Mathematical modelling of aggregate production planning in iron and steel industry: Green supply chain management approach

Document Type : Research Paper

Authors

1 Faculty of Humanities, Management Department, Meybod University, Meybod, Iran

2 Faculty of Economics, Management and Accounting, University of Yazd, Yazd, Iran

3 Faculty of Management, University of Science and Arts, Yazd, Iran

4 Faculty of Engineering, Industrial Engineering Department, Yazd University, Yazd, Iran

Abstract

Developing an aggregate production planning, as one of the most important manufacture tasks, can provide an efficient planning to optimize the companies’ objectives such as minimizing costs and maximizing profits. Also, community’s competitive pressures cause the need for considering green principles in production planning in order to balance environmental and economic performances. Hence, a multi-period, multi-product, multi-supplier, and multi-site aggregate production planning model is proposed to formulate a mathematical model of maximizing profit in green supply chain. Integer quadratic programming is used to solve the problem. Carbon dioxide emission from production and transportation modes are considered as green principle.  The feasibility and validity of the formulated model was tested using data from iron and steel industry as well as a sensitivity analysis on profit function. The results demonstrate the optimal amount of productions in order to maximize profit as well as developing green supply chain. Also, sensitivity analysis shows that profit objective fell steadily due to increase in total CO2 emissions from transportation and production processes. Consequently, some useful managerial insights were suggested regarding the consideration of green practices in aggregate production planning. 
 
 

