Research article Special Issues

An environmental decision support system for manufacturer-retailer within a closed-loop supply chain management using remanufacturing

  • Received: 09 January 2023 Revised: 09 October 2023 Accepted: 17 October 2023 Published: 30 October 2023
  • Industries face many challenges when emergencies arise. In emergency, there is an increasing demand for self-administered products that are easy to use. The decay rate of these products decreases with time. Moreover, the lack of disposal of used products increases waste and carbon emissions. By observing the scenario, this study develops a closed-loop supply chain management that considers the collection and remanufacturing of used products. The manufacturing rate is linear and the demand is ramp-type and carbon emissions dependent. The model is solved by a classical optimization and calculates the optimal total cost. The results show that the retailer can handle a shortage situation when the demand becomes stable (Case 2) and the total cost increases with the production rate. A sensitivity analysis shows the changes in the total cost with respect to the parameters.

    Citation: Subhash Kumar, Ashok Kumar, Rekha Guchhait, Biswajit Sarkar. An environmental decision support system for manufacturer-retailer within a closed-loop supply chain management using remanufacturing[J]. AIMS Environmental Science, 2023, 10(5): 644-676. doi: 10.3934/environsci.2023036

    Related Papers:

  • Industries face many challenges when emergencies arise. In emergency, there is an increasing demand for self-administered products that are easy to use. The decay rate of these products decreases with time. Moreover, the lack of disposal of used products increases waste and carbon emissions. By observing the scenario, this study develops a closed-loop supply chain management that considers the collection and remanufacturing of used products. The manufacturing rate is linear and the demand is ramp-type and carbon emissions dependent. The model is solved by a classical optimization and calculates the optimal total cost. The results show that the retailer can handle a shortage situation when the demand becomes stable (Case 2) and the total cost increases with the production rate. A sensitivity analysis shows the changes in the total cost with respect to the parameters.



