Optimizing Production Planning and Supplier Selection in Petrochemical Supply Chains
محورهای موضوعی : Operation ResearchReza Babazadeh 1 , Mohammad Khalili 2 , fatemeh dadmand 3
1 - Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Iran
2 - Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Ira
3 - Department of Management, Payame Noor University, Tehran, Iran
کلید واژه: production planning, supplier selection, Petrochemical industry,
چکیده مقاله :
In this research, the intricate world of the petrochemical supply chain was delved into, with a focus on the critical problem of production planning and supplier selection. The aim was to identify effective factors that contribute to the continuous supply chain process of petrochemical production. The study was conducted in two phases, first, the Analytic Hierarchy Process (AHP) method was employed to identify the best suppliers. In the second phase, an innovative model was developed to optimize production planning. The primary objective was to minimize the total cost associated with ordering, holding, and production. To ensure the practicality and relevance of the model, several constraints were incorporated. The results obtained from the AHP method revealed that Shiraz Petrochemical emerged as the optimal supplier for urea, Khorasan Petrochemical for ammonia, and Ilam Petrochemical for sulfur. Additionally, the optimization model provided valuable insights into the optimal production quantities, raw material procurement volumes, and raw material inventory levels for each period.
In this research, the intricate world of the petrochemical supply chain was delved into, with a focus on the critical problem of production planning and supplier selection. The aim was to identify effective factors that contribute to the continuous supply chain process of petrochemical production. The study was conducted in two phases, first, the Analytic Hierarchy Process (AHP) method was employed to identify the best suppliers. In the second phase, an innovative model was developed to optimize production planning. The primary objective was to minimize the total cost associated with ordering, holding, and production. To ensure the practicality and relevance of the model, several constraints were incorporated. The results obtained from the AHP method revealed that Shiraz Petrochemical emerged as the optimal supplier for urea, Khorasan Petrochemical for ammonia, and Ilam Petrochemical for sulfur. Additionally, the optimization model provided valuable insights into the optimal production quantities, raw material procurement volumes, and raw material inventory levels for each period.
Akkerman, R., Farahani, P., & Grunow, M. (2010). Quality, safety and sustainability in food distribution: A review of quantitative operations management approaches and challenges. Or Spectrum, 32(4), 863-904.
Ali, H., Zhang, J., Liu, S., & Shoaib, M. (2023). An integrated decision-making approach for global supplier selection and order allocation to create an environment-friendly supply chain. Kybernetes, 52(8), 2649-2671.
Al-Sharrah, G. K., Hankinson, G., & Elkamel, A. (2006). Decision-making for petrochemical planning using multiobjective and strategic tools. Chemical Engineering Research and Design, 84(11), 1019-1030.
Chauhan, S. S., & Kotecha, P. (2016, November). Single level production planning in petrochemical industries using Moth-flame optimization. In 2016 IEEE region 10 conference (TENCON) (pp. 263-266). IEEE.
Chen, Z., Hammad, A. W., Waller, S. T., & Haddad, A. N. (2023). Modelling supplier selection and material purchasing for the construction supply chain in a fuzzy scenario-based environment. Automation in Construction, 150, 104847.
Ding, G., Kaliyaperumal, K., & Wang, X. (2023). A New Mathematical Model for Economic Ordering with Preventive Maintenance and Reworking in a Supply Chain. Discrete Dynamics in Nature and Society, 2023.
Duckwen, A. C., Moreno, M. S., Borio, D. O., & Bandoni, J. A. (2017). Optimal production planning and crude oil blending in a conventional oilfield. In Computer Aided Chemical Engineering (Vol. 40, pp. 1309-1314). Elsevier.
Ghorbani, M., Bahrami, M., & Arabzad, S. M. (2012). An integrated model for supplier selection and order allocation; using Shannon entropy, SWOT and linear programming. Procedia-Social and Behavioral Sciences, 41, 521-527.
Goyal, S. K., & Deshmukh, S. G. (1992). Integrated procurement-production systems: a review. European journal of operational research, 62(1), 1-10.
Hamta, N., Ehsanifar, M., & Biglar, A. (2023). Optimization in Supply Chain Design of Assembled Products: A Case Study of HEPCO Company. Iranian Journal of Management Studies, 16(1), 61-77.
