportation System for an Automotive Company Based On Bi-Objective Optimization

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Recently, due to the increasing awareness of communities regarding environmental issues and environmental regulations, companies have evolved to provide products with lower prices and better quality to retain and attract customers. Economics should also pay attention to environmental goals. Therefore, it is essential to provide a supply chain model that can consider both economic and environmental objectives. In this paper, the green direct supply chain network is presented to an automotive company, including five suppliers, primary warehouses, manufacturing plants, distributors, and sales centers. The objectives of this model are to minimize the total cost of construction, transportation, and the amount of carbon dioxide emissions during forwarding network transportation at all levels. The proposed model is also drawn using the weight method, which is one of the methods for solving multi-objective problems, and the solution of the model part. Ultimately, it has been discussed how much the automobile company should focus on reducing carbon dioxide so that managers can determine the best solutions from the Pareto border according to their organization's priorities, which can be environmental or financial.
Rocznik
Strony
193--207
Opis fizyczny
Bibliogr. 26 poz., rys., tab., wykr.
Twórcy
autor
  • DS & CI Research Group Universitas Medan Area, Medan, Indonesia
  • Data Science & Computational Intelligence Research Group Universitas Sumatera, Utara Medan, Indonesia
  • Economic Sciences Department of Economic Analysis, Federal State Budgetary Educational Institution of Higher Education “Kuban State Agrarian University named after I.T. Trubilin”, Krasnodar, Russia
  • Plekhanov Russian University of Economics, Associate professor of Entrepreneurship and Logistics Department
  • Medical Laboratories Techniques Department, Al-Mustaqbal University College, Babylon, Hilla, Iraq
  • CAIC, DPU, Thailand
  • Department of Pharmacology, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, India
  • Medical physics department, Hilla university college, Babylon, Iraq
  • College of science for women University of Babylon, Iraq
Bibliografia
  • [1] Pak, N., Nahavandi, N. and Bagheri, B. Designing a multi-objective green supply chain network for an automotive company using an improved meta-heuristic algorithm. International Journal of Environmental Science and Technology, 1-24, 2021.
  • [2] Hasani, A., Mokhtari, H., & Fattahi, M. A multi-objective optimization approach for green and resilient supply chain network design: a real-life Case Study. Journal of Cleaner Production, 278, 123199, 2021.
  • [3] Pirdastan, M. An integrated Multi-Objective Optimization Model for Bank Green Supply Chain Network Under Uncertainty Using Fireworks and NSGA-II Algorithm. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(11), 6595-6623, 2021.
  • [4] Yozgat, S. and Erol, S. Sustainable Factors for Supply Chain Network Design Under Uncertainty: A Literature Review. Digitizing Production Systems, 585-597, 2022.
  • [5] Yadav, V. S., Singh, A. R., Gunasekaran, A., Raut, R. D. and Narkhede, B. E. A systematic literature review of the agro-food supply chain: Challenges, network design, and performance measurement perspectives. Sustainable Production and Consumption, 29, 685-704, 2022.
  • [6] Wang, F., Lai, X. and Shi, N. A multi-objective optimization for green supply chain network design. Decision Support Systems, 51(2), 262-269, 2011.
  • [7] Abdallah, T., Farhat, A., Diabat, A. and Kennedy, S. Green supply chains with carbon trading and environmental sourcing: Formulation and life cycle assessment Applied Mathematical Modelling 36, 42714285, 2012.
  • [8] Pishvaee, M.S. and Razmi, J. Environmental supply chain network design using multi objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 34333446, 2012.
  • [9] Fartaj, S. R., Kabir, G., Eghujovbo, V., Ali, S. M. and Paul, S. K. Modeling transportation disruptions in the supply chain of automotive parts manufacturing company. International Journal of Production Economics, 222, 107511, 2020.
  • [10] Zhang, C.T. and Liu, L.P. Research on coordination mechanism in three-level green supply chain under non-cooperative game. Applied Mathematical Modelling, 37(5), 3369-337, 2013.
  • [11] Hota, S. K., Sarkar, B. and Ghosh, S. K. Effects of unequal lot size and variable transportation in unreliable supply chain management. Mathematics, 8(3), 357, 2020.
  • [12] Ramezani, M., Kimiagari, A.M., Karimi, B. and Hejazi, T.H. Closed-loop supply chain network design under a fuzzy environment. Knowledge-Based Systems, 59 ,108-120, 2014.
  • [13] Garg, K., Kannan, D., Diabat, A. and Jha, P.C. A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design. Journal of Cleaner Production, 100, 297-314, 2015.
  • [14] Nurjanni, K.P., Carvalho, M.S. and Costa, L. Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model. International Journal of Production Economics, 183, 421-432, 2017.
  • [15] Sadeghi Rad, R. and Nahavandi, N. A novel multi-objective optimization model for Sadeghi Rad, integrated problem of green closed loop supply chain network design and quantity discount. Journal of Cleaner Production, 196, 1549-1565, 2018.
  • [16] Micheli, G. J., Cagno, E., Mustillo, G. and Trianni, A. Green supply chain management drivers, practices and performance: A comprehensive study on the moderators. Journal of Cleaner Production, 259, 121024, 2020.
  • [17] Lin, Y. and Zhang, W. An incentive model between a contractor and multiple subcontractors in a green supply chain based on robust optimization. Journal of Management Analytics, 7(4), 481-509, 2020.
  • [18] Mahjoob, M., Fazeli, S. S., Milanlouei, S., Mohammadzadeh, A. K. and Tavassoli, L. S. Green supply chain network design with emphasis on inventory decisions. arXiv preprint arXiv: 2104.05924, 2021.
  • [19] Qu, S., Yang, H. and Ji, Y. Low-carbon supply chain optimization considering warranty period and carbon emission reduction level under cap-and-trade regulation. Environment, Development and Sustainability, 1-28, 2021.
  • [20] Deb, K. Multi-objective optimization using evolutionary algorithm, 2001.
  • [21] Firouz, M. H. and Noradin, G., Wind energy uncertainties in multi-objective environmental/economic dispatch based on multi-objective evolutionary algorithm. UCT Journal of Research in Science, Engineering and Technology 3, 3, 8-15, 2015.
  • [22] Nekoonam, M., Hooman R. and Keyvan A., "Evaluation of urban transportion indicators with emphasis on sustainable development (Case study: Andishe New City)." Journal of Research in Science, Engineering and Technology 5, 4, 50-58, 2017.
  • [23] Hugo, A., Rutter, P., Pistikopoulos, S., Amorelli, A. and Zoia, G. Hydrogen infrastructure strategic planning using multi-objective optimization. International Journal of Hydrogen Energy, 30(15), 1523-1534, 2005.
  • [24] Bojarski, A. D., Laínez, J. M., Espuña, A. and Puigjaner, L. Incorporating environmental impacts and regulations in a holistic supply chains modeling: An LCA approach. Computers & Chemical Engineering, 33(10), 1747-1759, 2009.
  • [25] Ingrao, C., Scrucca, F., Matarazzo, A., Arcidiacono, C. and Zabaniotou, A. Freight transport in the context of industrial ecology and sustainability: evaluation of uni-and multi-modality scenarios via life cycle assessment. The International Journal of Life Cycle Assessment, 26(1), 127-142, 2021.
  • [26] Gunantara, N. A review of multi-objective optimization: Methods and its applications. Cogent Engineering, 5(1), 1502242, 2018.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fbc84438-9f64-4360-bd37-81120ecb031d
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