    加载中


    [1] Rani S, Ali R, Agarwal A (2022) Fuzzy inventory model for new and refurbished deteriorating items with cannibalisation in green supply chain. Int J Syst Sci Operat Logist 9: 22–38. https://doi.org/10.1080/23302674.2020.1803434 doi: 10.1080/23302674.2020.1803434
    [2] Sarkar B, Sarkar M, Ganguly B, et al. (2020) Combined effects of carbon emission and production quality improvement for fixed lifetime products in a sustainable supply chain management. Int J Prod Econ 231: 107867. https://doi.org/10.1016/j.ijpe.2020.107867 doi: 10.1016/j.ijpe.2020.107867
    [3] Sarkar B, Guchhait R (2023) Ramification of information asymmetry on a green supply chain management with the cap-trade, service, and vendor-managed inventory strategies. Elect Commer Res App 60: 101274. https://doi.org/10.1016/j.elerap.2023.101274 doi: 10.1016/j.elerap.2023.101274
    [4] Saxena N, Sarkar B, Wee H M, et al. (2023) A reverse logistics model with eco-design under the Stackelberg-Nash equilibrium and centralized framework. J Clean Prod 387: 135789. https://doi.org/10.1016/j.jclepro.2022.135789 doi: 10.1016/j.jclepro.2022.135789
    [5] Ullah M, Asghar I, Zahid M, et al. (2021) Ramification of remanufacturing in a sustainable three-echelon closed-loop supply chain management for returnable products. J Clean Prod 290: 125609. https://doi.org/10.1016/j.jclepro.2020.125609 doi: 10.1016/j.jclepro.2020.125609
    [6] Sarkar B, Bhuniya S (2022) A sustainable flexible manufacturing–remanufacturing model with improved service and green investment under variable demand, Exp Syst with Appli 202: 117154. https://doi.org/10.1016/j.eswa.2022.117154 doi: 10.1016/j.eswa.2022.117154
    [7] Cheng S, Zhang F, Chen X (2024) Optimal contract design for a supply chain with information asymmetry under dual environmental responsibility constraints. Exp Syst App 237: 121466. https://doi.org/10.1016/j.eswa.2023.121466 doi: 10.1016/j.eswa.2023.121466
    [8] Kumar S, Kumar A, Jain M (2020) Learning effect on an optimal policy for mathematical inventory model for decaying items under preservation technology with the environment of COVID-19 pandemic. Malaya J Matemat 8: 1694–1702. https://doi.org/10.26637/MJM0804/0063 doi: 10.26637/MJM0804/0063
    [9] Dey B K, Bhuniya S, Sarkar B (2021) Involvement of controllable lead time and variable demand for a smart manufacturing system under a supply chain management. Exp Syst App 184: 115464. https://doi.org/10.1016/j.eswa.2021.115464 doi: 10.1016/j.eswa.2021.115464
    [10] Ullah M, Sarkar B (2020) Recovery-channel selection in a hybrid manufacturing-remanufacturing production model with RFID and product quality. Int J Prod Econ 219: 360–374. https://doi.org/10.1016/j.ijpe.2019.07.017 doi: 10.1016/j.ijpe.2019.07.017
    [11] Cheng M, Zhang B, Wang G (2011) Optimal policy for deteriorating items with trapezoidal type demand and partial backlogging. App Math Model 35: 3552–3560. https://doi.org/10.1016/j.apm.2011.01.001 doi: 10.1016/j.apm.2011.01.001
    [12] Rini, Priyamvada, Jaggi C K (2021) Sustainable and flexible production system for a deteriorating item with quality consideration. Int J Syst Assuran Eng Manag 12: 951–960. https://doi.org/10.1007/s13198-021-01169-w doi: 10.1007/s13198-021-01169-w
    [13] Kawakatsu H (2010) Optimal retailer's replenishment policy for seasonal products with ramp-type demand rate. Int J App Math 40: 1–7.
    [14] Panda S, Senapati S, Basu M (2008) Optimal replenishment policy for perishable seasonal products in a season with ramp-type time dependent demand. Comput Indust Eng 54: 301–314. https://doi.org/10.1016/j.cie.2007.07.011 doi: 10.1016/j.cie.2007.07.011
    [15] Sarkar B, Ullah M, Sarkar M (2022) Environmental and economic sustainability through innovative green products by remanufacturing. J Clean Prod 332: 129813. https://doi.org/10.1016/j.jclepro.2021.129813 doi: 10.1016/j.jclepro.2021.129813
    [16] Saha S, Sarkar B, Sarkar M (2023) Application of improved meta-heuristic algorithms for green preservation technology management to optimize dynamical investments and replenishment strategies. Math Comp Simul 209: 426–450. https://doi.org/10.1016/j.matcom.2023.02.005 doi: 10.1016/j.matcom.2023.02.005
    [17] Skouri K, Konstantaras I, Papachristos S, et al. (2009) Inventory models with ramp type demand rate, partial backlogging and Weibull deterioration rate. Euro J Operat Res 192: 79–92. ttps://doi.org/10.1016/j.ejor.2007.09.003
    [18] Wu J, Skouri K, Teng J T, et al. (2016) Two inventory systems with trapezoidal-type demand rate and time-dependent deterioration and backlogging. Exp Syst App 46: 367–379. https://doi.org/10.1016/j.eswa.2015.10.048 doi: 10.1016/j.eswa.2015.10.048