Ishizaka, A., Khan, S. A., Kheybari, S., & Zaman, S. I. (2023). Supplier selection in closed loop pharma supply chain: a novel BWM–GAIA framework. Annals of Operations Research, 324(1-2), 13-36.
Islam, S., Amin, S. H., & Wardley, L. J. (2021). Machine learning and optimization models for supplier selection and order allocation planning. International Journal of Production Economics, 242, 108315.
Kadambur, R., & Kotecha, P. (2016). Optimal production planning in a petrochemical industry using multiple levels. Computers & Industrial Engineering, 100, 133-143.
Karimi, S. K., Sadjadi, J., & Naini, S. G. J. (2022). A bi-objective production planning for a flexible supply chain solved using NSGA-II and MOPSO. International Journal of Industrial Engineering and Management, 13(1), 18-37.
Kim, J., Tak, K., & Moon, I. (2012). Optimization of procurement and production planning model in refinery processes considering corrosion effect. Industrial & engineering chemistry research, 51(30), 10191-10200.
Kwon, H., Do, T. N., & Kim, J. (2020). Comprehensive decision framework combining price prediction and production-planning models for strategic operation of a petrochemical industry. Industrial & Engineering Chemistry Research, 59(25), 11610-11620.
Kwon, H., Do, T. N., & Kim, J. (2022). Optimization-based integrated decision model for smart resource management in the petrochemical industry. Journal of Industrial and Engineering Chemistry, 113, 232-246.
Liu, Q., Huang, H., Zeng, Z., Chen, L., & Ming, J. (2023). An optimal supplier selection method for uncertain sustainable supply chains. European Journal of Industrial Engineering, 17(3), 431-459.
Meyr, H., Wagner, M., & Rohde, J. (2015). Structure of advanced planning systems. Supply chain management and advanced planning: Concepts, models, software, and case studies, 99-106.
Mina, H., Kannan, D., Gholami-Zanjani, S. M., & Biuki, M. (2021). Transition towards circular supplier selection in petrochemical industry: A hybrid approach to achieve sustainable development goals. Journal of Cleaner Production, 286, 125273.
Razmi, J., & Maghool, E. (2010). Multi-item supplier selection and lot-sizing planning under multiple price discounts using augmented ε-constraint and Tchebycheff method. International Journal of Advanced Manufacturing Technology, 49(1), 379.
Rezaei, A., Aghsami, A., & Rabbani, M. (2021). Supplier selection and order allocation model with disruption and environmental risks in centralized supply chain. International Journal of System Assurance Engineering and Management, 12(6), 1036-1072.
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98.
Shadkam, E. (2023). Investigating the multi-objective optimisation problem of supplier selection using the new COTOP hybrid method. International Journal of Business Performance and Supply Chain Modelling, 14(1), 79-105.
Sung, C., & Maravelias, C. T. (2007). An attainable region approach for production planning of multiproduct processes. AIChE Journal, 53(5), 1298-1315.
Tarei, P. K., Kumar, G., & Ramkumar, M. (2022). A Mean-Variance robust model to minimize operational risk and supply chain cost under aleatory uncertainty: A real-life case application in petroleum supply chain. Computers & Industrial Engineering, 166, 107949.
Ustun, O., & Demı, E. A. (2008). An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection. Omega, 36(4), 509-521.
Ustun, O., & Demı, E. A. (2008). An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection. Omega, 36(4), 509-521.
Varma, V. A., Reklaitis, G. V., Blau, G. E., & Pekny, J. F. (2007). Enterprise-wide modeling & optimization—An overview of emerging research challenges and opportunities. Computers & Chemical Engineering, 31(5-6), 692-711.
Wang, C. N., Tsai, H. T., Ho, T. P., Nguyen, V. T., & Huang, Y. F. (2020). Multi-criteria decision making (MCDM) model for supplier evaluation and selection for oil production projects in Vietnam. Processes, 8(2), 134.
Zhao, H., Ierapetritou, M. G., Shah, N. K., & Rong, G. (2017). Integrated model of refining and petrochemical plant for enterprise-wide optimization. Computers & Chemical Engineering, 97, 194-207.
