EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 59 / No 3 (May-June 2025) RAIRO-Oper. Res., 59 3 (2025) 1527-1549 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 3, May-June 2025
Page(s) 1527 - 1549
DOI https://doi.org/10.1051/ro/2024225
Published online 04 June 2025
  • W. Ahmed, M. Moazzam, B. Sarkar and S.U. Rehman, Synergic effect of reworking for imperfect quality items with the integration of multi-period delay-in-payment and partial backordering in global supply chains. Engineering 7 (2021) 260–271. [CrossRef] [MathSciNet] [Google Scholar]
  • B. Sarkar, S. Sao, S. and S.K. Ghosh, Smart production and photocatalytic ultraviolet (PUV) wastewater treatment effect on a textile supply chain management. Int. J. Prod. Econ. 283 (2025) 109557. [CrossRef] [Google Scholar]
  • U. Chaudhari, A. Bhadoriya, M.Y. Jani and B. Sarkar, A generalized payment policy for deteriorating items when demand depends on price, stock, and advertisement under carbon tax regulations. Math. Comput. Simul. 207 (2023) 556–574. [CrossRef] [Google Scholar]
  • P.S. Deng, Improved inventory models with ramp type demand and Weibull deterioration. Int. J. Inf. Manage. Sci. 16 (2005) 79–86. [Google Scholar]
  • P.S. Deng, R.H.J. Lin and P. Chu, A note on the inventory models for deteriorating items with ramp type demand rate. Eur. J. Oper. Res. 178 (2007) 112–120. [CrossRef] [Google Scholar]
  • R. Guchhait and B. Sarkar, Economic evaluation of an outsourced fourth-party logistics (4PL) under a flexible production system. Int. J. Prod. Econ. 279 (2025) 109440. [CrossRef] [Google Scholar]
  • B.K. Dey, S. Pareek, M. Tayyab and B. Sarkar, Autonomation policy to control work-in-process inventory in a smart production system. Int. J. Prod. Res. 59 (2021) 1258–1280. [Google Scholar]
  • C.Y. Dye, The effect of preservation technology investment on a non-instantaneous deteriorating inventory model. Omega – Int. J. Manage. Sci. 41 (2013) 872–880. [CrossRef] [Google Scholar]
  • C.Y. Dye and T.P. Hsieh, An optimal replenishment policy for deteriorating items with effective investment in preservation technology. Eur. J. Oper. Res. 218 (2012) 106–112. [Google Scholar]
  • A. Garai and B. Sarkar, Economically independent reverse logistics of customer-centric closed-loop supply chain for herbal medicines and biofuel. J. Clean. Prod. 334 (2022) 129977. [CrossRef] [Google Scholar]
  • P. Gautam, S. Maheshwari, A. Kausar and C.K. Jaggi, Optimal inventory replenishment policies for deteriorating items with preservation technology under the effect of advertisement and price reliant demand. Int. J. Syst. Sci. Oper. Logistics 10 (2023) 2186753. [Google Scholar]
  • B.C. Giri, A.K. Jalan and K.S. Chaudhuri, Economic order quantity model with Weibull deterioration distribution, shortage and ramp-type demand. Int. J. Syst. Sci. 34 (2003) 237–243. [Google Scholar]
  • A. Guria, B. Das, S. Mondal and M. Maiti, Inventory policy for an item with inflation induced purchasing price, selling price and demand with immediate part payment. Appl. Math. Modell. 37 (2013) 240–257. [CrossRef] [Google Scholar]
  • M.S. Habib, O. Asghar, A. Hussain, M. Imran, M.P. Mughal and B. Sarkar, A robust possibilistic programming approach toward animal fat-based biodiesel supply chain network design under uncertain environment. J. Clean. Prod. 278 (2021) 122403. [CrossRef] [Google Scholar]
  • M.S. Habib, M. Omair, M.B. Ramzan, T.N. Chaudhary, M. Farooq and B. Sarkar, A robust possibilistic flexible programming approach toward a resilient and cost-efficient biodiesel supply chain network. J. Clean. Prod. 366 (2022) 132752. [CrossRef] [Google Scholar]
  • R.M. Hill, Inventory models for increasing demand followed by level demand. J. Oper. Res. Soc. 46 (1995) 250–1259. [Google Scholar]
  • K.L. Hou, An inventory model for deteriorating items with stock-dependent consumption rate and shortages under inflation and time discounting. Eur. J. Oper. Res. 168 (2006) 463–474. [Google Scholar]