    [19] Wee H M, Chung c C J (2009) Optimizing replenishment policy for an integrated production inventory deteriorating model considering green component-value design and remanufacturing. Int J Prod Res 47: 1343–1368. https://doi.org/10.1080/00207540701570182 doi: 10.1080/00207540701570182
    [20] Miramontes-Viña V, Romero-Castro N, López-Cabarcos M Á (2023) Advancing towards a sustainable energy model. Uncovering the untapped potential of rural areas. AIMS Env Sci 10: 287–312. https://doi.org/10.3934/environsci.2023017 doi: 10.3934/environsci.2023017
    [21] Monteiro S, Amor-Esteban V, Lemos K, et al. (2023) Are we doing the same? A worldwide analysis of business commitment to the SDGs. AIMS Env Sci 10: 446–466. https://doi.org/10.3934/environsci.2023025 doi: 10.3934/environsci.2023025
    [22] Osabuohien-Irabor O, Drapkin I M (2023) Toward achieving zero-emissions in European Union countries: The contributions of trade and overseas direct investments in consumptionbased carbon emissions. AIMS Env Sci 10: 129–156. https://doi.org/10.3934/environsci.2023008 doi: 10.3934/environsci.2023008
    [23] Zhou X, Zhu Q, Xu L, et al. (2024) The effect of carbon tariffs and the associated coping strategies: A global supply chain perspective. Omega 122: 102960. https://doi.org/10.1016/j.omega.2023.102960 doi: 10.1016/j.omega.2023.102960
    [24] Rossit D, Guggeri E M, Ham C, et al. (2023) Goal programming and multi-criteria methods in remanufacturing and reverse logistics: Systematic literature review and survey. Comp Indust Eng 109587. https://doi.org/10.1016/j.cie.2023.109587 doi: 10.1016/j.cie.2023.109587
    [25] Jaber M Y, El Saadany A M A (2009) The production, remanufacture and waste disposal model with lost sales. Int J Prod Econ 115–124. https://doi.org/10.1016/j.ijpe.2008.07.016 doi: 10.1016/j.ijpe.2008.07.016
    [26] Konstantaras I, Skouri K, Jaber M Y (2010) Lot sizing for a recoverable product with inspection and sorting, Comput Indust Eng 58: 452–462. https://doi.org/10.1016/j.cie.2009.11.004 doi: 10.1016/j.cie.2009.11.004
    [27] Koh S G, Hwang H, Sohn K I, et al. (2002) An optimal ordering and recovery policy for reusable items. Comput Indust Eng 43: 59–73. https://doi.org/10.1016/S0360-8352(02)00062-1 doi: 10.1016/S0360-8352(02)00062-1
    [28] Liao T Y (2018) Reverse logistics network design for product recovery and remanufacturing. App Math Model 60: 145–163. https://doi.org/10.1016/j.apm.2018.03.003 doi: 10.1016/j.apm.2018.03.003
    [29] Marić J, Opazo-Basáez M (2019) Green servitization for flexible and sustainable supply chain operations: A review of reverse logistics services in manufacturing. Global J Flex Syst Manag 20: S65-S80. https://doi.org/10.1007/s40171-019-00225-6 doi: 10.1007/s40171-019-00225-6
    [30] Weng Z K, McClurg T (2003) Coordinated ordering decisions for short life cycle products with uncertainty in delivery time and demand. Euro J Operat Res 151: 12–24. https://doi.org/10.1016/S0377-2217(02)00577-5 doi: 10.1016/S0377-2217(02)00577-5
    [31] Ahmadi S, Shokouhyar S, Amerioun M, et al. (2024) A social media analytics-based approach to customer-centric reverse logistics management of electronic devices: A case study on notebooks. J Retail Consum Serv 76: 103540. https://doi.org/10.1016/j.jretconser.2023.103540 doi: 10.1016/j.jretconser.2023.103540
    [32] Ahmed W, Sarkar B (2019) Management of next-generation energy using a triple bottom line approach under a supply chain framework. Resour Conserv Recyc 150: 104431. https://doi.org/10.1016/j.resconrec.2019.104431 doi: 10.1016/j.resconrec.2019.104431
    [33] Goodarzian F, Ghasemi P, Gonzalez E D R S, et al. (2023) A sustainable-circular citrus closed-loop supply chain configuration: Pareto-based algorithms. J Environ Manag 328: 116892. https://doi.org/10.1016/j.jenvman.2022.116892 doi: 10.1016/j.jenvman.2022.116892
    [34] Momenitabar M, Dehdari Ebrahimi Z, Arani M, et al. (2022) Designing a sustainable closed-loop supply chain network considering lateral resupply and backup suppliers using fuzzy inference system. Environ Dev Sustain https://doi.org/10.1007/s10668-022-02332-4. doi: 10.1007/s10668-022-02332-4
    [35] Mashud A H M, Pervin M, Mishra U, et al. (2021) A sustainable inventory model with controllable carbon emissions in green-warehouse farms. J Clean Prod 298: 126777. https://doi.org/10.1016/j.jclepro.2021.126777 doi: 10.1016/j.jclepro.2021.126777
