A Robust Green multi-Channel Sustainable Supply Chain based on RFID technology with considering pricing strategy and subsidizing policies

Journal of Industrial Engineering International

دوره 18، شماره 4، اسفند 2022، صفحه 43-78   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.30495/jiei.2023.1958875.1229

نویسندگان

Elham Kouchaki Tajani¹؛ Armin Ghane Kanafi^{* 2}؛ Maryam Daneshmand-Mehr¹؛ Ali-Asghar Hosseinzadeh²

¹Department of Industrial Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran

²Department of Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, Iran

چکیده

In recent years, increasing carbon emissions and relatively unfavorable climate change have led to paying attention to the concepts of sustainability as well as the imposition of strict government regulations on manufacturers and service providers. This has caused all parts of society, containing consumers, governments, and companies, to pay greater attention to low-carbon manufacturing in the supply chain (SC). To this end, this paper has designed a sustainable, multi-echelon, multi-product, multi-period, and multi-objective closed-loop supply chain (CLSC) network, with different distribution and collection channels and Radio Frequency Identification (RFID) technology, in addition, in order to produce low-carbon products, low-carbon products and subsidizing policies, and also deals with pricing strategy. This model, at the same time, maximizes profits and the social responsibility of the SC network, while it minimizes the overall delay in delivery time and environmental pollution. To cope with the parameters' uncertainty, a Robust scenario-based Stochastic programming (RSSP) approach has been used, and to solve and validate, the small-size model the Augmented Epsilon Constraint (AEC) method, and to solve large-sized ones, the third edition of the Non-dominated Sorting Genetic Algorithm (NSGA-III) and Multi-Objective Grey Wolf Optimizer Algorithm (MOGWO) are used. According to the computational results, the suggested model can provide efficient decisions and the MOGWO algorithm yields 14.5% improvement in execution time compared to the NSGA-III algorithm. Also suggested model can be a great tool for managers and professionals with a wide range of strategic applications.

کلیدواژه‌ها

Government Subsidy؛ Low-Carbon؛ Multi-Channel Closed-Loop Supply Chain؛ Pricing؛ Radio Frequency Identification System

مراجع

[1] Abdolazimi, O., Bahrami, F., Shishebori, D., & Ardakani, M. A. (2021). A multi-objective closed-loop supply chain network design problem under parameter uncertainty: comparison of exact methods. Environment, Development and Sustainability, 24(9), 10768-10802. doi:10.1007/s10668-021-01883-2

[2] Abdolazimi, O., Salehi Esfandarani, M., Salehi, M., & Shishebori, D. (2020). 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. doi:10.1016/j.jclepro.2020.121566

[3] Ahmed, W., & Sarkar, B. (2018). Impact of carbon emissions in a sustainable supply chain management for a second generation biofuel. Journal of Cleaner Production, 186, 807-820. doi:10.1016/j.jclepro.2018.02.289

[4] Alimohammadi Ardekani, M. J. A. i. I. E. (2022). A Multi-Objective Supply Chain Configuration for the Oil Industry under Uncertainty. 56(1), 15-41. doi:10.22059/aie.2022.335295.1816

[5] Atabaki, M. S., Mohammadi, M., & Naderi, B. (2020). New robust optimization models for closed-loop supply chain of durable products: Towards a circular economy. Computers & Industrial Engineering, 146, 106520. doi:10.1016/j.cie.2020.106520

[6] Babaeinesami, A., Tohidi, H. & Seyedaliakbar, S.M. (2020): Designing a data-driven leagile sustainable closed-loop supply chain network, International Journal of Management Science and Engineering Management, DOI: 10.1080/17509653.2020.1811794

[7] Ben-Daya, M., Hassini, E., & Bahroun, Z. (2017). Internet of things and supply chain management: a literature review. International Journal of Production Research, 57(15-16), 4719-4742. doi:10.1080/00207543.2017.1402140
[8] Calatayud, A., Mangan, J., & Christopher, M. (2019). The self-thinking supply chain. Supply Chain Management: An International Journal, 24(1), 22-38. doi:10.1108/scm-03-2018-0136
[9] Chang, C.-T., & Chang, C.-C. (2000). A linearization method for mixed 0–1 polynomial programs. Computers & Operations Research, 27(10), 1005-1016. doi:10.1016/s0305-0548(99)00071-4
[10] Chen, C., Zhang, G., shi, J., & Xia, Y. (2019). Remanufacturing Network Design for Dual-Channel Closed-Loop Supply Chain. Procedia CIRP, 83, 479-484. doi:10.1016/j.procir.2019.04.132

