A Multi-Objective Supply Chain Configuration for the Oil Industry under Uncertainty

       [1]        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.

       [2]        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.

       [3]        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.

       [4]        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.

       [5]        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.

       [6]        Ambrosino, D., & Scutella, M. G. (2005). Distribution network design: New problems and related models. European journal of operational research, 165(3), 610-624.

       [7]        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.

       [8]        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.

       [9]        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.

     [10]      Eriksson, P. E. (2010). Improving construction supply chain collaboration and performance: a lean construction pilot project. Supply Chain Management: An International Journal.

     [11]      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.

     [12]      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.

     [13]      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.

     [14]      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.

     [15]      Galbraith, J. (1973). Designing complex organizations. Reading, Mass.

     [16]      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.

     [17]      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.

     [18]      Hamdan, B., & Diabat, A. (2019). A two-stage multi-echelon stochastic blood supply chain problem. Computers & Operations Research, 101, 130-143.

     [19]      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.

     [20]      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.

     [21]      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.

     [22]      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.

     [23]      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.

     [24]      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.

     [25]      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.

     [26]      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.

     [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.

     [28]      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).

     [29]      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.

     [30]      Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281.

     [31]      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.

     [32]      Optimization, M. I. (2014). Mode Frontier Version 4.0. User Manual, Esteco, SPA.

     [33]      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.

     [34]      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.

     [35]      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.

     [36]      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.

     [37]      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.

     [38]      Suler, J. (2009). The psychotherapeutics of online photosharing. International Journal of Applied Psychoanalytic Studies, 6(4), 339-344.

     [39]      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.

     [40]      Tang, C. S. (2006). Perspectives in supply chain risk management. International journal of production economics, 103(2), 451-488.

     [41]      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.

     [42]      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.

     [43]      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.

     [44]      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.

     [45]      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.

     [46]      Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-report, 103.

     [47]      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.

     [48]      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.

     [49]      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.

     [50]      Chima, C. M. (2007). Supply-chain management issues in the oil and gas industry. Journal of Business & Economics Research (JBER), 5(6).

     [51]      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.

     [52]      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.

     [53]      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.

     [54]      Sahebishahemabadi, H. (2013). Strategic and Tactical Crude Oil Supply Chain: Mathematical Programming Models.

     [55]      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.

     [56]      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.

     [57]      Wisner, J. D. (2003). A structural equation model of supply chain management strategies and firm performance. Journal of Business logistics, 24(1), 1-26.

     [58]      Ernst, D., & Steinhubl, A. M. (1997). Alliances in upstream oil and gas. McKinsey Quarterly, 144-155.

     [59]      Ramdas, K., & Spekman, R. E. (2000). Chain or shackles: understanding what drives supply-chain performance. Interfaces, 30(4), 3-21.

     [60]      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.

     [61]      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.