|تعداد مشاهده مقاله||111,491,745|
|تعداد دریافت فایل اصل مقاله||86,129,914|
A Multi-Objective Supply Chain Configuration for the Oil Industry under Uncertainty
|Advances in Industrial Engineering|
|دوره 56، شماره 1، فروردین 2022، صفحه 15-41 اصل مقاله (1.04 M)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22059/aie.2022.335295.1816|
|Majid Alimohammadi Ardekani*|
|Department of Industrial Engineering, Faculty of Engineering, Ardakan University, Ardakan, Iran.|
|In recent years, supply chains have become an attractive topic for managers and industrialists, and the life and death of organizations and businesses somehow depend on the activity of intertwined chains. On the other hand, in today's highly competitive environment, the high speed of change and evolution has increased the uncertainty and ambiguity of decisions, which makes it difficult to predict future conditions in supply chains. Therefore, reliable planning should be done in uncertain and ambiguous conditions for better and more accurate planning. One of the new and reliable approaches is the robust programming approach. In this study, transferring petroleum products from supply points to consumption areas is examined through a supply chain. Due to the uncertainty in the product demand, a mathematical model is used with two objectives including the reduction of shipping costs and the reduction of the number of loads. Due to the high volume of calculations and the problem data as well as the lack of ability to use exact solution methods, especially on a large scale, PSO and MOGA-II meta-heuristic algorithms are used to solve the proposed model. The results show that the model has the required efficiency in large dimensions and the proposed solution methods provide appropriate answers.|
|Oil Supply Chain؛ Robust Optimization؛ Multi-Objective Optimization؛ Meta-Heuristic Algorithm|
 Abdel-Basset, M., Mohamed, R., Sallam, K., & Elhoseny, M. (2020). A novel decision-making model for sustainable supply chain finance under uncertainty environment. Journal of Cleaner Production, 269, 122324.
 Abdolazimi, O., Esfandarani, M. S., Salehi, M., & Shishebori, D. (2020a). Robust design of a multi-objective closed-loop supply chain by integrating on-time delivery, cost, and environmental aspects, case study of a Tire Factory. Journal of Cleaner Production, 264, 121566.
 Abdolazimi, O., Salehi Esfandarani, M., Salehi, M., & Shishebori, D. (2020b). A Comparison of Solution Methods for the Multi-Objective Closed Loop Supply Chains. Advances in Industrial Engineering, 54(1), 75-98.
 Abdolazimi, O., Esfandarani, M. S., & Shishebori, D. (2021a). Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods. Neural Computing and Applications, 33(12), 6641-6656.
 Abdolazimi, O., Bahrami, F., Shishebori, D., & Ardakani, M. A. (2021b). A multi-objective closed-loop supply chain network design problem under parameter uncertainty: comparison of exact methods. Environment, Development and Sustainability, 1-35.
 Ambrosino, D., & Scutella, M. G. (2005). Distribution network design: New problems and related models. European journal of operational research, 165(3), 610-624.
 Asamoah, D., Agyei-Owusu, B., Andoh-Baidoo, F. K., & Ayaburi, E. (2021). Inter-organizational systems use and supply chain performance: Mediating role of supply chain management capabilities. International journal of information management, 58, 102195.
 Barros, A. I., Dekker, R., & Scholten, V. (1998). A two-level network for recycling sand: a case study. European journal of operational research, 110(2), 199-214.
 Abdolazimi, O., & Abraham, A. (2020c, December). Meta-heuristic Based Multi Objective Supply Chain Model for the Oil Industry in Conditions of Uncertainty. In International Conference on Innovations in Bio-Inspired Computing and Applications (pp. 141-153). Springer, Cham.
 Eriksson, P. E. (2010). Improving construction supply chain collaboration and performance: a lean construction pilot project. Supply Chain Management: An International Journal.
 Fahimnia, B., Davarzani, H., & Eshragh, A. (2018). Planning of complex supply chains: A performance comparison of three meta-heuristic algorithms. Computers & Operations Research, 89, 241-252.
 Farnsworth, M., Benkhelifa, E., Tiwari, A., Zhu, M., & Moniri, M. (2011). An efficient evolutionary multi-objective framework for MEMS design optimisation: validation, comparison and analysis. Memetic Computing, 3(3), 175-197.
 Fathollahi-Fard, A. M., Ahmadi, A., & Al-e-Hashem, S. M. (2020). Sustainable closed-loop supply chain network for an integrated water supply and wastewater collection system under uncertainty. Journal of Environmental Management, 275, 111277.
