International Transactions in Mathematical Sciences and Computer
8:10:45
 
 
More....

International Transactions in Mathematical Sciences and ComputerJuly-Dec 2024 Vol:17 Issue:2

EPQ model for deteriorating items with carbon emission having multivariate demand considering freshness under shortages

Abstract

There is a growing health consciousness among consumers and they are paying attention to the issue of greenhouse gas emissions, one of the major environmental threats. That's why companies are more and more eager to use green components. Currently, technological advances are causing markets to become more volatile and consumer preferences to change more rapidly. Thus, a product's life cycle is shortening every day, affecting its demolition rate. There are several factors which can influence its demand, including the price, on-hand inventory, and the freshness of the product. On the other hand, perishable items are prone to causing damage to the atmosphere if not handled properly. Therefore, the handling of deteriorated items must be done in an effective and efficient manner to prevent environmental hazards. The purpose of this research is therefore to develop a model for inventory management involving perishable items that is constrained both by physical degradations as well as freshness degradations. Price, stock availability, and the life cycle of products all affect the demand for the items. In this model we have used the linear price, time, and stock and freshness dependent demand. Shortage is taken into consideration, which is fully backlogged. Carbon emission has been considered to be minimized by using the carbon tax.

Author

Jitendra Kumar  ( Pages 249-271 )
Email:jitte.dm@gmail.com
Affiliation: Department of Mathematics, Marwari College, L.N. Mithila University, Darbhanga, Bihar      DOI:

Keyword

EPQ model, carbon emission, multivariate demand, shortages

References

Agi, M. A., & Soni, H. N. (2020). Joint pricing and inventory decisions for perishable products with age-, stock-, and price-dependent demand rate. Journal of the Operational Research Society, 71(1), 85-99.

Alfares, H. K., & Ghaithan, A. M. (2019). EOQ and EPQ production-inventory models with variable holding cost: State-of-the-art review. Arabian Journal for Science and Engineering, 44(3), 1737-1755.

Avinadav, T., Herbon, A., & Spiegel, U. (2014). Optimal ordering and pricing policy for demand functions that are separable into price and inventory age. International Journal of Production Economics, 155, 406-417.

Banerjee, S., & Agrawal, S. (2017). Inventory model for deteriorating items with freshness and price dependent demand: optimal discounting and ordering policies. Applied Mathematical Modelling, 52, 53-64.

Chen, S. C., Min, J., Teng, J. T., & Li, F. (2016). Inventory and shelf-space optimization for fresh produce with expiration date under freshness-and-stock-dependent demand rate. Journal of the Operational Research Society, 67(6), 884-896.

Dobson, G., Pinker, E. J., & Yildiz, O. (2017). An EOQ model for perishable goods with age-dependent demand rate. European Journal of Operational Research, 257(1), 84-88.

Feng, L., Chan, Y. L., & Cárdenas-Barrón, L. E. (2017). Pricing and lot-sizing polices for perishable goods when the demand depends on selling price, displayed stocks, and expiration date. International Journal of Production Economics, 185, 11-20.

Herbon, A., & Ceder, A. (2018). Monitoring perishable inventory using quality status and predicting automatic devices under various stochastic environmental scenarios. Journal of Food Engineering, 223, 236-247.

Herbon, A., & Khmelnitsky, E. (2017). Optimal dynamic pricing and ordering of a perishable product under additive effects of price and time on demand. European Journal of Operational Research, 260(2), 546-556.

Herbon, A., Levner, E., & Cheng, T. C. E. (2014). Perishable inventory management with dynamic pricing using time–temperature indicators linked to automatic detecting devices. International Journal of Production Economics, 147, 605-613.

Herbon, A., Levner, E., & Cheng, E. (2012). Perishable inventory management and DynamicPricing using TTI technologies. International Journal of Innovation, Management and Technology, 3(3), 262.

Hsieh, T. P., & Dye, C. Y. (2017). Optimal dynamic pricing for deteriorating items with reference price effects when inventories stimulate demand. European Journal of Operational Research, 262(1), 136-150.

Li, R., & Teng, J. T. (2018). Pricing and lot-sizing decisions for perishable goods when demand depends on selling price, reference price, product freshness, and displayed stocks. European Journal of Operational Research, 270(3), 1099-1108.

Sivashankari, C. K. (2016). Production inventory model with deteriorating items with constant, linear and quadratic holding cost-a comparative study. International Journal of Operational Research, 27(4), 589-609.

Tirkolaee, E. B., Goli, A., Bakhsi, M., & Mahdavi, I. (2017). A robust multi-trip vehicle routing problem of perishable products with intermediate depots and time windows. Numerical Algebra, Control & Optimization, 7(4), 417.

Tripathi, R. P., & Mishra, S. M. (2016). EOQ model with linear time dependent demand and different holding cost functions. International Journal of Mathematics in Operational Research, 9(4), 452-466.

Qin, Y., Wang, J., & Wei, C. (2014). Joint pricing and inventory control for fresh produce and foods with quality and physical quantity deteriorating simultaneously. International Journal of Production Economics, 152, 42-48.

Valliathal, M., & Uthayakumar, R. (2011). Designing a new computational approach of partial backlogging on the economic production quantity model for deteriorating items with non-linear holding cost under inflationary conditions. Optimization Letters, 5(3), 515-530.

Yavari, M., Enjavi, H., & Geraeli, M. (2020). Demand management to cope with routes disruptions in location-inventory-routing problem for perishable products. Research in Transportation Business & Management, 100552.

Subscription content Buy the paper to read

AACS Journals
Visitor:-

Copyright © 2020 AACS All rights reserved