A Joint Return Policy for a Multi-Item Perishable Inventory Model with Deterministic Demands, Return and All-Units Discount

Abstract

In this paper, we develop a multi-item perishable inventory model with deterministic demands, return and all-units discount. We consider a situation where a retailer sells several products to the customer and orders the products from one supplier. Demands are assumed to be deterministic following an inventory-dependent demand, and the supplier offers all-units discount to the retailer who has an opportunity to return unsold or deteriorated products to the supplier at a certain cost. In order to minimize the total cost for the retailer, the decision variables are the optimal return time and the optimal ordering quantity. Considering a multi-item case as an extension of the model by Setiawan et al. (2018) is the main contribution of this paper. We also develop an algorithm to find the optimal solution of the model. Numerical examples for three items are given to illustrate the model and a sensitivity analysis is performed to study the effect of the changes in parameter values on the optimal solution. We consider two scenarios, one with all-units discount and one with no discount. Within these two scenarios, we consider conditions of individual or joint return time for these three items. It is found that the individual return time with no discount gives the least total inventory cost in the numerical examples. Also, in general increasing the value of holding cost, deteriorating rate, return cost per unit and backorder cost will increase the total inventory cost in all scenarios.

Rotation cycle time and delivery decision for a multi-item producer-retailer integrated system featuring overtime and random scrap

“…A procedure along with numerical illustration was provided to jointly calculate the optimal lot size and sampling plan to the problem and demonstrate applicability and performance of their results. Additional studies (Afshar-Nadjafi et al, 2019;Bhagat et al, 2021;Chiu, Lin, & Wu, 2020;Daryanto, & Christata, 2021;Hariga & Ben-Daya, 1998;Lesmono et al, 2020;Mokao, 2020;Sana, 2010;Terdpaopong et al, 2021;Yamada et al, 2021) also focused on different features of imperfection products and/or manufacturing systems.…”

“…An optimal control model was constructed/formulated to deal with the problem, and as a result, an explicit solution along with illustrative example was presented to show applicability of the obtained results. Extra papers (Campbell, 2017;Chiu, Y. et al, 202;Chiu, Lin, & Wu, 2020;Ivanov et al, 2019;Lin et al, 2019;Lesmono et al, 2020;Mokao, 2020;Nicolaisen, 2011;Ohmori & Yoshimoto, 2021;) also addressed systems with overtime options and adjusted fabrication/output rates. This study intends to jointly decide the rotation cycle length and frequency of delivery for a multiproduct producer-retailer integrated system featuring overtime and random scrap.…”

Economic Order Quantity Inventory Considering Perishable Factor in Product, Delay in Payment, All-Unit Discount, and Product Return

“…Generally, suppliers also provide discounts if the company makes a large purchase, so it is expected that the company will buy in large quantities. This discount can reduce the total cost of inventory, where discounts can be divided into two groups: discounts on the overall price of goods (all unit discounts) and incremental discounts [7]. In addition, usually, drug suppliers also provide a policy of returning goods that have been damaged (past the expiration period).…”

Multi-Item Production Lot Sizing with Postponement, External Source for Common Parts, and Adjustable Rate for End Products

“…Their study used several cases to validate their study's applicability. Recent works (Fathallah et al, 2019;Lin et al, 2019;Demić et al, 2020;Lesmono et al, 2020;Moin et al, 2020;Yadavalli et al, 2020;Glock and Grosse, 2021;) investigated the effect of variable manufacturing rates on various aspects of fabrication systems and supply chain systems. Since few past works emphasized multi-item production lot-sizing problems with postponement and dual uptime-reduction strategies, we intend to fill the gap.…”