  • P.H. Hsu, H.M. Wee and H.M. Teng, Preservation technology investment for deteriorating inventory. Int. J. Prod. Econ. 124 (2010) 388–394. [Google Scholar]
  • A.-A. Khan, L.E.C. Barrón, G.T. Garza, A.C. Mota, I.D. Hernández and L. Jesús, Integrating prepayment installment, pricing and replenishment decisions for growing items with power demand pattern and non-linear holding cost under carbon regulations. Comput. Oper. Res. 156 (2023) 106225. [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. Math. 8 (2020) 1694–1702. [CrossRef] [Google Scholar]
  • S. Kumar, B. Sarkar and A. Kumar, Fuzzy reverse logistics inventory model of smart items with two warehouses of a retailer considering carbon emissions. RAIRO-Oper. Res. 55 (2021) 2285–2307. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • S. Kumar, M. Sigroha, K. Kumar and B. Sarkar, Manufacturing/remanufacturing based supply chain management under advertisements and carbon emissions process. RAIRO-Oper. Res. 56 (2022) 831–851. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • S. Kumar, A. Kumar, R. Guchchait and B. Sarkar, An environmental decision support system for manufacturer–retailer within a closed-loop supply chain management using remanufacturing. AIMS Environ. Sci. 10 (2023) 644–676. [CrossRef] [Google Scholar]
  • S. Kumar, B. Sarkar and M. Sarkar, Effect of green technology for a production system through a reverse logistic process. RAIRO-Oper. Res. 58 (2024) 2683–2707. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • Y.P. Lee and C.Y. Dye, An inventory model for deteriorating items under stock-dependent demand and controllable deterioration rate. Comput. Ind. Eng. 63 (2012) 474–482. [Google Scholar]
  • G. Li, X. He, J. Zhou and H. Wu, Pricing, replenishment and preservation technology investment decisions for non-instantaneous deteriorating items. Omega – Int. J. Manage. Sci. 84 (2019) 114–126. [CrossRef] [Google Scholar]
  • G. Liu, J. Zhang and W. Tang, Joint dynamic pricing and investment strategy for perishable foods with price-quality dependent demand. Ann. Oper. Res. 226 (2015) 397–416. [Google Scholar]
  • L. Liu, L. Zhao and X. Ren, Optimal preservation technology investment and pricing policy for fresh food. Comput. Ind. Eng. 135 (2019) 746–756. [CrossRef] [Google Scholar]
  • S. Maheshwari, P. Gautam, A. Kausar and C.K. Jaggi, Sustainable retail model with preservation technology investment to moderate deterioration with environmental deliberations. J. Clean. Prod. 390 (2023) 136128. [CrossRef] [Google Scholar]
  • B. Sarkar, A.S.H. Kugele and M. Sarkar, Two non-linear programming models for the multi-stage multi-cycle smart production system with autonomation and remanufacturing in same and different cycles to reduce wastes. J. Indust. Inform. Integ. 44 (2025) 100749. [Google Scholar]
  • B. Mandal and A.K. Pal, Order level inventory system with ramp type demand rate for deteriorating items. J. Interdiscipl. Math. 1 (1998) 49–66. [CrossRef] [Google Scholar]
  • S.K. Manna and K.S. Chaudhuri, An EOQ model with ramp type demand rate, time dependent deterioration rate, unit production cost and shortages. Eur. J. Oper. Res. 171 (2006) 557–566. [CrossRef] [Google Scholar]
  • A.H.M. Mashud, H.M. Wee, C.V. Huang and J.Z. Wu, Optimal replenishment policy for deteriorating products in a newsboy problem with multiple just-in-time deliveries. Mathematics 8 (2020) 1981. [CrossRef] [Google Scholar]
  • A.H.M. Mashud, M.R. Hasan, Y. Daryanto and H.M. Wee, A resilient hybrid payment supply chain inventory model for post COVID-19 recovery. Comput. Ind. Eng. 157 (2021a) 107249. [CrossRef] [Google Scholar]
  • A.H.M. Mashud, D. Roy, Y. Daryanto, K.R. Chakrabortty and M.L. Tseng, A sustainable inventory model with controllable carbon emissions, deterioration and advance payments. J. Clean. Prod. 296 (2021b) 126608. [CrossRef] [Google Scholar]
