Issue |
RAIRO-Oper. Res.
Volume 54, Number 1, January-February 2020
|
|
---|---|---|
Page(s) | 287 - 306 | |
DOI | https://doi.org/10.1051/ro/2018102 | |
Published online | 11 February 2020 |
Inventory and pricing decisions for imperfect quality items with inspection errors, sales returns, and partial backorders under inflation
1
Department of Operational Research, Faculty of Mathematical Sciences, New Academic Block, University of Delhi, Delhi 110007, India
2
Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul, 03722, South Korea
* Corresponding author: bsbiswajitsarkar@gmail.com
Received:
12
April
2018
Accepted:
10
November
2018
In this paper, an optimal replenishment inventory policy for imperfect quality items is presented with a selling price-dependent demand under inflationary conditions using a discounted cash flow (DCF) approach. Due to the presence of defectives in the system, all items go through a 100% inspection process. However, the screening process is also considered to be imperfect and involves errors, namely Type-I and Type-II. In addition, shortages are allowed and are partially backlogged. An optimal solution for the proposed model is derived by maximizing the expected profit function by jointly optimizing three decision variables: selling price, order quantity, and backorder level. To validate the theoretical results, a numerical example along with comprehensive sensitivity analysis is offered. The model has pertinence in industries like textiles, electronics, furniture, footwear, automobiles, and plastics.
Mathematics Subject Classification: 90B05
Key words: Inventory / Screening errors / Time value of money / Partial Backlogging / Selling price-dependent demand
© EDP Sciences, ROADEF, SMAI 2020
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.