In reality, deterioration of items during storage period is a realistic phenomenon in many inventory sectors. Controlling and regulating the deteriorating items are very difficult in practice. In storage system, fruits, vegetables, foodstuffs, etc. deteriorate during their normal storage period. The deteriorating items cannot be used for its original purpose. The loss of inventory due to deterioration cannot be ignored. Thus, it is very essential to control the deterioration of items. A model with exponentially decaying inventory was initially proposed by Covert and Philip [1] . Dye et al. [2] considered a deteriorating inventory model with time-varying demand and shortage-dependent partial backlogging. Chung and Wee [3] discussed a deteriorating inventory model for pricing policy with imperfect production, inspection planning, warranty period, and stock-level-dependant demand. Sana [4] established an inventory model for both ameliorating and deteriorating items with capacity constraint for storage. Wee et al. [5] proposed an optimal replenishment policy for deteriorating green products. Sett et al. [6] developed a two-warehouse inventory model with increasing demand and time-varying deterioration. They considered the maximum lifetime of products. Always all deterioration functions are not deterministic type; it may follow probabilistic nature sometimes. Most recently, Sarkar [7] developed a production-inventory model for three different types of continuously distributed deterioration functions. Sarkar and Sarkar [8] explained a control- inventory problem with probabilistic deterioration. They solved the model with the help of Euler-Lagrange method. Sarkar and Sarkar [9] developed an inventory model with time-varying demand and deterioration. Sarkar et al. [10] considered a deteriorating inventory model with variable demand. C rdenas-Barr n et al. [11] developed an improved solution procedure to solve a production model with reworking and multiple shipments. Sarkar and Saren [12] established a partial trade-credit model for retailer with exponentially deterioration. Sarkar et al. [13] considered a deteriorating inventory model with trade-credit policy for fixed lifetime products.

In classical inventory models, it is often assumed that shortages are either completely backlogged or completely lost. But in the real life, when shortages occur, it is observed that some customers may prefer their demands to be backordered, and some may refuse the backorder case. In this direction, Deb and Chaudhuri [14] were the first to incorporate shortages into inventory model―that model was an extension of Donaldson’s [15] model with shortages. Chang and Dye [16] developed an inventory model in which the backlogging rate is the reciprocal of a linear function of the waiting time. C rdenas-Barr n [17] explained without using differential calculus how inventory model with shortages can be solved with using basic algebraic procedure. Teng et al. [18] extended the model in which the backlogging rate is any decreasing function of the waiting time up to the next replenishment. Sometimes managers prefer to use planned backorders to reduce the total system cost. In this direction, C rdenas-Barr n [19] presented an inventory model with reworking process at a single-stage manufacturing system with planned backorders. Sarkar et al. [20] described a production policy in order to find out an optimal safety stock, production lotsize, and reliability parameters. Sarkar et al. [21] developed an integrated inventory model with variable lead time, defective units, and delay in payments. Sarkar and Majumder [22] developed an integrated vendor-buyer supply chain model with vendors setup cost reduction. Sarkar and Sarkar [23] presented an improved inventory model with partial backlogging, time-varying deterioration, and stock-dependent demand. Most recently, Sarkar et al. [24] extended the inventory model with random defective rate, rework process, and variable backorders. Sarkar et al. [25] developed a continuous review inventory model with backorder price discount under controllable lead time.

Classical inventory model considers constant demand rate. However it is observed that the demand rate for electronic goods (e.g., hard disk, RAM, processor, mobile, etc.), new brand of consumer goods, seasonal products (fruits, e.g., mango, orange, etc.) increases linearly at the beginning up to a certain moment as time increases and then stabilizes to a constant rate until the end of the inventory cycle. To represent such type of demand pattern, the “term/ramp-type” is used. Mandal and Pal [26] were the first authors to introduce ramp-type demand in inventory model. Wu [27] developed an EOQ model with ramp-type demand, Weibull distributed deterioration and partial backlogging. Giri et al. [28] extended the model of Wu [27] with more generalized Weibull deterioration distribution. A model with partial backlogging was considered by Skouri et al. [29] . Sana [30] formulated an EOQ model over an infinite time horizon for deteriorating items with price-sensitive demand and partial backordering. C rdenas-Barr n et al. [31] developed two easy and improved algorithms to determine jointly both the optimal replenishment lot size and the optimal number of shipments. Sarkar et al. [32] proposed a continuous review manufacturing inventory model with setup cost reduction, quality improvement, and a service level constraint.