 Fleischmann, M., Bloemhof-Ruwaard, J. M., Dekker, R., Van der Laan, E., Van Nunen, J. A., & Van Wassenhove, L. N. (1997). Quantitative models for reverse logistics: A review. European journal of operational research, 103(1), 1-17.
 Galbraith, J. (1973). Designing complex organizations. Reading, Mass.
 Ghahremani-Nahr, J., Kian, R., & Sabet, E. (2019). A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm. Expert systems with applications, 116, 454-471.
 Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European journal of operational research, 240(3), 603-626.
 Hamdan, B., & Diabat, A. (2019). A two-stage multi-echelon stochastic blood supply chain problem. Computers & Operations Research, 101, 130-143.
 Hidalgo, K. J., Sierra-Garcia, I. N., Dellagnezze, B. M., & de Oliveira, V. M. (2020). Metagenomic insights into the mechanisms for biodegradation of polycyclic aromatic hydrocarbons in the oil supply chain. Frontiers in Microbiology, 11.
 Jayaraman, V., Guide Jr, V. D. R., & Srivastava, R. (1999). A closed-loop logistics model for remanufacturing. Journal of the operational research society, 50(5), 497-508.
 Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: a critical review. European Journal of Operational Research, 203(2), 283-293.
 Krikke, H. R., van Harten, A., & Schuur, P. C. (1999). Business case Oce: reverse logistic network re-design for copiers. Or-Spektrum, 21(3), 381-409.
 Kumar, R. S., Choudhary, A., Babu, S. A. I., Kumar, S. K., Goswami, A., & Tiwari, M. K. (2017). Designing multi-period supply chain network considering risk and emission: A multi-objective approach. Annals of Operations Research, 250(2), 427-461.
 Larimi, N. G., Yaghoubi, S., & Hosseini-Motlagh, S. M. (2019). Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories. Socio-Economic Planning Sciences, 68, 100697.
 Leung, S. C., Tsang, S. O., Ng, W. L., & Wu, Y. (2007). A robust optimization model for multi-site production planning problem in an uncertain environment. European journal of operational research, 181(1), 224-238.
 Liu, B., Wang, L., & Jin, Y. H. (2007). An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37(1), 18-27.
 Min, H., Ko, C. S., & Ko, H. J. (2006). The spatial and temporal consolidation of returned products in a closed-loop supply chain network. Computers & Industrial Engineering, 51(2), 309-320.
 Mohammed, M. K., Umer, U., & Al-Ahmari, A. (2017). Optimization of laser micro milling of alumina ceramic using radial basis functions and MOGA-II. The International Journal of Advanced Manufacturing Technology, 91(5).
 Mondal, A., & Roy, S. K. (2021). Multi-objective sustainable opened-and closed-loop supply chain under mixed uncertainty during COVID-19 pandemic situation. Computers & Industrial Engineering, 159, 107453.
 Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281.
 Obreque, C., Donoso, M., Gutiérrez, G., & Marianov, V. (2010). A branch and cut algorithm for the hierarchical network design problem. European Journal of Operational Research, 200(1), 28-35.
 Optimization, M. I. (2014). Mode Frontier Version 4.0. User Manual, Esteco, SPA.
 Peng, H., Shen, N., Liao, H., Xue, H., & Wang, Q. (2020). Uncertainty factors, methods, and solutions of closed-loop supply chain—A review for current situation and future prospects. Journal of Cleaner Production, 254, 120032.
 Piya, S., Shamsuzzoha, A., Khadem, M., & Al-Hinai, N. (2020). Identification of critical factors and their interrelationships to design agile supply chain: special focus to oil and gas industries. Global Journal of Flexible Systems Management, 21(3), 263-281.
 Reiner, G., & Trcka, M. (2004). Customized supply chain design: Problems and alternatives for a production company in the food industry. A simulation based analysis. International Journal of Production Economics, 89(2), 217-229.
 Sakib, N., Hossain, N. U. I., Nur, F., Talluri, S., Jaradat, R., & Lawrence, J. M. (2021). An assessment of probabilistic disaster in the oil and gas supply chain leveraging Bayesian belief network. International Journal of Production Economics, 108107.
 Shoja, A., Molla-Alizadeh-Zavardehi, S., & Niroomand, S. (2019). Adaptive meta-heuristic algorithms for flexible supply chain network design problem with different delivery modes. Computers & Industrial Engineering, 138, 106107.
 Suler, J. (2009). The psychotherapeutics of online photosharing. International Journal of Applied Psychoanalytic Studies, 6(4), 339-344.
 Stanworth, S. J., New, H. V., Apelseth, T. O., Brunskill, S., Cardigan, R., Doree, C., ... & Thachil, J. (2020). Effects of the COVID-19 pandemic on supply and use of blood for transfusion. The Lancet Haematology.
