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Yousefi-Babadi, Abolghasem, Soleimani, Niloofar, Shishebori, Davood. (1400). Capacitated Sustainable Resilient Closed-Loop Supply Chain Network Design: A Heuristics Algorithm. , 55(4), 447-479. doi: 10.22059/aie.2022.333393.1813
Abolghasem Yousefi-Babadi; Niloofar Soleimani; Davood Shishebori. "Capacitated Sustainable Resilient Closed-Loop Supply Chain Network Design: A Heuristics Algorithm". , 55, 4, 1400, 447-479. doi: 10.22059/aie.2022.333393.1813
Yousefi-Babadi, Abolghasem, Soleimani, Niloofar, Shishebori, Davood. (1400). 'Capacitated Sustainable Resilient Closed-Loop Supply Chain Network Design: A Heuristics Algorithm', , 55(4), pp. 447-479. doi: 10.22059/aie.2022.333393.1813
Yousefi-Babadi, Abolghasem, Soleimani, Niloofar, Shishebori, Davood. Capacitated Sustainable Resilient Closed-Loop Supply Chain Network Design: A Heuristics Algorithm. , 1400; 55(4): 447-479. doi: 10.22059/aie.2022.333393.1813

Capacitated Sustainable Resilient Closed-Loop Supply Chain Network Design: A Heuristics Algorithm

Advances in Industrial Engineering
دوره 55، شماره 4، دی 2021، صفحه 447-479    اصل مقاله (1.72 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/aie.2022.333393.1813
نویسندگان
Abolghasem Yousefi-Babadi1؛ Niloofar Soleimani2؛ Davood Shishebori* 2
1School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
2Department of Industrial Engineering, Yazd University, Yazd, Iran
چکیده
Consideration of environmental and social issues in addition to economic ones is a critical strategy that companies pay special attention to designing their supply chain. A resilient system prevents organizations from being surprised by catastrophic disruptions and critical conditions and eliminates high unwanted costs. In this study, a mixed-integer mathematical programming model is proposed to design a sustainable and resilient closed-loop supply chain network. Since suppliers are the most important external players, the slightest probability of disruptions can have a significant impact on chain performance. Accordingly, applying efficient strategies can be very helpful for coping with them. Also, because of the uncertain nature of some input parameters, the P-robust optimization method has been used to tackle them. An efficient algorithm has been carried out beside a heuristic method based on the strategic variables relaxation to solve the model. A case study of a lighting projectors industry has been conducted to evaluate the efficiency of the proposed approach. Finally, sensitivity analysis is performed on critical parameters of the problem. By solving the example, it is seen that 3 primary suppliers and 3 backups are selection, and 3 production centers, 2 collection centers and 1 repair, recycling and disposal centers have been established. The value of the economic objective function is equal to 565.857552 monetary units (MU). The CL-SCN environmental score is 658.07, while it is 608.93 in the social dimension. Eventually, the value of the final multi-objective function is equal to 0.658.
کلیدواژه‌ها
Closed-loop supply chain network؛ Sustainability؛ Resilience؛ Heuristic algorithm؛ Lighting projectors industry
مراجع
 

                   [1]        Chalmardi, M. K., & Camacho-Vallejo, J. F. 2019.  A bi-level programming model for sustainable supply chain network design that considers incentives for using cleaner technologies. Journal of cleaner production, 213, 1035-1050.

                   [2]        Chalmardi MK, Camacho-Vallejo JF. A bi-level programming model for sustainable supply chain network design that considers incentives for using cleaner technologies. Journal of Cleaner Production. 2019 Mar 10;213:1035-50.

                   [3]        Ponomarov SY, Holcomb MC. Understanding the concept of supply chain resilience. The international journal of logistics management. 2009 May 22.

                   [4]        Kristianto Y, Gunasekaran A, Helo P, Hao Y. A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path. Expert systems with applications. 2014 Jan 1;41(1):39-49. 

                   [5]        Shishebori D, Babadi AY. Designing a capacitated multi-configuration logistics network under disturbances and parameter uncertainty: a real-world case of a drug supply chain. Journal of Industrial Engineering International. 2018 Mar;14(1):65-85.

                   [6]        Goodarzian F, Shishebori D, Bahrami F, Abraham A, Appolloni A. Hybrid meta-heuristic algorithms for optimising a sustainable agricultural supply chain network considering CO2 emissions and water consumption. International Journal of Systems Science: Operations & Logistics. 2021 Dec 16:1-30.

