In today’s era of higher competition in the business, there are many conditions such as offered concession in bulk purchasing, seasonality, higher ordering cost, etc., which force a retailer to purchase more quantities than needed or exceed the storage capacity. So in this situation the retailer has to purchase an extra warehouse named as rented warehouse to stock the extra quantity. In this paper an inventory model for deteriorating products with selling price dependent rate is developed. The occurring shortages are assumed to be partially backlogged and cycle time is also variable. The purpose of the development of this model is to compute the amount and time of order which can optimize the total average cost of the system. A solution procedure and numerical example are presented to illustrate the implementation of the proposed study. Sensitivity analysis concerning with distinct system parameters is also presented to demonstrate the model.

Quantity discount is a usual term in business and has been a topic of interest for a long time, but have received very little attention. Many vendors offer different schemes to their customers to increase the existing size of order, which results in higher annual sale for the vendor and a lower purchasing price for the retailer. Therefore, the buyer adjusts his/her selling price, which influences the demand for the product. The objective of this paper is to develop an inventory model for deteriorating products with quantity discount and partial backlogging to determine the optimal ordering quantity for the retailer optimizing the total cost or profit of the associated model. A numerical example is also given to illustrate the model and its significant features.

In this paper we develop an inventory model for deteriorating items with price sensitive demand. Generally the vendor offers a cash discount or fix time period to the retailer to pay all the dues. According to the availability of money the retailer chooses his/her option. In this paper we discuss the possible cases of permissible delay and cash discount. The shortages are allowed in this model and are partially backlogged. Holding cost is considered as varying function of time, which reflects a more realistic concept. The purpose of this study is to optimize the overall cost of the system and to compute optimal ordering quantity. Numerical examples for different cases are also presented to illustrate the study. A sensitivity analysis with regard to distinct associated parameters is shown to make sure the constancy of the model.

The objective of this study is to develop of an inventory policy for deteriorating items, in which demand for the products is stock dependent and the retailer invests in preservation technology to reduce the rate of product deterioration. In many real-life situations, for certain types of consumer goods, the consumption rate is sometimes influenced by the stock-level. It is usually observed that a large pile of goods on a shelf in a supermarket will lead the customer to buy more and then generate higher demand. The consumption rate may go up or down with the on-hand stock level. This paper is developed with the realistic conditions of demand, allowable credit period, partial backlogging and variable ordering cost. A solution procedure is given to find the optimal preservation technology cost and total cost of the system. A numerical example and sensitivity analysis are presented to illustrate the model.

For today’s high competitive market, when immensity purchasing of inventory becomes convenient or obligatory, to discover an alternative market in order to maximize the revenue earned is a tradition. In this paper, we considered an inventory model for seasonal products with Weibull rate of deterioration having two potential markets, say, primary and alternate. To handle the inventory up to the next season will result an increase in total cost. So it is a favorable task to transfer the remaining stock to the alternate market even at a slightly differ in selling price. Solution procedure, numerical examples and sensitivity analysis for different variables are presented to illustrate the model.