    [36] Sarkar B, Omair M, Kim N (2020) A cooperative advertising collaboration policy in supply chain management under uncertain conditions. App Soft Comput 88: 105948. https://doi.org/10.1016/j.asoc.2019.105948 doi: 10.1016/j.asoc.2019.105948
    [37] Mishra U, Wu J Z, Sarkar B (2021) Optimum sustainable inventory management with backorder and deterioration under controllable carbon emissions. J Clean Prod 279: 123699. https://doi.org/10.1016/j.jclepro.2020.123699 doi: 10.1016/j.jclepro.2020.123699
    [38] Kugele A S H, Sarkar B (2023) Reducing carbon emissions of a multi-stage smart production for biofuel towards sustainable development. Alex Eng J 70: 93-113. https://doi.org/10.1016/j.aej.2023.01.003 doi: 10.1016/j.aej.2023.01.003
    [39] Mridha B, Pareek S, Goswami A, et al. (2023) Joint effects of production quality improvement of biofuel and carbon emissions towards a smart sustainable supply chain management. J Clean Prod 386: 135629. https://doi.org/10.1016/j.jclepro.2022.135629 doi: 10.1016/j.jclepro.2022.135629
    [40] Teng Y, Feng B (2021) Optimal channel structure for remanufacturing under cap-and-trade regulation. Process 9: 370. https://doi.org/10.3390/pr9020370 doi: 10.3390/pr9020370
    [41] Tiwari S, Daryanto Y, Wee H M (2018) Sustainable inventory management with deteriorating and imperfect quality items considering carbon emission. J Clean Prod 192: 281–292. https://doi.org/10.1016/j.jclepro.2018.04.261 doi: 10.1016/j.jclepro.2018.04.261
    [42] Yadav S, Khanna A (2021) Sustainable inventory model for perishable products with expiration date and price reliant demand under carbon tax policy. Proces Int Opt Sust 5: 475–486. https://doi.org/10.1007/s41660-021-00157-8 doi: 10.1007/s41660-021-00157-8
    [43] Mishra U, Wu J Z, Sarkar B (2020) A sustainable production-inventory model for a controllable carbon emissions rate under shortages. J Clean Prod 256: 120268. https://doi.org/10.1016/j.jclepro.2020.120268 doi: 10.1016/j.jclepro.2020.120268
    [44] Babaeinesami A, Tohidi H, Ghasemi P, et al. (2022) A closed-loop supply chain configuration considering environmental impacts: A self-adaptive NSGA-Ⅱ algorithm. App Intell 52: 13478–13496. https://doi.org/10.1007/s10489-021-02944-9 doi: 10.1007/s10489-021-02944-9
    [45] Goodarzian F, Kumar V, Ghasemi P (2022) Investigating a citrus fruit supply chain network considering CO2 emissions using meta-heuristic algorithms. Ann Oper Res https://doi.org/10.1007/s10479-022-05005-7. doi: 10.1007/s10479-022-05005-7
    [46] Alamri A A (2010) Theory and methodology on the global optimal solution to a general reverse logistics inventory model for deteriorating items and time-varying rates. Comput Indust Eng 60: 236–247. https://doi.org/10.1016/j.cie.2010.11.005 doi: 10.1016/j.cie.2010.11.005
    [47] Chung C J, Wee H M Short (2011) lifecycle deteriorating product remanufacturing in a green supply chain inventory control system. Int J Prod Econ 129: 195–203. https://doi.org/10.1016/j.ijpe.2010.09.033 doi: 10.1016/j.ijpe.2010.09.033
    [48] Motla R, Kumar A, Singh S R, et al. (2021) A fuzzy integrated inventory system with end of life treatment: A possibility in sport industry. Opsearch 58: 869–888. https://doi.org/10.1007/s12597-020-00492-3 doi: 10.1007/s12597-020-00492-3
    [49] Rani S, Ali R, Agarwal A (2019) Fuzzy inventory model for deteriorating items in a green supply chain with carbon concerned demand. Opsearch 56: 91–122. https://doi.org/10.1007/s12597-019-00361-8 doi: 10.1007/s12597-019-00361-8
    [50] Rani S, Ali R, Agarwal A (2020) Inventory model for deteriorating items in green supply chain with credit period dependent demand. Int J App Eng Res 15: 157–172.
    [51] Garai A, Sarkar B (2022) Economically independent reverse logistics of customer-centric closed-loop supply chain for herbal medicines and biofuel J Clean Prod 334: 129977. https://doi.org/10.1016/j.jclepro.2021.129977 doi: 10.1016/j.jclepro.2021.129977
    [52] Safdar N, Khalid R, Ahmed W, et al. (2020) Reverse logistics network design of e-waste management under the triple bottom line approach. J Clean Prod 272: 122662. https://doi.org/10.1016/j.jclepro.2020.122662 doi: 10.1016/j.jclepro.2020.122662
    [53] Sarkar B, Debnath A, Chiu A S F, et al. (2022) Circular economy-driven two-stage supply chain management for nullifying waste. J Clean Prod 339: 130513. https://doi.org/10.1016/j.jclepro.2022.130513 doi: 10.1016/j.jclepro.2022.130513
    [54] Singh S R, Sharma S (2019) A partially backlogged supply chain model for deteriorating items under reverse logistics, imperfect production/remanufacturing and inflation. Int J Logist Syst Manag 33: 221. https://doi.org/10.1504/IJLSM.2019.100113 doi: 10.1504/IJLSM.2019.100113