                   [7]        Peck H. Resilience: surviving the unthinkable. Logistics Manager. 2004 Mar:16-8.

                   [8]        Fleischmann M, Beullens P, Bloemhof‐Ruwaard JM, Van Wassenhove LN. The impact of product recovery on logistics network design. Production and operations management. 2001 Jun;10(2):156-73.

                   [9]        Vahdani B, Tavakkoli-Moghaddam R, Zandieh M, Razmi J. Vehicle routing scheduling using an enhanced hybrid optimization approach. Journal of Intelligent Manufacturing. 2012 Jun;23(3):759-74.

                 [10]      Pishvaee MS, Rabbani M, Torabi SA. A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied mathematical modelling. 2011 Feb 1;35(2):637-49.

                 [11]      Amin SH, Zhang G. An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems with Applications. 2012 Jun 15;39(8):6782-91.

                 [12]      Baghalian A, Rezapour S, Farahani RZ. Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European journal of operational research. 2013 May 16;227(1):199-215.

                 [13]      Jabbarzadeh A, Fahimnia B, Sheu JB, Moghadam HS. Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transportation Research Part B: Methodological. 2016 Dec 1;94:121-49.

                 [14]      Fazli-Khalaf M, Mirzazadeh A, Pishvaee MS. A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network. Human and ecological risk assessment: an international journal. 2017 Nov 17;23(8):2119-49.

                 [15]      Gholami F, Paydar MM, Hajiaghaei-Keshteli M, Cheraghalipour A. A multi-objective robust supply chain design considering reliability. Journal of Industrial and Production Engineering. 2019 Aug 18;36(6):385-400.

                 [16]      Islam M, Azeem A, Jabir M, Paul A, Paul SK. An inventory model for a three-stage supply chain with random capacities considering disruptions and supplier reliability. Annals of Operations Research. 2020 May 15:1-26.

                 [17]      Tolooie A, Maity M, Sinha AK. A two-stage stochastic mixed-integer program for reliable supply chain network design under uncertain disruptions and demand. Computers & Industrial Engineering. 2020 Oct 1;148:106722.

                 [18]      Yan S, Ji X. Supply chain network design under the risk of uncertain disruptions. International Journal of Production Research. 2020 Mar 18;58(6):1724-40.

                 [19]      Zhu X, Cao Y. The optimal recovery-fund based strategy for uncertain supply chain disruptions: A risk-averse two-stage stochastic programming approach. Transportation Research Part E: Logistics and Transportation Review. 2021 Aug 1;152:102387.

                 [20]      Pishvaee MS, Razmi J, Torabi SA. Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems. 2012 Nov 1;206:1-20.

                 [21]      You F, Tao L, Graziano DJ, Snyder SW. Optimal design of sustainable cellulosic biofuel supply chains: multiobjective optimization coupled with life cycle assessment and input–output analysis. AIChE Journal. 2012 Apr;58(4):1157-80.

                 [22]      Zhang Q, Shah N, Wassick J, Helling R, Van Egerschot P. Sustainable supply chain optimisation: An industrial case study. Computers & Industrial Engineering. 2014 Aug 1;74:68-83.

                 [23]      Mota B, Gomes MI, Carvalho A, Barbosa-Povoa AP. Towards supply chain sustainability: economic, environmental and social design and planning. Journal of cleaner production. 2015 Oct 15;105:14-27.

                 [24]      Shen J. An uncertain sustainable supply chain network. Applied Mathematics and Computation. 2020 Aug 1;378:125213.

                 [25]      Zarei J, Amin-Naseri MR, Khorasani AH, Kashan AH. A sustainable multi-objective framework for designing and planning the supply chain of natural gas components. Journal of Cleaner Production. 2020 Jun 20;259:120649.

                 [26]      Durmaz YG, Bilgen B. Multi-objective optimization of sustainable biomass supply chain network design. Applied Energy. 2020 Aug 15;272:115259.

                 [27]      Mastrocinque E, Ramírez FJ, Honrubia-Escribano A, Pham DT. An AHP-based multi-criteria model for sustainable supply chain development in the renewable energy sector. Expert Systems with Applications. 2020 Jul 15;150:113321.

                 [28]      Jia F, Zhang T, Chen L. Sustainable supply chain Finance: Towards a research agenda. Journal of cleaner production. 2020 Jan 10;243:118680.

                 [29]      Kalantari F, Hosseininezhad SJ. A Multi-objective Cross Entropy-based algorithm for sustainable global food supply chain with risk considerations: A case study. Computers & Industrial Engineering. 2022 Feb 1;164:107766.