[11] Deb, K., & Jain, H. (2014). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577-601. doi:10.1109/tevc.2013.2281535
[12] Demirel, N. Ö., & Gökçen, H. (2008). A mixed integer programming model for remanufacturing in reverse logistics environment. The International Journal of Advanced Manufacturing Technology, 39(11-12), 1197-1206. doi:10.1007/s00170-007-1290-7
[13] Deutch, J. M., & Lester, R. K. (2004). Making technology work: applications in energy and the environment: Cambridge University Press. [14] Ebrahimi, S. B., & Bagheri, E. (2022). A multi-objective formulation for the closed-loop plastic supply chain under uncertainty. Operational Research, 1-44. doi:10.1007/s12351-022-00716-y
[15] Fang, I., Lin, W. J. A. i. P. E., & Management. (2021). A multi‐objective optimal decision model for a green closed‐loop supply chain under uncertainty: A real industrial case study. 16(2), 161-172. doi:0.14743/apem2021.2.391
[16] Fathollahi-Fard, A. M., Dulebenets, M. A., Hajiaghaei–Keshteli, M., Tavakkoli-Moghaddam, R., Safaeian, M., & Mirzahosseinian, H. (2021). Two hybrid meta-heuristic algorithms for a dual-channel closed-loop supply chain network design problem in the tire industry under uncertainty. Advanced Engineering Informatics, 50, 101418. doi:10.1016/j.aei.2021.101418
[17] Feng, L., Govindan, K., & Li, C. (2017). Strategic planning: Design and coordination for dual-recycling channel reverse supply chain considering consumer behavior. European Journal of Operational Research, 260(2), 601-612. doi:10.1016/j.ejor.2016.12.050
[18] Gharye Mirzaei, M., Goodarzian, F., Maddah, S., Abraham, A., & Abdelkareim Gabralla, L. (2022). Investigating a Dual-Channel Network in a Sustainable Closed-Loop Supply Chain Considering Energy Sources and Consumption Tax. Sensors (Basel), 22(9), 3547. doi:10.3390/s22093547
[19] Gholami, F., Paydar, M. M., Hajiaghaei-Keshteli, M., & Cheraghalipour, A. (2019). A multi-objective robust supply chain design considering reliability. Journal of Industrial and Production Engineering, 36(6), 385-400. doi:10.1080/21681015.2019.1658136
[20] Gholizadeh, H., & Fazlollahtabar, H. (2020). Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry. Computers & Industrial Engineering, 147, 106653. doi:10.1016/j.cie.2020.106653
[21] Gholizadeh, H., Jahani, H., Abareshi, A., & Goh, M. (2021). Sustainable closed-loop supply chain for dairy industry with robust and heuristic optimization. Computers & Industrial Engineering, 157, 107324. doi:10.1016/j.cie.2021.107324
[22] Gholizadeh, H., Tajdin, A., & Javadian, N. (2018). A closed-loop supply chain robust optimization for disposable appliances. Neural Computing and Applications, 32(8), 3967-3985. doi:10.1007/s00521-018-3847-9
[23] Glassmeyer, S. T., Hinchey, E. K., Boehme, S. E., Daughton, C. G., Ruhoy, I. S., Conerly, O., . . . Thompson, V. G. (2009). Disposal practices for unwanted residential medications in the United States. Environ Int, 35(3), 566-572. doi:10.1016/j.envint.2008.10.007
[24] Hajipour, V., Tavana, M., Di Caprio, D., Akhgar, M., & Jabbari, Y. (2019). An optimization model for traceable closed-loop supply chain networks. Applied Mathematical Modelling, 71, 673-699. doi:10.1016/j.apm.2019.03.007
[25] Ho, C.-J. (2007). Evaluating the impact of operating environments on MRP system nervousness. International Journal of Production Research, 27(7), 1115-1135. doi:10.1080/00207548908942611