 Tang, C. S. (2006). Perspectives in supply chain risk management. International journal of production economics, 103(2), 451-488.
 Tsao, Y. C., Thanh, V. V., Lu, J. C., & Yu, V. (2018). Designing sustainable supply chain networks under uncertain environments: Fuzzy multi-objective programming. Journal of Cleaner Production, 174, 1550-1565.
 Zhang, S., Lei, Q., Wu, L., Wang, Y., Zheng, L., & Chen, X. (2021). Supply chain design and integration for the Co-Processing of bio-oil and vacuum gas oil in a refinery. Energy, 122912.
 Zhang, J., Yalcin, M. G., & Hales, D. N. (2021). Elements of paradoxes in supply chain management literature: a systematic literature review. International Journal of Production Economics, 232, 107928.
 Zheng, M., Li, W., Liu, Y., & Liu, X. (2020). A Lagrangian heuristic algorithm for sustainable supply chain network considering CO2 emission. Journal of Cleaner Production, 270, 122409.
 Zhou, X., Zhang, H., Xin, S., Yan, Y., Long, Y., Yuan, M., & Liang, Y. (2020). Future scenario of China’s downstream oil supply chain: Low carbon-oriented optimization for the design of planned multi-product pipelines. Journal of Cleaner Production, 244, 118866.
 Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-report, 103.
 Arabi, M., & Gholamian, M. R. (2021). Sustainable Supply Chain Network Design with Price-Based Demand Considering Sound and Dust Pollutions: A Case Study. Advances in Industrial Engineering, 55(3), 285-306.
 Salehi, F., Allahyari Emamzadeh, Y., Mirzapour, A. E., Hashem, S. M. J., & Shafiei Aghdam, R. (2021). An L-Shaped Method to Solve a Stochastic Blood Supply Chain Network Design Problem in a Natural Disaster. Advances in Industrial Engineering, 55(1), 47-68.
 Seifbarghy, M. S., Soleimani, M., & Jabbari, M. (2020). Comparing Multi-Objective Meta-Heuristics for Multi-Commodity Supply Chain Design Problem with Partial Coverage. Advances in Industrial Engineering, 54(4), 365-379.
 Chima, C. M. (2007). Supply-chain management issues in the oil and gas industry. Journal of Business & Economics Research (JBER), 5(6).
 Aslam, J., Saleem, A., Khan, N. T., & Kim, Y. B. (2021). Factors influencing blockchain adoption in supply chain management practices: A study based on the oil industry. Journal of Innovation & Knowledge, 6(2), 124-134.
 ALNAQBI, A., DWEIRI, F., & CHAABANE, A. (2022). Impact of Horizontal Mergers on Supply Chain Performance: The Case of the Upstream Oil and Gas Industry. Computers & Chemical Engineering, 107659.
 Ara, R. A., Paardenkooper, K., & van Duin, R. (2021). A new blockchain system design to improve the supply chain of engineering, procurement and construction (EPC) companies–a case study in the oil and gas sector. Journal of Engineering, Design and Technology.
 Sahebishahemabadi, H. (2013). Strategic and Tactical Crude Oil Supply Chain: Mathematical Programming Models.
 Lima, C., Relvas, S., & Barbosa-Póvoa, A. P. F. (2016). Downstream oil supply chain management: A critical review and future directions. Computers & Chemical Engineering, 92, 78-92.
 Fernandes, L. J., Relvas, S., & Barbosa-Póvoa, A. P. (2014). Collaborative design and tactical planning of downstream petroleum supply chains. Industrial & Engineering Chemistry Research, 53(44), 17155-17181.
 Wisner, J. D. (2003). A structural equation model of supply chain management strategies and firm performance. Journal of Business logistics, 24(1), 1-26.
 Ernst, D., & Steinhubl, A. M. (1997). Alliances in upstream oil and gas. McKinsey Quarterly, 144-155.
 Ramdas, K., & Spekman, R. E. (2000). Chain or shackles: understanding what drives supply-chain performance. Interfaces, 30(4), 3-21.
 Zhou, Y. C., Wang, X. N., Liu, X. P., Xue, L., Liang, S., & Sun, C. H. (2010, July). Enabling integrated information framework as cloud services for chemical and petroleum industry. In 2010 6th World Congress on Services (pp. 1-7). IEEE.
 Yusuf, Y. Y., Gunasekaran, A., Musa, A., Dauda, M., El-Berishy, N. M., & Cang, S. (2014). A relational study of supply chain agility, competitiveness and business performance in the oil and gas industry. International Journal of Production Economics, 147, 531-543.