                 [30]      Tseng M.L., Bui T.D., Lim M.K., Fujii M., Mishra U. Assessing data-driven sustainable supply chain management indicators for the textile industry under industrial disruption and ambidexterity. International Journal of Production Economics. 2022 Mar 1;245:108401.

                 [31]      Yazdanparast R, Jolai F, Pishvaee MS, Keramati A. A resilient drop-in biofuel supply chain integrated with existing petroleum infrastructure: Toward more sustainable transport fuel solutions. Renewable Energy. 2022 Jan 1;184:799-819.

                 [32]      Kamalahmadi M, Parast MM. A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics. 2016 Jan 1;171:116-33.

                 [33]      Poudel S, Marufuzzaman M, Quddus MA, Chowdhury S, Bian L, Smith B. Designing a reliable and congested multi-modal facility location problem for biofuel supply chain network. Energies. 2018 Jul;11(7):1682.

                 [34]      Azad N, Hassini E. A benders decomposition method for designing reliable supply chain networks accounting for multimitigation strategies and demand losses. Transportation Science. 2019 Sep;53(5):1287-312.

                 [35]      Haghjoo N, Tavakkoli-Moghaddam R, Shahmoradi-Moghadam H, Rahimi Y. Reliable blood supply chain network design with facility disruption: A real-world application. Engineering Applications of Artificial Intelligence. 2020 Apr 1;90:103493.

                 [36]      Haghjoo N, Tavakkoli-Moghaddam R, Shahmoradi-Moghadam H, Rahimi Y. Reliable blood supply chain network design with facility disruption: A real-world application. Engineering Applications of Artificial Intelligence. 2020 Apr 1;90:103493.

                 [37]      Sahebjamnia N, Fathollahi-Fard AM, Hajiaghaei-Keshteli M. Sustainable tire closed-loop supply chain network design: Hybrid metaheuristic algorithms for large-scale networks. Journal of cleaner production. 2018 Sep 20;196:273-96.

                 [38]      Zahiri B, Zhuang J, Mohammadi M. Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study. Transportation Research Part E: Logistics and Transportation Review. 2017 Jul 1;103:109-42.

                 [39]      Carvalho H, Barroso AP, Machado VH, Azevedo S, Cruz-Machado V. Supply chain redesign for resilience using simulation. Computers & Industrial Engineering. 2012 Feb 1;62(1):329-41.

                 [40]      Jabbarzadeh A, Fahimnia B, Sabouhi F. Resilient and sustainable supply chain design: sustainability analysis under disruption risks. International Journal of Production Research. 2018 Sep 2;56(17):5945-68.

                 [41]      Mousavi Ahranjani P, Ghaderi SF, Azadeh A, Babazadeh R. Robust design of a sustainable and resilient bioethanol supply chain under operational and disruption risks. Clean Technologies and Environmental Policy. 2020 Jan;22(1):119-51.

                 [42]      Mousavi Ahranjani P, Ghaderi SF, Azadeh A, Babazadeh R. Robust design of a sustainable and resilient bioethanol supply chain under operational and disruption risks. Clean Technologies and Environmental Policy. 2020 Jan;22(1):119-51.

                 [43]      Sarkar B, Mridha B, Pareek S, Sarkar M, Thangavelu L. A flexible biofuel and bioenergy production system with transportation disruption under a sustainable supply chain network. Journal of Cleaner Production. 2021 Oct 1;317:128079.

                 [44]      Taleizadeh AA, Ahmadzadeh K, Sarker BR, Ghavamifar A. Designing an optimal sustainable supply chain system considering pricing decisions and resilience factors. Journal of Cleaner Production. 2022 Jan 15;332:129895.

                 [45]      Snyder LV, Daskin MS. Stochastic p-robust location problems. Iie Transactions. 2006 Nov 1;38(11):971-85.

                 [46]      Torabi SA, Hassini E. An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy sets and systems. 2008 Jan 16;159(2):193-214.

                 [47]      Shishebori D, Karimi-Nasab M, Snyder LV. A two-phase heuristic algorithm for designing reliable capacitated logistics networks under disruptions. European Journal of Industrial Engineering. 2017;11(4):425-68.

                 [48]      Shishebori D, Yousefi Babadi A, Noormohammadzadeh Z. A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem. Scientia Iranica. 2018 Jun 1;25(3):1750-67.

 
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