[26] Honarvar, M., & Ahmadi Yazdi, A. J. I. J. o. E. (2015). A Two Stage Stochastic Programming Model of the Price Decision Problem in the Dualchannel Closed-loop Supply Chain. International Journal of Engineering, 28(5 (B)), 738-745. doi:10.5829/idosi.ije.2015.28.05b.12
[27] Hong, X., Wang, Z., Wang, D., & Zhang, H. (2013). Decision models of closed-loop supply chain with remanufacturing under hybrid dual-channel collection. The International Journal of Advanced Manufacturing Technology, 68(5-8), 1851-1865. doi:10.1007/s00170-013-4982-1
[28] Huang, S.-M., & Su, J. C. P. (2013). Impact of product proliferation on the reverse supply chain. Omega, 41(3), 626-639.
doi:10.1016/j.omega.2012.08.003
[29] Kaoud, E., Abdel-Aal, M. A. M., Sakaguchi, T., & Uchiyama, N. (2020). Design and Optimization of the Dual-Channel Closed Loop Supply Chain with E-Commerce. Sustainability, 12(23), 10117. doi:10.3390/su122310117
[30] Khorshidvand, B., Soleimani, H., Sibdari, S., & Mehdi Seyyed Esfahani, M. (2021). A hybrid modeling approach for green and sustainable closedloop supply chain considering price, advertisement and uncertain demands. Computers & Industrial Engineering, 157, 107326. doi:10.1016/j.cie.2021.107326
[31] Kim, T., & Glock, C. H. (2014). On the use of RFID in the management of reusable containers in closed-loop supply chains under stochastic container return quantities. Transportation Research Part E: Logistics and Transportation Review, 64, 12-27. doi:10.1016/j.tre.2014.01.011
[32] Liu, Z., Anderson, T. D., & Cruz, J. M. (2012). Consumer environmental awareness and competition in two-stage supply chains. European Journal of Operational Research, 218(3), 602-613. doi:10.1016/j.ejor.2011.11.027
[33] Ma, R., Yao, L., Jin, M., Ren, P., & Lv, Z. (2016). Robust environmental closed-loop supply chain design under uncertainty. Chaos, Solitons & Fractals, 89, 195-202. doi:10.1016/j.chaos.2015.10.028
[34] Mavrotas, G. (2009). Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 213(2), 455-465. doi:10.1016/j.amc.2009.03.037
[35] Mavrotas, G., Figueira, J. R., & Antoniadis, A. J. J. o. G. O. (2011). Using the idea of expanded core for the exact solution of bi-objective multidimensional knapsack problems. 49(4), 589-606.
[36] Mavrotas, G. J. A. m., & computation. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. 213(2), 455-465.
[37] Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. d. S. J. E. S. w. A. (2016). Multi-objective grey wolf optimizer: a novel algorithm for multicriterion optimization. 47, 106-119.
[38] Mirzaee, H., Samarghandi, H., & Willoughby, K. J. a. p. a. (2022). A robust optimization model for green supplier selection and order allocation in a closed-loop supply chain considering cap-and-trade mechanism. doi:10.48550/arXiv.2208.02926
[39] Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust Optimization of Large-Scale Systems. Operations Research, 43(2), 264-281. doi:10.1287/opre.43.2.264
[40] Nezhad, F. J., Taghizadeh-Yazdi, M., Dahooie, J. H., Babgohari, A. Z., & Sajadi, S. M. J. B. F. J. (2021). Designing a new mathematical model for optimising a multi-product RFID-based closed-loop food supply chain with a green entrepreneurial orientation.
[41] Nikas, A., Fountoulakis, A., Forouli, A., & Doukas, H. (2020). A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems. Operational Research, 22(2), 1291-1332. doi:10.1007/s12351-020-00574-6
[42] Niranjan, T., Parthiban, P., Sundaram, K., & Jeyaganesan, P. N. (2019). Designing a omnichannel closed loop green supply chain network adapting preferences of rational customers. Sādhanā, 44(3), 1-10. doi:10.1007/s12046-018-1038-0