Keywords

Main Subjects

References

Akbarzadeh Sarabi, M., Ghaffari, M., & Torabi, S. A. (2022). A multi-objective Sustainable supply chain network design problem for perishable foods. Journal of Industrial and Systems Engineering.
Amin, S. H., & Zhang, G. (2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems with Applications, 39(8), 6782-6791.
Amirian, S., Amiri, M., & Taghavifard, M. T. (2022). Sustainable and reliable closed-loop supply chain network design: Normalized Normal Constraint (NNC) method application. Journal of Industrial and Systems Engineering.
Ardakani, D. A., Soltanmohammadi, A., and Seuring, S. (2022). The impact of customer and supplier collaboration on green supply chain performance. Benchmarking: An International Journal(ahead-of-print).
Asrawi, I., Saleh, Y., & Othman, M. (2017). Integrating drivers’ differences in optimizing green supply chain management at tactical and operational levels. Computers & Industrial Engineering, 112, 122-134.
Cárdenas-Barrón, L. E., González-Velarde, J. L., & Treviño-Garza, G. (2015). A new approach to solve the multi-product multi-period inventory lot sizing with supplier selection problem. Computers & Operations Research, 64, 225-232.
Chakrabortty, R. K., Hasin, M. A. A., Sarker, R. A., & Essam, D. L. (2015). A possibilistic environment based particle swarm optimization for aggregate production planning. Computers & Industrial Engineering, 88, 366-377.
Entezaminia, A., Heydari, M., & Rahmani, D. (2016). A multi-objective model for multi-product multi-site aggregate production planning in a green supply chain: Considering collection and recycling centers. Journal of Manufacturing Systems, 40, 63-75.
Fahimnia, B., Sarkis, J., & Eshragh, A. (2015). A tradeoff model for green supply chain planning: A leanness-versus-greenness analysis. Omega, 54, 173-190.
Ghaithan, A. M., Attia, A., & Duffuaa, S. O. (2017). Multi-objective optimization model for a downstream oil and gas supply chain. Applied Mathematical Modelling, 52, 689-708.
Homayouni, Z., Pishvaee, M. S., Jahani, H., & Ivanov, D. (2021). A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty. Annals of Operations Research, 1-41.
IEA. (2018). CO₂ emissions from fuel combustion overview. Retrieved from http://wds.iea.org/wds/pdf/WorldCO2_documentation.pdf.
Jamalnia, A., Yang, J.-B., Xu, D.-L., & Feili, A. (2017). Novel decision model based on mixed chase and level strategy for aggregate production planning under uncertainty: Case study in beverage industry. Computers & Industrial Engineering, 114, 54-68.
Jamshidpour Poshtahani, S., & Pasandideh, S. H. R. (2020). Optimizing a bi-objective vendor-managed inventory of multi-product EPQ model for a green supply chain with stochastic constraints. Journal of Industrial and Systems Engineering, 13(1), 1-34.
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.
Kisomi, M. S., Solimanpur, M., & Doniavi, A. (2016). An integrated supply chain configuration model and procurement management under uncertainty: a set-based robust optimization methodology. Applied Mathematical Modelling, 40(17-18), 7928-7947.
Liu, J., An, R., Xiao, R., Yang, Y., Wang, G., & Wang, Q. (2017). Implications from substance flow analysis, supply chain and supplier’risk evaluation in iron and steel industry in Mainland China. Resources Policy, 51, 272-282.
Liu, S., & Papageorgiou, L. G. (2018). Fair profit distribution in multi-echelon supply chains via transfer prices. Omega, 80, 77-94.
Liu, L.-F., & Yang, X. (2021). Multi-objective aggregate production planning for multiple products: A local search-based genetic algorithm optimization approach. International Journal of Computational Intelligence Systems, 14(1), 1-16.
Mirzapour Al-e-Hashem, S., Baboli, A., & Sazvar, Z. (2013). A stochastic aggregate production planning model in a green supply chain: Considering flexible lead times, nonlinear purchase and shortage cost functions. European Journal of Operational Research, 230(1), 26-41.
Modarres, M., & Izadpanahi, E. (2016). Aggregate production planning by focusing on energy saving: A robust optimization approach. Journal of Cleaner Production, 133, 1074-1085.
Moradinasab, N., Amin-Naseri, M., Behbahani, T. J., & Jafarzadeh, H. (2018). Competition and cooperation between supply chains in multi-objective petroleum green supply chain: A game theoretic approach. Journal of Cleaner Production, 170, 818-841.
Mota, B., Gomes, M. I., Carvalho, A., & Barbosa-Povoa, A. P. (2018). Sustainable supply chains: An integrated modeling approach under uncertainty. Omega, 77, 32-57.
Nurjanni, K. P., Carvalho, M. S., & 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.
Özceylan, E., Demirel, N., Çetinkaya, C., & Demirel, E. (2017). A closed-loop supply chain network design for automotive industry in Turkey. Computers & Industrial Engineering, 113, 727-745.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637-649.
Rad, R. S., & Nahavandi, N. (2018). A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. Journal of Cleaner Production.
Rahmani, D., Ramezanian, R., Fattahi, P., & Heydari, M. (2013). A robust optimization model for multi-product two-stage capacitated production planning under uncertainty. Applied Mathematical Modelling, 37(20-21), 8957-8971.
Ramezani, M., Kimiagari, A. M., Karimi, B., and Hejazi, T. H. (2014). Closed-loop supply chain network design under a fuzzy environment. Knowledge-Based Systems, 59, 108-120.
Ramezanian, R., Rahmani, D., & Barzinpour, F. (2012). An aggregate production planning model for two phase production systems: Solving with genetic algorithm and tabu search. Expert Systems with Applications, 39(1), 1256-1263.
Shabani, A., Saen, R. F., & Torabipour, S. M. R. (2014). A new data envelopment analysis (DEA) model to select eco-efficient technologies in the presence of undesirable outputs. Clean Technologies and Environmental Policy, 16(3), 513-525.
Shi, J., Zhang, G., & Sha, J. (2011). Optimal production and pricing policy for a closed loop system. Resources, Conservation and Recycling, 55(6), 639-647.
Soleimani, H., Govindan, K., Saghafi, H., & Jafari, H. (2017). Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Computers & Industrial Engineering, 109, 191-203.
Tognetti, A., Grosse-Ruyken, P. T., & Wagner, S. M. (2015). Green supply chain network optimization and the trade-off between environmental and economic objectives. International Journal of Production Economics, 170, 385-392.
Tseng, M.-L. (2011). Green supply chain management with linguistic preferences and incomplete information. Applied soft computing, 11(8), 4894-4903.
Validi, S., Bhattacharya, A., & Byrne, P. J. (2015). A solution method for a two-layer sustainable supply chain distribution model. Computers & Operations Research, 54, 204-217.
Varsei, M., & Polyakovskiy, S. (2017). Sustainable supply chain network design: A case of the wine industry in Australia. Omega, 66, 236-247.
Varsei, M., Soosay, C., Fahimnia, B., & Sarkis, J. (2014). Framing sustainability performance of supply chains with multidimensional indicators. Supply Chain Management: An International Journal, 19(3), 242-257.
Yaghin, R. G. (2018). Integrated multi-site aggregate production-pricing planning in a two-echelon supply chain with multiple demand classes. Applied Mathematical Modelling, 53, 276-295.
Yaghin, R. G., Torabi, S., & Ghomi, S. F. (2012). Integrated markdown pricing and aggregate production planning in a two echelon supply chain: A hybrid fuzzy multiple objective approach. Applied Mathematical Modelling, 36(12), 6011-6030.
Yang, H., Tao, W., Liu, Y., Qiu, M., Liu, J., Jiang, K., ejunJiang, K., Yi, Y., Xiao, &, Sh., Tao (2019).The contribution of the Beijing, Tianjinand Hebeiregion's iron and steel industry to local air pollution in winter. Environmental Pollution, 245, 1095-1106.
Zhao, R., Liu, Y., Zhang, N., & Huang, T. (2017). An optimization model for green supply chain management by using a big data analytic approach. Journal of Cleaner Production, 142, 1085-1097.

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History

  • Receive Date: 13 June 2022
  • Revise Date: 02 August 2022
  • Accept Date: 17 August 2022

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