The capacity of a firm to accomplish its goals is financially compromised by degeneration of goods. A suitable preservation strategy to reduce degradation is a vital part of the managerial decisions. This study employs preservation technologies under uncertain demand to frame a continuous review inventory model with full back-ordering and the influence of promotional efforts. Survey of existing research finds few models with synchronised optimization over this entire scenario with all factors.The best values of the preservation cost and the two fractions of the cycle period when inventory is kept against the backorder part are determined to lower the total average cost. A mathematical model is built to incorporate these elements and numerical scenarios are presented to compare three possible approaches. In both crisp and fuzzy contexts, the sensitivity of the solution and decision variables concerning various inventory characteristics is investigated. Backorder duration is inversely proportional to the presence of preservation. The coefficient of preservation has a tipping point below which accepting the impact of undamped deterioration becomes more cost-effective. The total cost at the optimal point is more elastic to a reduction in base deterioration rate and relatively inelastic to its increase. Finally, this study proves that the preservation strategy converges over deterioration for the crisp case rather than the fuzzy case. It is expected the fuzzy case can provide better results, however, the crisp case provides lower total cost than the fuzzy case though it is slightly less efficient in per unit cost.
Accepté le :
Première publication :
Publié le :
DOI : 10.1051/ro/2022145
Keywords: Continuous review inventory, deterioration, preservation investment, backlogging, uncertain demand
@article{RO_2022__56_6_4251_0,
author = {Mahapatra, Amalendu Singha and Dasgupta, Arup and Shaw, Ashok Kumar and Sarkar, Biswajit},
title = {An inventory model with uncertain demand under preservation strategy for deteriorating items},
journal = {RAIRO. Operations Research},
pages = {4251--4280},
year = {2022},
publisher = {EDP-Sciences},
volume = {56},
number = {6},
doi = {10.1051/ro/2022145},
mrnumber = {4523952},
zbl = {1532.90005},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2022145/}
}
TY - JOUR AU - Mahapatra, Amalendu Singha AU - Dasgupta, Arup AU - Shaw, Ashok Kumar AU - Sarkar, Biswajit TI - An inventory model with uncertain demand under preservation strategy for deteriorating items JO - RAIRO. Operations Research PY - 2022 SP - 4251 EP - 4280 VL - 56 IS - 6 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2022145/ DO - 10.1051/ro/2022145 LA - en ID - RO_2022__56_6_4251_0 ER -
%0 Journal Article %A Mahapatra, Amalendu Singha %A Dasgupta, Arup %A Shaw, Ashok Kumar %A Sarkar, Biswajit %T An inventory model with uncertain demand under preservation strategy for deteriorating items %J RAIRO. Operations Research %D 2022 %P 4251-4280 %V 56 %N 6 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2022145/ %R 10.1051/ro/2022145 %G en %F RO_2022__56_6_4251_0
Mahapatra, Amalendu Singha; Dasgupta, Arup; Shaw, Ashok Kumar; Sarkar, Biswajit. An inventory model with uncertain demand under preservation strategy for deteriorating items. RAIRO. Operations Research, Tome 56 (2022) no. 6, pp. 4251-4280. doi: 10.1051/ro/2022145
[1] , , and , Back-ordered inventory model with inflation in a cloudy-fuzzy environment. J. Ind. Manag. Optim. 17 (2021) 1913. | MR | Zbl | DOI
[2] and , Convex Optimization. Cambridge University Press, New York (2004). | MR | Zbl | DOI
[3] , , , and , An inventory model for non-instantaneous deteriorating items with preservation technology and multiple credit periods-based trade credit financing via particle swarm optimization. Soft Comput. 25 (2021) 5365–5384. | Zbl | DOI
[4] and , A profit jump inventory model for imperfect quality items with receiving reparative batch and order overlapping in dense fuzzy environment. Revue d’Orthopdie Dento-Faciale 55 (2021). | MR | Zbl | Numdam
[5] and , An optimal replenishment policy for deteriorating items with effective investment in preservation technology. Eur. J. Oper. Res. 218 (2012) 106–112. | MR | Zbl | DOI
[6] , and , Fully fuzzy inventory model with price-dependent demand and time varying holding cost under fuzzy decision variables. J. Intell. Fuzzy Syst. 36 (2019) 3725–3738.
