Free Access
RAIRO-Oper. Res.
Volume 55, Number 4, July-August 2021
Page(s) 2285 - 2307
Published online 13 August 2021
  • Q. Bai, Y.Y. Gong, M. Jin and X. Xu, Effects of carbon emission reduction on supply chain coordination with vendor-managed deteriorating product inventory. Int. J. Prod. Econ. 208 (2018) 83–99. [CrossRef] [Google Scholar]
  • R. Batarfi, M.Y. Jaber and C.H. Glock, Pricing and inventory decisions in a dual-channel supply chain with learning and forgetting. Comput. Ind. Eng. 136 (2019) 397–420. [CrossRef] [Google Scholar]
  • M. Bernon and J. Cullen, An integrated approach to managing reverse logistics. Int. J. Logistics Res. App. 10 (2007) 41–56. [CrossRef] [Google Scholar]
  • R. Chakrabarty, T. Roy and K.S. Chaudhuri, A two-warehouse inventory model for deteriorating items with capacity constraints and back-ordering under financial considerations. Int. J. Appl. Comput. Math. 4 (2018) 58. [CrossRef] [Google Scholar]
  • M. Chouinard, S. D’Amours and D. Aït-Kadi, Integration of reverse logistics activities within a supply chain information system. Comput. Ind. 56 (2005) 105–124. [CrossRef] [Google Scholar]
  • K.-J. Chung, C.-C. Her and S.-D. Lin, A two-warehouse inventory model with imperfect quality production processes. Comput. Ind. Eng. 56 (2009) 193–197. [CrossRef] [Google Scholar]
  • C.-Y. Dye and C.-T. Yang, Sustainable trade credit and replenishment decisions with credit-linked demand under carbon emission constraints. Eur. J. Oper. Res. 244 (2015) 187–200. [CrossRef] [Google Scholar]
  • Y. Ghiamia and P. Beullens, The continuous resupply policy for deteriorating items with stock-dependent observable demand in a two-warehouse and two-echelon supply chain. Appl. Math. Modell. 82 (2020) 271–292. [CrossRef] [Google Scholar]
  • B.C. Giri and C.H. Glock, A closed-loop supply chain with stochastic product returns and worker experience under learning and forgetting. Int. J. Prod. Res. 55 (2017) 6760–6778. [CrossRef] [Google Scholar]
  • B.C. Giri and M. Masanta, Developing a closed-loop supply chain model with price and quality dependent demand and learning in production in a stochastic environment. Int. J. Syst. Sci.: Oper. Logistics 7 (2020) 147–163. [Google Scholar]
  • Y.-S. Huang, C.-C. Fang and Y.-A. Lin, Inventory management in supply chains with consideration of logistics, green investment and different carbon emissions policies. Comput. Ind. Eng. 139 (2019). [Google Scholar]
  • P. Jawla and S.R. Singh, A reverse logistic inventory model for imperfect production process with preservation technology investment under learning and inflationary environment, Uncertain Suppl. Chain Manage. 4 (2016) 107–122. [CrossRef] [Google Scholar]
  • W.F. Khan and O. Dey, Periodic review inventory model with normally distributed fuzzy random variable demand. Int. J. Syst. Sci.: Oper. Logistics 6 (2017) 1–11. [Google Scholar]
  • M.A. Khan, A.A. Shaikh, G.C. Panda and I. Konstantaras, Two-warehouse inventory model for deteriorating items with partial backlogging and advance payment scheme. RAIRO:OR 53 (2019) 1691–1708. [Google Scholar]
  • M.A.-A. Khan, A.A. Shaikh, G.C. Panda, A.K. Bhunia and I. Konstantaras, Non-instantaneous deterioration effect in ordering decisions for a two-warehouse inventory system under advance payment and backlogging. Ann. Oper. Res. 289 (2020) 243–275. [CrossRef] [Google Scholar]
  • R.S. Kumar, M.K. Tiwari and A. Goswami, Two-echelon fuzzy stochastic supply chain for the manufacturer–buyer integrated production-inventory system. J. Intell. Manuf. 27 (2014) 875–888. [CrossRef] [Google Scholar]
  • S. Kumar, A. Kumar and M. Jain, 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. Matematik 8 (2020) 1694–1702. [CrossRef] [Google Scholar]
  • T.-Y. Liao, Reverse logistics network design for product recovery and remanufacturing. Appl. Math. Model. 60 (2018) 145–163. [CrossRef] [Google Scholar]
  • J.-J. Liao, K.-J. Chung and K.-N. Huang, A deterministic inventory model for deteriorating items with two warehouses and trade credit in a supply chain system. Int. J. Prod. Econ. 146 (2013) 557–565. [CrossRef] [Google Scholar]
  • A.K. Manna, J.K. Dey and S.K. Mondal, Three-layer supply chain in an imperfect production inventory model with two storage facilities under fuzzy rough environment. J. Uncertainty Anal. App. 2 (2014) 17. [Google Scholar]
  • P. Majumder, U.K. Bera and M. Maiti, An EPQ model for two-warehouse in unremitting release pattern with two-level trade credit period concerning both supplier and retailer. Appl. Math. Comput. 274 (2016) 430–458. [Google Scholar]
  • J. Maric and M. Opazo-Basáez, Green servitization for flexible and sustainable supply chain operations: a review of reverse logistics services in manufacturing. Global J. Flexible Syst. Manage. 20 (2019) S65–S80. [CrossRef] [Google Scholar]
  • U. Mishra, J.-Z. Wu and B. Sarkar, A sustainable production-inventory model for a controllable carbon emissions rate under shortages. J. Cleaner Prod. 256 (2020). [Google Scholar]