  • U. Mishra, L.E. Cárdenas-Barrón, S. Tiwari, A.A. Shaikh and G. Trevi˜no-Garza, An inventory model under price and stock dependent demand for controllable deterioration rate with shortages and preservation technology investment. Ann. Oper. Res. 254 (2017) 165–190. [CrossRef] [MathSciNet] [Google Scholar]
  • U. Mishra, J. Tijerina-Aguilera, S. Tiwari and L.E. Cárdenas-Barrón, Retailer’s joint ordering, pricing, and preservation technology investment policies for a deteriorating item under permissible delay in payments. Math. Prob. Eng. 2018 (2018) 6962417. [CrossRef] [Google Scholar]
  • U. Mishra, A.H.M. Mashud, M.-L. Tseng and J.Z. Wu, Optimizing a sustainable supply chain inventory model for controllable deterioration and emission rates in a greenhouse farm. Mathematics 9 (2021) 495. [CrossRef] [Google Scholar]
  • I. Moon, W.Y. Yun and B. Sarkar, Effects of variable setup cost, reliability, and production costs under controlled carbon emissions in a reliable production system, Eur. J. Ind. Eng. 16 (2022) 371–397. [CrossRef] [Google Scholar]
  • R. Guchhait, and B. Sarkar, A decision-making problem for product outsourcing with flexible production under a global supply chain management. Int. J. Prod. Econ. 272 (2024) 109230. [CrossRef] [Google Scholar]
  • B. Oryani, A. Moridian, B. Sarkar, S. Rezania, H. Kamyab and M.K. Khan, Assessing the financial resource curse hypothesis in Iran: the novel dynamic ARDL approach. Res. Policy 78 (2022) 102899. [CrossRef] [Google Scholar]
  • S. Pal, G.S. Mahapatra and G.P. Samanta, A production inventory model for deteriorating item with ramp type demand allowing inflation and shortages under fuzziness. Econ. Model. 46 (2015) 334–345. [CrossRef] [Google Scholar]
  • S. Panda, S. Saha and M. Basu, An EOQ model with generalized ramp-type demand and Weibull distribution deterioration. Asia–Pac. J. Oper. Res. 24 (2007) 93–109. [CrossRef] [MathSciNet] [Google Scholar]
  • S. Panda, S. Senapati and M. Basu, Optimal replenishment policy for perishable seasonal products in a season with ramp-type time dependent demand. Comput. Ind. Eng. 54 (2008) 301–314. [CrossRef] [Google Scholar]
  • M. Rinaldi, M.A. Turino, M. Fera and R. Macchiaroli, Improving the distribution of COVID-19 vaccines using the blockchain technology: the Italian case study. Proc. Comput. Sci. 217 (2023) 366–375. [CrossRef] [Google Scholar]
  • M. Sarkar and B. Sarkar, How does an industry reduce waste and consumed energy within a multi-stage smart sustainable biofuel production system? J. Clean. Prod. 262 (2020) 121200. [CrossRef] [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, B. Mridha and S. Pareek, A sustainable smart multi-type biofuel manufacturing with the optimum energy utilization under flexible production. J. Clean. Prod. 332 (2022) 129869. [CrossRef] [Google Scholar]
  • B. Sarkar, M. Omair and N. Kim, A cooperative advertising collaboration policy in supply chain management under uncertain conditions. Appl. Soft Comput. 88 (2020) 105948. [CrossRef] [Google Scholar]
  • B. Sarkar, A. Debnath, A.S. Chiu and W. Ahmed, Circular economy-driven two-stage supply chain management for nullifying waste. J. Clean. Prod. 339 (2022) 130513. [CrossRef] [Google Scholar]
  • B. Sarkar, B.K. Dey, M. Sarkar and S.J. Kim, A smart production system with an autonomation technology and dual channel retailing. Comput. Ind. Eng. 173 (2022) 108607. [CrossRef] [Google Scholar]
  • B. Sarkar, B. Ganguly, S. Pareek and L.E. Cárdenas-Barrón, A three-echelon green supply chain management for biodegradable products with three transportation modes. Comput. Ind. Eng. 174 (2022) 108727. [CrossRef] [Google Scholar]
  • B. Sarkar, D. Takeyeva, R. Guchhait and M. Sarkar, Optimized radio-frequency identification system for different warehouse shapes. Knowl.-Based Syst. 258 (2022) 109811. [CrossRef] [Google Scholar]
  • A.S. Mahapatra, M.S. Mahapatra, B. Sarkar and S.K. Majumder, Benefit of preservation technology with promotion and time-dependent deterioration under fuzzy learning. Expert Syst. App. 201 (2022) 117169. [CrossRef] [Google Scholar]
  • A. Datta, B.K. Dey, S. Bhuniya, I. Sangal, B. Mandal, M. Sarkar, R. Guchhait, B. Sarkar and B. Ganguly, Adaptation of e-commerce retailing to enhance customer satisfaction within a dynamical system under transfer of risk. J. Retail. Consum. Serv. 84 (2025) 104129. [CrossRef] [Google Scholar]
  • A. Sepehri and M.R. Gholamian, Joint pricing and lot-sizing for a production model under controllable deterioration and carbon emissions. Int. J. Syst. Sci. Oper. Logistics 9 (2022) 324–338. [Google Scholar]
  • S. Kumar, M. Sigroha, N. Kumar, M. Kumari and B. Sarkar, How does the retail price maintain trade-credit management with continuous investment to support the cash flow? J. Retail. Consum. Serv. 83 (2025) 104116. [CrossRef] [Google Scholar]
  • N.H. Shah, U. Chaudhari and M.Y. Jani, Optimal control analysis for service, inventory and preservation technology investment. Int. J. Syst. Sci. Oper. Logistics 6 (2019) 130–142. [Google Scholar]
  • K. Skouri, I. Konstantaras, S. Papachristos and I. Ganas, Inventory models with ramp type demand rate, partial backlogging and Weibull deterioration rate. Eur. J. Oper. Res. 192 (2009) 79–92. [Google Scholar]
  • K. Skouri, I. Konstantaras, S. Papachristos and J.T. Teng, Supply chain models for deteriorating products with ramp type demand rate under permissible delay in payments. Expert Syst. App. 38 (2011) 14861–14869. [CrossRef] [Google Scholar]
  • M. Tayyab and B. Sarkar, An interactive fuzzy programming approach for a sustainable supplier selection under textile supply chain management. Comput. Ind. Eng. 155 (2021) 107164. [CrossRef] [Google Scholar]
  • M. Tayyab, J. Jemai, H. Lim and B. Sarkar, A sustainable development framework for a cleaner multi-item multi-stage textile production system with a process improvement initiative. J. Clean. Prod. 246 (2020) 119055. [CrossRef] [Google Scholar]
  • Y.C. Tsao, Joint location, inventory, and preservation decisions for non-instantaneous deterioration items under delay in payments. Int. J. Syst. Sci. 47 (2016) 572–585. [Google Scholar]
  • A.S. Mahapatra, S. Sengupta, A. Dasgupta, B. Sarkar and R.T. Goswami, What is the impact of demand patterns on integrated online-offline and buy-online-pickup in-store (BOPS) retail in a smart supply chain management? J. Retail. Consum. Serv. 82 (2025) 104093. [CrossRef] [Google Scholar]
  • K.S. Wu, An EOQ inventory model for items with Weibull distribution deterioration, ramp type demand rate and partial backlogging. Prod. Planning Control 12 (2001) 787–793. [CrossRef] [Google Scholar]
  • K.S. Wu and L.Y. Ouyang, A replenishment policy for deteriorating items with ramp type demand rate. Proc. Nat. Sci. Council ROC (A) 24 (2000) 279–286. [Google Scholar]
  • J.W. Wu, C. Lin, B. Tan and W.C. Lee, An EOQ model with ramp type demand rate for items with Weibull deterioration. Int. J. Inf. Manage. Sci. 10 (2000) 41–51. [Google Scholar]
  • A. Sarkar, R. Guchhait and B. Sarkar, B. Application of the artificial neural network with multithreading within an inventory model under uncertainty and inflation. Int. J. Fuzzy Syst. 24 (2022) 2318–2332. [CrossRef] [Google Scholar]
  • D. Yadav, R. Singh, A. Kumar and B. Sarkar, Reduction of pollution through sustainable and flexible production by controlling by-products. J. Environ. Inf. 40 (2022) 106–124. [Google Scholar]
  • C.T. Yang, C.Y. Dye and J.F. Ding, Optimal dynamic trade credit and preservation technology allocation for a deteriorating inventory model. Comput. Ind. Eng. 87 (2015) 356–369. [Google Scholar]
  • J. Zhang, Z. Bai and W. Tang, Optimal pricing policy for deteriorating items with preservation technology investment. J. Ind. Manage. Optim. 10 (2014) 1261–1277. [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.