[7] , and , An EOQ model with fuzzy demand and learning in fuzziness. Int. J. Serv. Oper. Manag. 12 (2012) 90–100.
[8] , , , , and , A robust possibilistic flexible programming approach toward a resilient and cost-efficient biodiesel supply chain network, J. Clean. Prod. 366 (2022) 132752. | DOI
[9] , and , Fuzzification of EOQ model under the condition of permissible delay in payments. Int. J. Strateg. Decis. Sci. 3 (2012) 1–19. | DOI
[10] , , and , Optimal investment in preservation technology for variable demand under trade-credit and shortages. Mathematics 9 (2021) 1301. | DOI
[11] , , , and , The effect of advance payment with discount facility on supply decisions of deteriorating products whose demand is both price and stock dependent. Int. Trans. Oper. Res. 27 (2020) 1343–1367. | MR | Zbl | DOI
[12] and , Cost optimization inventory model for deteriorating items with trapezoidal demand rate under completely backlogged shortages in crisp and fuzzy environment. RAIRO: OR 56 (2022) 1969–1994. | MR | Numdam | DOI
[13] , , and , Manufacturing/remanufacturing based supply chain management under advertisements and carbon emission process. RAIRO: OR (2022). | MR | Zbl | Numdam
[14] , , , , and , Optimal ordering policy in an economic order quantity (EOQ) model for non-instantaneous deteriorating items with defective quality and permissible delay in payments. Rev. Real Acad. Cienc. Exactas Fis. Nat. Ser. A: Mat. 114 (2020) 1–26. | MR | Zbl
[15] , , , and , Development of a fuzzy economic order quantity model of deteriorating items with promotional effort and learning in fuzziness with a finite time horizon. Inventions 4 (2019) 36. | DOI
[16] , , and , Benefit of preservation technology with promotion and time-dependent deterioration under fuzzy learning. Expert Syst. Appl. 201 (2022) 117169. | DOI
[17] and , Fuzzy inventory models for items with imperfect quality and shortage backordering under crisp and fuzzy decision variables. Comput. Ind. Eng. 64 (2013) 190–199. | DOI
[18] , , , and , 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. | MR | Zbl | DOI
[19] , and , 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. | DOI
[20] , and , Proposing a discount policy for two-level supply chain coordination with periodic review replenishment and promotional efforts decisions. Oper. Res. 21 (2021) 365–398.
[21] , , , , and , Assessing the financial resoure curse hypothesis in Iran: The novel dynamic ARDL approach. Resour. Pol. 78 (2022) 102899. | DOI
[22] , and , An EOQ model for deteriorating items under trade credits. J. Oper. Res. Soc. 56 (2005) 719–726. | Zbl | DOI
[23] , Optimal pricing and offering reward decisions in a competitive closed-loop dual-channel supply chain with recycling and remanufacturing. RAIRO: OR 56 (2022) 1763–1780. | MR | Zbl | Numdam | DOI
[24] , and , An integrated inventory model with variable holding cost under two levels of trade-credit policy. Numer. Algebra, Control Optim. 8 (2018) 169. | MR | Zbl | DOI
[25] , and , Multi-item deteriorating two-echelon inventory model with price-and stock-dependent demand: A trade-credit policy. J. Ind. Manag. Optim. 15 (2019) 1345. | MR | Zbl | DOI
[26] , and , Deteriorating inventory with preservation technology under price and stock-sensitive demand. J. Ind. Manag. Optim. 16 (2020) 1585. | MR | Zbl | DOI
[27] , and , An integrated vendor-buyer model with quadratic demand under inspection policy and preservation technology. Hacet. J. Math. Stat. 49 (2020) 1168–1189. | MR | Zbl | DOI
[28] , , and , An inventory model under price and stock dependent demand for controllable deterioration rate with shortages and preservation technology investment: revisited. Opsearch 58 (2021) 181–202. | MR | DOI
[29] , and , Optimal inventory strategies for deteriorating items with price-sensitive investment in preservation technology. RAIRO: OR 56 (2022) 601–617. | MR | Zbl | Numdam | DOI
[30] , and , A two-warehouse probabilistic model with price discount on backorders under two levels of trade-credit policy. J. Ind. Manag. Optim. 16 (2020) 553. | MR | Zbl | DOI