The effects of inflation and time-value of money cannot be ignored for the present study. Several researchers have examined the inflationary effect on the inventory policy. Buzacott [33] was the first researcher to assume inflation in inventory model. Datta and Pal [34] presented the effect of inflation and time value of money on an inventory model with linear time-dependent demand rate and shortages. Jaggi et al. [35] considered a deteriorating inventory model under inflationary conditions using a discounted cash flow (DCF) approach over a finite planning horizon. Sarkar and Moon [36] extended an economic production quantity (EPQ) model with inflation in an imperfect production system. Sarkar et al. [37] [38] developed two inventory models for imperfect production with inflation and time value of money.

This model is developed for deteriorating items with ramp-type demand under stock-dependent demand. In addition, different types of probabilistic deteriorations are considered in this model. Shortages are allowed which are backlogged. The effect of inflation and time value of money are incorporated into the model. The main purpose of this paper is to develop an optimal replenishment policy which maximizes the total profit per unit time. The necessary and sufficient conditions of the existence and the uniqueness of the optimal solutions are also provided. Sensitivity analysis of the optimal solution with respect to major parameters and their discursion is carried.

To derive the model, following notation and assumptions are made:

The model considers an inventory model for deteriorating items with ramp-type demand and stock-dependent selling rate. The replenishment at the beginning of the cycle brings the inventory level up to. The inventory level decreases during the time interval due to demand and deterioration of items, and falls to zero at. Thereafter shortages occur during the period, which are partially backlogged. The inventory level, satisfies the following differential equations

The solutions of these differential equations depend on the selling rate. There are two cases considering in this paper: (a) and (b) The fluctuation of the inventory level for the two cases is depicted in Figure 1 and Figure 2, respectively.

To derive the optimal solution, we solve two examples that consist of the different situation of the ramp-type demand and the deterioration rates. Let us consider the following parametric values:, , , , , , , , , , ,.

We now study the effects of changes in parameters such as on optimal total profit. The sensitivity analysis is performed by changing each of the parameters by taking one parameter at a time while keeping the remaining parameters unchanged. The results of Example 1 and Example 2 are presented in Table 1.

From Table 1, the discussion of sensitivity analysis of the key parameters is as follows:

From the above table we can conclude that and are highly sensitive towards change in and whereas slightly sensitive in and. On the other hand, and are moderately sensitive towards change in and

In this section, we will discuss some special cases that influence the total profit.

In this marketing environment, when a new brand of consumer goods is launched, the demand of goods increases quickly to a certain moment and after some time it stabilizes. Finally, it becomes almost constant. Keeping this type of demand pattern in mind, we considered demand as a ramp-type function of time. To make the research a more realistic one, four different types of continuous probabilistic deterioration functions are considered here. The associated profit function was maximized at the optimal values of decision variables. A unique solution procedure was provided as an optimal solution. Some numerical examples, graphical representations, special cases, and sensitivity analysis were given to illustrate the model. There are several extensions of this work that can constitute future research related in this field. This model can be extended in several ways, like multi-item inventory models, and reliability of the items. Another interesting idea is to consider fuzzy demand case.

The authors would like to thank the reviewers for their helpful comments to improve the paper. The authors are grateful to Guest Editor Professor S. S. Sana for his useful comments. This work was supported by the research fund of Hanyang University (HY-2014-N, Project number 201400000002202) for new faculty members.