[43] Niranjan, T., Thanigaivelan, R., & Singaravel, B. (2022). Analysis of a Multi-channel Closed Loop Green Supply Chain Using Modified Particle Swarm Optimization Algorithm. In Innovations in Mechanical Engineering (pp. 797-807): Springer.
[44] Paydar, M. M., Babaveisi, V., & Safaei, A. S. (2017). An engine oil closed-loop supply chain design considering collection risk. Computers & Chemical Engineering, 104, 38-55. doi:10.1016/j.compchemeng.2017.04.005
[45] Persson, M., Sabelström, E., & Gunnarsson, B. J. E. i. (2009). Handling of unused prescription drugs—knowledge, behaviour and attitude among Swedish people. 35(5), 771-774. doi:10.1016/j.envint.2008.10.002
[46] Rahmani, D., Hasan Abadi, M. Q., & Hosseininezhad, S. J. (2020). Joint decision on product greenness strategies and pricing in a dual-channel supply chain: A robust possibilistic approach. Journal of Cleaner Production, 256, 120437. doi:10.1016/j.jclepro.2020.120437
[47] Ramezani, M., Kimiagari, A. M., Karimi, B., & Hejazi, T. H. (2014). Closed-loop supply chain network design under a fuzzy environment. Knowledge-Based Systems, 59, 108-120. doi:10.1016/j.knosys.2014.01.016
[48] Ross, D. F., Weston, F. S., & Stephen, W. (2010). Introduction to supply chain management technologies: Crc Press.
[49] Sadeghi Rad, R., & 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, 196, 1549-1565. doi:10.1016/j.jclepro.2018.06.034
[50] Safaei, A. S., Roozbeh, A., & Paydar, M. M. (2017). A robust optimization model for the design of a cardboard closed-loop supply chain. Journal of Cleaner Production, 166, 1154-1168. doi:10.1016/j.jclepro.2017.08.085
[51] Saffari, H., Makui, A., Mahmoodian, V., Pishvaee, M. S. J. J. o. I., & Engineering, S. (2015). Multi-objective robust optimization model for social responsible closed-loop supply chain solved by non-dominated sorting genetic algorithm. 8(3), 42-58.
[52] Şakar, C. T., & Köksalan, M. J. J. o. G. O. (2013). A stochastic programming approach to multicriteria portfolio optimization. 57(2), 299-314.
[53] Samuel, C. N., Venkatadri, U., Diallo, C., & Khatab, A. (2020). Robust closed-loop supply chain design with presorting, return quality and carbon emission considerations. Journal of Cleaner Production, 247, 119086. doi:10.1016/j.jclepro.2019.119086
[54] Soleimani, H., Chhetri, P., Fathollahi-Fard, A. M., Mirzapour Al-e-Hashem, S. M. J., & Shahparvari, S. (2022). Sustainable closed-loop supply chain with energy efficiency: Lagrangian relaxation, reformulations and heuristics. Annals of Operations Research, 1-26. doi:10.1007/s10479-022-04661-z
[55] Tohidi, H., Jabbari, M.M., (2012). “Decision role in management to increase effectiveness of an organization”. Procedia-social and behavioral sciences, 32: 825-828. doi:10.1016/j.sbspro.2011.12.149
[56] Tong, A. Y., Peake, B. M., & Braund, R. (2011). Disposal practices for unused medications around the world. Environ Int, 37(1), 292-298. doi:10.1016/j.envint.2010.10.002
[57] Vidal, C. J., & Goetschalckx, M. (2001). A global supply chain model with transfer pricing and transportation cost allocation. European Journal of Operational Research, 129(1), 134-158. doi:10.1016/s0377-2217(99)00431-2
[58] Wang, Z.-B., Wang, Y.-Y., & Wang, J.-C. (2016). Optimal distribution channel strategy for new and remanufactured products. Electronic Commerce Research, 16(2), 269-295. doi:10.1007/s10660-016-9225-8
[59] Yan, W., Xiong, Y., Xiong, Z., & Guo, N. (2015). Bricks vs. clicks: Which is better for marketing remanufactured products? European Journal of Operational Research, 242(2), 434-444. doi:10.1016/j.ejor.2014.10.023
[60] Yu, Y., Han, X., & Hu, G. (2016). Optimal production for manufacturers considering consumer environmental awareness and green subsidies. International Journal of Production Economics, 182, 397-408. doi:10.1016/j.ijpe.2016.09.014
[61] Zelbst, P. J., Green, K. W., Sower, V. E., & Bond, P. L. (2019). The impact of RFID, IIoT, and Blockchain technologies on supply chain transparency. Journal of Manufacturing Technology Management, 31(3), 441-457. doi:10.1108/jmtm-03-2019-0118
[62] Zhou, Z., Hu, F., & Xiao, D. (2020). Optimal pricing strategy of competing manufacturers under carbon policy and consumer environmental awareness. Computers & Industrial Engineering, 150, 106918. doi:10.1016/j.cie.2020.106918