    [55] Wang C, Huang R (2014) Pricing for seasonal deteriorating products with price-and ramp-type time-dependent demand. Comput Indust Eng 77: 29–34. https://doi.org/10.1016/j.cie.2014.09.005 doi: 10.1016/j.cie.2014.09.005
    [56] Yang P C, Chung S L, Wee H M, et al. (2013) Collaboration for a closed-loop deteriorating inventory supply chain with multi-retailer and price-sensitive demand. Int J Prod Econ 143: 557–566. https://doi.org/10.1016/j.ijpe.2012.07.020 doi: 10.1016/j.ijpe.2012.07.020
    [57] Sarkar M, Sarkar B (2020) How does an industry reduce waste and consumed energy within a multi-stage smart sustainable biofuel production system? J Clean Prod 262: 121200. https://doi.org/10.1016/j.jclepro.2020.121200 doi: 10.1016/j.jclepro.2020.121200
    [58] Dey B K, Pareek S, Tayyab M, et al. (2021) Autonomation policy to control work-in-process inventory in a smart production system. Int J Prod Res 59: 1258–1280. https://doi.org/10.1080/00207543.2020.1722325 doi: 10.1080/00207543.2020.1722325
    [59] Li P, Chen B, Cui Q (2023) A probabilistic life-cycle assessment of carbon emission from magnesium phosphate cementitious material with uncertainty analysis. J Clean Prod 139164. https://doi.org/10.1016/j.jclepro.2023.139164 doi: 10.1016/j.jclepro.2023.139164
    [60] Habib M S, Asghar O, Hussain A, et al. (2021) A robust possibilistic programming approach toward animal fat-based biodiesel supply chain network design under uncertain environment. J Clean Prod 278: 122403. https://doi.org/10.1016/j.jclepro.2020.122403 doi: 10.1016/j.jclepro.2020.122403
    [61] Sarkar B, Tayyab M, Kim N, et al. (2019) Optimal production delivery policies for supplier and manufacturer in a constrained closed-loop supply chain for returnable transport packaging through metaheuristic approach. Comp Indust Eng 135: 987–1003. https://doi.org/10.1016/j.cie.2019.05.035 doi: 10.1016/j.cie.2019.05.035
    [62] Singh S K, Chauhan A, Sarkar B (2023) Sustainable biodiesel supply chain model based on waste animal fat with subsidy and advertisement. J Clean Prod 382: 134806. https://doi.org/10.1016/j.jclepro.2022.134806 doi: 10.1016/j.jclepro.2022.134806
    [63] Ke C, Pan X, Wan P, et al. (2023) An integrated design method for used product remanufacturing scheme considering carbon emission. Sustain Prod Consum 41: 348–361. https://doi.org/10.1016/j.spc.2023.08.018 doi: 10.1016/j.spc.2023.08.018
    [64] Huang J, Yan Y, Kang J, et al. (2023) Driving technology factors of carbon emissions: Theoretical framework and its policy implications for China. Sci Total Environ 904: 166858. https://doi.org/10.1016/j.scitotenv.2023.166858 doi: 10.1016/j.scitotenv.2023.166858
    [65] Tiwari S, Ahmed W, Sarkar B (2019) Sustainable ordering policies for non-instantaneous deteriorating items under carbon emission and multi-trade-credit-policies. J Clean Prod 240: 118183. https://doi.org/10.1016/j.jclepro.2019.118183 doi: 10.1016/j.jclepro.2019.118183
    [66] Sepehri A, Mishra U, Sarkar B (2021) A sustainable production-inventory model with imperfect quality under preservation technology and quality improvement investment. J Clean Prod 310: 127332. https://doi.org/10.1016/j.jclepro.2021.127332 doi: 10.1016/j.jclepro.2021.127332
    [67] Yadav D, Kumari R, Kumar N, et al. (2021) Reduction of waste and carbon emission through the selection of items with cross-price elasticity of demand to form a sustainable supply chain with preservation technology. J Clean Prod 297: 126298. https://doi.org/10.1016/j.jclepro.2021.126298 doi: 10.1016/j.jclepro.2021.126298
    [68] Tayyab M, Jemai J, Lim H, et al. (2020)A sustainable development framework for a cleaner multi-item multi-stage textile production system with a process improvement initiative. J Clean Prod 246: 119055. https://doi.org/10.1016/j.jclepro.2019.119055 doi: 10.1016/j.jclepro.2019.119055
    [69] Mao H, Wang W, Liu C, et al. (2023) Effects of the carbon emission quota policy on the quality and sales of manufactured and remanufactured products. Int J Prod Econ 266: 109058. https://doi.org/10.1016/j.ijpe.2023.109058 doi: 10.1016/j.ijpe.2023.109058
    [70] LI Y, YI F, YUAN C Influences of large-scale farming on carbon emissions from cropping: Evidence from China. J Integr Agricul https://doi.org/10.1016/j.jia.2023.08.006
  • Environ-10-05-036-s001.pdf
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(702) PDF downloads(90) Cited by(0)

Article outline

Figures and Tables

Figures(7)  /  Tables(4)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog