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All issues Volume 59 / No 4 (July-August 2025) RAIRO-Oper. Res., 59 4 (2025) 1865-1897 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 4, July-August 2025
Page(s) 1865 - 1897
DOI https://doi.org/10.1051/ro/2025044
Published online 23 July 2025
  • M. Abuhelwa, W.A. Salah and M.J.K. Bashir, Potential energy production from organic waste and its environmental and economic impacts at a tertiary institution in Palestine. Environ. Q. Manage. 32 (2023) 167–177. [Google Scholar]
  • A.A. Alamri, A sustainable closed loop supply chains inventory model considering optimal number of remanufacturing times. Sustainability 15 (2023) 9517. [CrossRef] [Google Scholar]
  • A.A. Asha, M. Dulal and A. Habib, The influence of sustainable supply chain management technology orientation and organizational culture on the delivery product quality customer satisfaction nexus. Clean. Logistics Supply Chain 7 (2023) 100107. [Google Scholar]
  • S. Banerjee and S. Agrawal, Inventory model for deteriorating items with freshness and price dependent demand: Optimal discounting and ordering policies. Appl. Math. Model. 52 (2017) 53–64. [CrossRef] [MathSciNet] [Google Scholar]
  • H. Barman, M. Pervin, S.K. Roy and G.W. Weber, Back-ordered inventory model with inflation in a cloudy fuzzy environment. J. Ind. Manage. Optim. 17 (2021) 1913–1941. [CrossRef] [Google Scholar]
  • P. Becerra, J. Mula and R. Sanchis, Green supply chain quantitative models for sustainable inventory management: A review. J. Clean. Prod. 328 (2021) 129544. [Google Scholar]
  • M. Ben-Daya, R. As’ad and K.A. Nabi, A single-vendor multi-buyer production remanufacturing inventory system under a centralized consignment arrangement. Comput. Ind. Eng. 135 (2019) 10–27. [CrossRef] [Google Scholar]
  • A.K. Bhunia, A.A. Shaikh and R.K. Gupta, A study on two-warehouses partially backlogged deteriorating inventory models under inflation via particle swarm optimization. Int. J. Syst. Sci. 46 (2013) 1036–1050. [Google Scholar]
  • E. Cakmak and E. Guney, Spare parts inventory classification using neutrosophic fuzzy EDAS method in the aviation industry. Expert Syst. App. 224 (2023) 120008. [Google Scholar]
  • N. Chauhan and A.S. Yadav, An inventory model for deteriorating items with two-warehouse and dependent demand using genetic algorithm. Int. J. Adv. Sci. Technol. 29 (2020) 1152–1162. [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]
  • M. Forkan, M.M. Rizvi and M.A.M. Chowdhury, Multi-objective reverse logistics model for inventory management with environment impact: An application in industry. Intell. Syst. App. 14 (2022) 200078. [Google Scholar]
  • C. Fu, Y.Q. Liu and M. Shan, Drivers of low-carbon practices in green supply chain management in construction industry: An empirical study in China. J. Clean. Prod. 428 (2023) 139497. [Google Scholar]
  • P. Gautam, S. Maheshwari, 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.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.K. Jaggi, S. Pareek, N. Sharma and Nidhi, Fuzzy inventory model for deteriorating items with time-varying demand and shortage. Am. J. Oper. Res. 2 (2012) 81–92. [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]
  • W.A. Jauhari, N.H. Shah and C.N. Rosyidi, A closed loop supply chain model with rework waste disposal and carbon emission. Oper. Res. Perspect. 7 (2020) 100155. [MathSciNet] [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]
  • M. Kerin and D.T. Pham, A review of emerging industry 4.0 technologies in remanufacturing. J. Clean. Prod. 237 (2019) 117805. [CrossRef] [Google Scholar]
  • A.S. Mahapatra, S. Sengupta, A. Dasgupta, B. Sarkar ans 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]
  • B. Sarkar and S. Bhuniya, A sustainable flexible manufacturing-remanufacturing model with improved service and green investment under variable demand. Exp. Syst. App. 202 (2022) 117154. [CrossRef] [Google Scholar]
  • G. Li, X. He, J. Zhou and H. Wu, Pricing, replenishment and preservation technology investment decisions for non-instantaneous deteriorating items. Omega 84 (2019) 114–126. [CrossRef] [Google Scholar]
  • S.S. Lin, A note on parallel machine scheduling with controllable processing times and job dependent learning effects. RAIRO Oper. Res. 55 (2021) 561–569. [Google Scholar]
  • Y.W. Lok, S.S. Supadi and K.B. Wong, Optimal investment in preservation technology for non-instantaneous deteriorating items under carbon emissions consideration. Comput. Ind. Eng. 183 (2023) 109446. [CrossRef] [Google Scholar]
  • X. Ma, Y. Zhao, Q. Luo and Q. Bai, Preservation technology investment and carbon abatement strategies in a supplier–retailer cold chain based on a differential game. Comput. Ind. Eng. 172 (2022) 108540. [Google Scholar]
  • M. Tayyab and B. Sarkar, An interactive fuzzy programming approach for a sustainable supplier selection under textile supply chain management. Comput. Indust. Eng. 155 (2021) 107164. [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]
  • 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. Pal, A. Sarkar and B. Sarkar, Optimal decisions in a dual-channel competitive green supply chain management under promotional effort. Exp. Syst. App. 211 (2023) 118315. [Google Scholar]
  • S.V.S. Padiyar, V.C. Kuraie, N. Bhagat, S.R. Singh and R. Chaudhar, An integrated inventory model for imperfect production process having preservation facilities under fuzzy and inflationary environment. Int. J. Math. Model. Numer. Optim. 12 (2022) 252–286. [Google Scholar]
  • S.V.S. Padiyar, V.C. Kuraie, N. Bhagat, S.R. Singh and H. Dem, Green Integrated model for imperfect production process under reliability. J. Math. Control Sci. App. 8 (2022) 1–13. [Google Scholar]
  • S.V.S. Padiyar, N. Bhagat and N. Punetha, A multistage sustainable inventory model with backorder, fuzzy parameters, and decision variables for deteriorating items with imperfect production and reliability. Int. J. Appl. Decis. Sci. 16 (2023) 445–473. [Google Scholar]
  • S.V.S. Padiyar, N. Bhagat, S.R. Singh and N. Punetha, Multi echelon fuzzy inventory model for perishable items in a supply chain with imperfect production and exponential demand rate. Int. J. Process Manage. Benchmarking 14 (2023) 23–28. [Google Scholar]
  • S.V.S. Padiyar, N. Bhagat, S.R. Singh, N. Punetha and H. Dem, Production policy for an inventory system under cloudy fuzzy environment. Int. J. Appl. Decis. Sci. 16 (2023) 255–299. [Google Scholar]
  • M. Rahaman, S.P. Mondal, S. Alam and S.K. De, A study of a lock fuzzy EPQ model with deterioration and stock and unit selling price-dependent demand using preservation technology. Soft Comput. 26 (2022) 2721–2740. [CrossRef] [Google Scholar]
  • M. Rahmani, A. Romsdal, F. Sgarbossa, J.O. Strandhagen and M. Holm, Towards smart production planning and control: A conceptual framework linking planning environment characteristics with the need for smart production planning and control. Ann. Rev. Control 53 (2022) 370–381. [CrossRef] [Google Scholar]
  • B. Sarkar, M. Omair and N. Kim, A cooperative advertising collaboration policy in supply chain management under uncertain conditions. App. Soft Comput. 88 (2020) 105948. [CrossRef] [Google Scholar]
  • C. Rout, A. Paul, R.S. Kumar, D. Chakraborty and A. Goswami, Cooperative sustainable supply chain for deteriorating item and imperfect production under different carbon emission regulations. J. Clean. Prod. 272 (2020) 122170. [CrossRef] [Google Scholar]
  • C. Saha, D.K. Jana and A. Duary, Enhancing production inventory management for imperfect items using fuzzy optimization strategies and differential evolution (DE) algorithms. Franklin Open 5 (2023) 100051. [CrossRef] [Google Scholar]
  • A. Garai and B. Sarkar, B. 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]
  • L.A. San-José, J. Sicilia, L.E. Cárdenas-Barrón and M. González-de-la-Rosa, A sustainable inventory model for deteriorating items with power demand and full backlogging under a carbon emission tax. Int. J. Prod. Econ. 268 (2024) 109098. [Google Scholar]
  • M. Sarkar and B.D. Chung, Flexible work-in-process production system in supply chain management under quality improvement. Int. J. Prod. Res. 58 (2020) 3821–3838. [CrossRef] [Google Scholar]
  • B. Sarkar and R. Guchhait, Ramification of information asymmetry on a green supply chain management with the cap-trade, service, and vendor-managed inventory strategies. Electron. Commer. Res. App. 60 (2023) 101274. [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. Sarkar, S. Sao 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]
  • 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 129869. [Google Scholar]
  • M. Sebatjane, The impact of preservation technology investments on lot-sizing and shipment strategies in a three-echelon food supply chain involving growing and deteriorating items. Oper. Res. Perspect. 9 (2022) 100241. [Google Scholar]
  • D. Sharmila and R. Uthayakumar, Inventory model for deterioration items involving fuzzy with shortage and exponential demand. Int. J. Supply Oper. Manage. 2 (2015) 888–904. [Google Scholar]
  • S. Sindhuja and P. Arathi, An inventory model for deteriorating products under preseravation technology with time dependent quality demand. Cogent Eng. 10 (2023) 2176968. [Google Scholar]
  • C. Singh and S.R. Singh, Two echelon supply chain model with imperfect production, for weibull distribution deteriorating items under imprecise and inflationary. Int. J. Oper. Res. Optim. 1 (2010) 9–25. [Google Scholar]
  • S.R. Singh and C. Singh, Imperfect production process with exponential demand rate, wiebull deterioration under inflation. Int. J. Oper. Res. 12 (2011) 430–444. [Google Scholar]
  • S.R. Singh, S. Tayal and A. Gaur, Replenishment policy for deteriorating items with trade credit and allowable shortages under inflationary environment. Int. J. Process Manage. Benchmarking 10 (2020) 462–478. [CrossRef] [Google Scholar]
  • S.K. Singh, A. Chauhan and B. Sarkar, Sustainable biodiesel supply chain model based on waste animal fat with subsidy and advertisement. J. Cleaner Prod. 382 (2023) 134806. [CrossRef] [Google Scholar]
  • W. Sun and Y. Su, Analysing green forward reverse logistics with NSGA-II. Sustainability 12 (2020) 6082. [Google Scholar]
  • M. Taheri, M.S. Amalnick, A.A. Taleizadeh and E. Mardan, A fuzzy programming model for optimizing the inventory management problem considering financial issues: A case study of the dairy industry. Expert Syst. App. 221 (2023) 119766. [Google Scholar]
  • A.A. Taleizadeh, H.M. Wee and F. Jolai, Revisiting a fuzzy rough economic order quantity model for deteriorating items considering quantity discount and prepayment. Math. Comput. Model. 57 (2013) 1466–1479. [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. Journal of Indust. Inform. Integ. 44 (2025) 100749. [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. Euro. J. Indust. Eng. 16 (2022) 371–397. [Google Scholar]
  • N. Zhang, S. Tian, J. Xu, Y. Deng and K. Cai, Optimal production lot-sizing and condition-based maintenance policy considering imperfect manufacturing process and inspection errors. Comput. Ind. Eng. 177 (2023) 108929. [Google Scholar]
  • X. Zhuang, X. Song, H. Yang, D. Zhao, W. Wu and J. Zhao, The energy saving and GHG mitigation benefits assessment of product remanufacturing from a multi-life cycle perspective: A case study of spark plug. Environ. Impact Assess. Rev. 99 (2023) 107018. [Google Scholar]

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