An EOQ Model with Carbon Emissions and Inflation for Deteriorating Imperfect Quality Items under Learning Effect

32Citations
Citations of this article
24Readers
Mendeley users who have this article in their library.

Abstract

We developed an economic order quantity (EOQ) model with a learning effect and carbon emissions under inflationary conditions and inspection for retailers where the items deteriorate naturally. Finally, the total profit of the retailer is maximized with respect to cycle length. A sensitivity analysis was also performed to understand the robustness of the model. In the sensitivity analysis, we discuss the impact of learning rate, inflation rate, and deterioration rate on lot size and length of the cycle, as well as the retailer’s entire profit function. Observations and managerial insights are discussed. The effect of inventory parameters on the total profit is shown in the sensitivity section.

References Powered by Scopus

Economic production quantity model for items with imperfect quality

943Citations
N/AReaders
Get full text

The carbon-constrained EOQ

452Citations
N/AReaders
Get full text

ECONOMIC ORDER QUANTITIES WITH INFLATION.

388Citations
N/AReaders
Get full text

Cited by Powered by Scopus

A sustainable inventory model for defective items under fuzzy environment

23Citations
N/AReaders
Get full text

A Sustainable Green Supply Chain Model with Carbon Emissions for Defective Items under Learning in a Fuzzy Environment

21Citations
N/AReaders
Get full text

Smart Production System with Random Imperfect Process, Partial Backordering, and Deterioration in an Inflationary Environment

16Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Alamri, O. A., Jayaswal, M. K., Khan, F. A., & Mittal, M. (2022). An EOQ Model with Carbon Emissions and Inflation for Deteriorating Imperfect Quality Items under Learning Effect. Sustainability (Switzerland), 14(3). https://doi.org/10.3390/su14031365

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 6

55%

Professor / Associate Prof. 3

27%

Lecturer / Post doc 2

18%

Readers' Discipline

Tooltip

Business, Management and Accounting 4

44%

Engineering 3

33%

Computer Science 1

11%

Agricultural and Biological Sciences 1

11%

Save time finding and organizing research with Mendeley

Sign up for free