[31] , and , Imperfection with inspection policy and variable demand under trade-credit: A deteriorating inventory model. Numer. Algebra, Control Optim. 10 (2020) 45. | MR | Zbl | DOI
[32] , and , Strategic inventory and pricing decision for substitutable products. Comput. Ind. Eng. 160 (2021) 107570. | DOI
[33] , , and , Continuous review inventory model with delay in payments. Int. J. Prod. Econ. 85 (2003) 91–95. | DOI
[34] , , and , A smart production system with an autonomation technology and dual channel retailing, Comp. Indust. Eng. 173 (2022) 108607. | DOI
[35] , , and , A three-echelon green supply chain management for biodegradable products with three transportation modes. Comp. Ind. Eng. 174 (2022) 108727. | DOI
[36] , and , Application of the artificial neural network with multithreading within an inventory model under uncertainty and inflation. Int. J. Fuzzy Syst. 24 (2022) 1–15. | DOI
[37] , , , and , Controlling defective items in a complex multi-phase manufacturing system. RAIRO: OR 56 (2022). | MR | Zbl | Numdam | DOI
[38] , , and , A sustainable managerial decision-making problem for a substitutable product in a dual-channel under carbon tax policy. Comp. Ind. Eng. 172 (2022) 108635. | DOI
[39] and , Continuous review inventory model with fuzzy stochastic demand and variable lead time. Int. J. Appl. Ind. Eng. 1 (2012) 7–24.
[40] and , Reducing the deterioration rate of inventory through preservation technology investment under fuzzy and cloud fuzzy environment. in Predictive Analytics, Edited by and . CRC Press, Boca Raton (2021) 65–80. | DOI
[41] , and , EPQ model to price-sensitive stock dependent demand with carbon emission under green and preservation technology investment. Econ. Comput. Econ. Cybern. Stud. Res. 56 (2022).
[42] , and , Inventory and preservation investment for deteriorating system with stock-dependent demand and partial backlogged shortages. Yugosl. J. Oper. Res. 31 (2021) 181–192. | MR | DOI
[43] , and , Greening efforts and deteriorating inventory policies for price-sensitive stock-dependent demand, Int. J. Syst. Sci.: Oper. Logist. (2022) 1–7.
[44] , , and , Economic order quantity model for deteriorating item with preservation technology in time dependent demand with partial backlogging and trade credit. Int. J. Logist. Syst. Manag. 32 (2019) 1.
[45] and , An analytical approach for EOQ determination using trapezoidal fuzzy function. Int. J. Procure. Manag. 11 (2018) 356–369.
[46] and , Joint pricing and replenishment policies for non-instantaneous deteriorating items with imprecise deterioration free time. Comput. Ind. Eng. 66 (2013) 944–951. | DOI
[47] and , Pricing and inventory decisions for non-instantaneous deteriorating items with price and promotional effort stochastic demand. J. Control. Decis. 6 (2019) 191–215. | MR | DOI
[48] and , Analysis and optimization of an EOQ inventory model with promotional efforts and back ordering under delay in payments. J. Manag. Anal. 4 (2017) 159–181.
[49] , , and , An EOQ model with partial delayed payment and partial backordering. Omega 41 (2013) 354–368. | DOI
[50] , , and , Optimal replenishment policy for a deteriorating green product: Life cycle costing analysis. Int. J. Prod. Econ. 133 (2011) 603–611. | DOI
[51] , , and , Reduction of pollution through sustainable and flexible production by controlling by-products. J. Environ. Inf. 40 (2022) 106–124.
[52] , and , Fuzzy inventory without backorder for fuzzy order quantity and fuzzy total demand quantity. Comput. Oper. Res. 27 (2000) 935–962. | Zbl | DOI
[53] , Fuzzy sets. Inf. Control 8 (1965) 338–353. | MR | Zbl | DOI
[54] , , and , Optimal dynamic pricing and replenishment cycle for non-instantaneous deterioration items with inventory-level-dependent demand. Int. J. Prod. Econ. 170 (2015) 136–145. | DOI
[55] , and , Deep reinforcement learning approach for solving joint pricing and inventory problem with reference price effects. Expert Syst. Appl. 195 (2022) 116564. | DOI
Cité par Sources :