  • J. Noh and J.S. Kim, Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand. J. Cleaner Prod. 208 (2019) 1421–1435. [CrossRef] [Google Scholar]
  • S. Rani, R. Ali and A. Agarwal, Fuzzy inventory model for deteriorating items in a green supply chain with carbon concerned demand. OPSEARCH 56 (2019) 91–122. [CrossRef] [Google Scholar]
  • S. Rani, R. Ali and A. Agarwal, Inventory model for deteriorating items in green supply chain with credit period dependent demand. Int. J. Appl. Eng. Res. 15 (2020) 157–172. [Google Scholar]
  • S. Rani, R. Ali and A. Agarwal, Fuzzy inventory model for new and refurbished deteriorating items with cannibalisation in green supply chain. Int. J. Syst. Sci.: Oper. Logistics (2020) 1–17. DOI: 10.1080/23302674.2020.1803434. [Google Scholar]
  • A. Roy, K. Maity, S. Kar and M. Maiti, A production-inventory model with remanufacturing for defective and usable items in fuzzy-environment. Comput. Ind. Eng. 56 (2009) 87–96. [CrossRef] [Google Scholar]
  • B. Sarkar, Mathematical and analytical approach for the management of defective items in a multi-stage production system. J. Cleaner Prod. 218 (2019) 896–919. [CrossRef] [Google Scholar]
  • B. Sarkar, M. Ullah and N. Kim, Environmental and economic assessment of closed-loop supply chain with remanufacturing and returnable transport items. Comput. Ind. Eng. 111 (2017) 148–163. [Google Scholar]
  • B. Sarkar, M. Tayyab, N. Kim and M.S. Habib, Optimal production delivery policies for supplier and manufacturer in a constrained closed-loop supply chain for returnable transport packaging through metaheuristic approach. Comput. Ind. Eng. 135 (2019) 987–1003. [Google Scholar]
  • B. Sarkar, M. Sarkar, B. Ganguly and L.E. Cardenas-Barron, Combined effects of carbon emission and production quality improvement for fixed lifetime products in a sustainable supply chain management. Int. J. Prod. Econ. (2020) 107867. [Google Scholar]
  • N. Safdar, R. Khalid, W. Ahmed and M. Imran, Reverse logistics network design of e-waste management under the triple bottom line approach. J. Cleaner Prod. 272 (2020) 122662. [CrossRef] [Google Scholar]
  • C. Singh and S.R. Singh, Supply chain model for expiring items following ramp-type demand with stochastic lead time under crisp and fuzzy environment. Int. J. Fuzzy Syst. App. 9 (2020) 64–91. [Google Scholar]
  • S.R. Singh and S. Sharma, A partially backlogged supply chain model for deteriorating items under reverse logistics, imperfect production/remanufacturing and inflation. Int. J. Logistics Syst. Manage. 33 (2019) 221. [CrossRef] [Google Scholar]
  • S.R. Singh, S. Sharma and M. Kumar, A reverse logistics model for decaying items with variable production and remanufacturing incorporating learning effects. Int. J. Oper. Res. 38 (2020) 422. [CrossRef] [Google Scholar]
  • N. Saxena, S.R. Singh and S.S. Sana, A green supply chain model of vendor and buyer for remanufacturing. RAIRO:OR 51 (2017) 1133–1150. [Google Scholar]
  • N. Saxena, B. Sarkar and S.R. Singh, Selection of remanufacturing/production cycles with an alternative market: a perspective on waste management. J. Cleaner Prod. 245 (2020) 118935. [CrossRef] [Google Scholar]
  • M. Tayyab, B. Sarkar and B. Yahya, Imperfect multi-stage lean manufacturing system with rework under fuzzy demand. Mathematics 7 (2018) 13. [CrossRef] [Google Scholar]
  • M. Tayyab, B. Sarkar and M. Ullah, Sustainable lot size in a multi-stage lean-green manufacturing process under uncertainty. Mathematics 7 (2018) 20. [CrossRef] [Google Scholar]
  • S. Tiwari, C.K. Jaggi, A.K. Bhunia, A.A. Shaikh and M. Goh, Two-warehouse inventory model for non-instantaneous deteriorating items with stock-dependent demand and inflation using particle swarm optimization. Ann. Oper. Res. 254 (2017) 401–423. [Google Scholar]
  • Y.-C. Tsao and G.-J. Sheen, Effects of promotion cost sharing policy with the sales learning curve on supply chain coordination. Comput. Oper. Res. 39 (2012) 1872–1878. [CrossRef] [Google Scholar]
  • M. Ullah, I. Asghar, M. Zahid, M. Omair, A. AlArjani and B. Sarkar, Ramification of remanufacturing in a sustainable three-echelon closed-loop supply chain management for returnable products. J. Cleaner Prod. 290 (2021) 125609. [CrossRef] [Google Scholar]
  • H.-M. Wee, C.-C. Lo and P.-H. Hsu, A multi-objective joint replenishment inventory model of deteriorated items in a fuzzy environment. Eur. J. Oper. Res. 197 (2009) 620–631. [CrossRef] [Google Scholar]
  • J.C.P. Yu, Optimizing a two-warehouse system under shortage backordering, trade credit, and decreasing rental conditions. Int. J. Prod. Econ. 209 (2019) 147–155. [CrossRef] [Google Scholar]
  • Q. Zhang, W. Tang and J. Zhang, Green supply chain performance with cost learning and operational inefficiency effects. J. Cleaner Prod. 112 (2016) 3267–3284. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.