Mathematical Problems in Engineering

Volume 2013 (2013), Article ID 739425, 13 pages

http://dx.doi.org/10.1155/2013/739425

Research Article

An EOQ Model for Perishable Items with Supply Uncertainty

Xiaoming Yan1,2 and Yong Wang³

¹School of Computer, Dongguan University of Technology, Dongguan, Guangdong 523808, China

²Chongqing Key Laboratory of Logistics, Chongqing University, Chongqing 400044, China

³School of Economics and Business Administration, Chongqing University, Chongqing 400044, China

Received 10 December 2012; Accepted 31 January 2013

Academic Editor: Sivaguru Ravindran

Copyright © 2013 Xiaoming Yan and Yong Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This paper aims to develop a continuous inventory model for finding the strategy for a retailer that sells a seasonal perishable item over a finite planning time. The purpose of this retailer is to maximize the expected profit by choosing an optimal ordering quantity before the sales horizon and adopting an optimal pricing policy during the sales horizon. With the help of Pontryagin's maximum principle, we can obtain the optimal ordering quantity and the optimal pricing policy by solving a list of equations. For a special case, we not only characterize the structure of the optimal pricing policy for any given initial inventory level but also obtain the optimal solutions by solving a set of equations. A numerical analysis reveals the influence of some parameters on the optimal ordering quantity.

1. Introduction

Many unpredictable events, such as machine breakdowns, unscheduled maintenance, labor strikes, and natural disaster, exist widely in the previous several decades and produce some adverse and long-lasting effects on the downstream supply chain members. For example, in March 2000, lightning caused a fire that shut down the Philips Semiconductor plant in Albuquerque, New Mexico, for six weeks, leading to a shortage of components for both Ericsson and Nokia. According to The Wall Street Journal, company officials say that they [Ericsson] lost at least 400 million in potential revenue [1]. Moreover, we can find a large number of other examples in Sheffi's book [2]. The wide existence of yield uncertainty has drawn significant attention of scholars and turns the random yield to an important research topic in the area of operations management and production economics. For example, Gerchak et al. [3], Ciarallo et al. [4], Wang and Gerchak [5], Erdem and Özekici [6], Li and Zheng [7], Chao et al. [8], and Feng [9] analyze the inventory systems with random yield and periodic review; Erdem et al. [10], Fadıloğlu et al. [11], Yan and Liu [12], and Tajbakhsh et al. [13] address the random yield models with continuous review; Agrawal and Nahmias [14], Inderfurth [15], Dada et al. [16], Grasman et al. [17], Rekik et al. [18], Maddah et al. [19], and Tang and Kouvelis [20] study the newsvendor problems with unreliable supply and uncertain demand.

In this paper, we consider an EOQ model where a retailer orders, stocks, and sells a perishable item over a fixed finite horizon. Before the sales horizon, the retailer orders a quantity of an item which can be received at the beginning of the sales horizon. However, the supplier may be unreliable, that is, the quantity effectively received at the beginning of the sales horizon cannot correspond exactly to the ordering quantity. After the ordering quantity has been received and the sales horizon has started, the retailer sells the item by adopting a dynamic pricing policy. We allow the prices to be varied continuously over the sales horizon and the item to be of physical decay or deterioration. Deterioration of inventory is commonplace in real life, such as fruit, vegetables, and seafood. At the end of the sales horizon, the inventory, which is not sold during the horizon, will be disposed of with a salvage, which may be negative. Once the products have been sold out before the end of the horizon, the retailer stops the selling behavior and the trade is over. The objective of the retailer is to maximize his expected profit by determining an ordering quantity before the horizon and adopting a dynamic pricing policy during the horizon. This problem arises in a variety of industries, such as supermarket selling vegetables, fruits, and fishes.

Next, we review some literatures on EOQ models with pricing decisions. Rajan et al. [21] consider the relationship between pricing and ordering decisions for a monopolistic retailer facing a known demand function which is deterministic. They achieve the optimal pricing policy and the length of pricing cycle. Abad [22] extends Rajan et al. [21]'s model, and the only difference is that when it is economic for a highly perishable product to backlog demand, the retailer can plan for periods of shortage during which demand can be partially backlogging. Abad [23] considers the problem of determining the optimal price and lot size for a reseller, where the selling price is constant within the inventory cycle. Teng and Chang [24] establish an EPQ model in which the demand rate is a function of the on-display stock level and the selling price. J. M. Chen and L. T. Chen [25] deal with the joint decisions on pricing and replenishment schedule for a periodic review inventory system in which a replenishment order may be placed at the beginning of some or all of the periods. Shinn and Hwang [26] deal with the problem of determining the retailer's optimal price and order size simultaneously under the condition of order-size-dependent delay in payments. Chou and Parlar [27] consider a dynamic pricing problem over a finite horizon where the demand for a product is a time-varying linear function of price. Our work is most closely related to the model considered by You and Hsieh [28]. They study an EOQ model for a nonperishable product with stock- and price-sensitive demand. In their model, the price is varied periodically and the supplier is reliable. However, we study an EOQ model with random yield and continuously dynamic pricing decisions. By transforming the primal problem into a standard control problem, the optimal pricing policy can be obtained by solving a list of equations. To a certain extent, our model is an extension of You and Hsieh's model. Many other literatures on dynamic pricing model can be found in the review papers, such as Chan et al. [29] and Elmaghraby and Keskinocak [30].

The rest of this paper is organized as follows. In Section 2, we present a mathematical model and transform the retailer's problem into a standard control problem. In Section 3, we determine the optimal pricing policy and initial ordering quantity for a special case. In Section 4, we present some conclusions and directions for future research.

2. Mathematical Model and Analysis

Consider a retailer which orders and sells a perishable product over a finite time horizon. Before the sales horizon, the retailer orders a quantity of an item which can be received at the beginning of the sales horizon. However, the quantity effectively received at the beginning of the sales horizon may be less than the ordering quantity. After the ordering quantity has been received and the sales horizon has started, the retailer sells the item by adopting a dynamic pricing policy. In our formulation, let be the unit purchasing cost, the per unit inventory holding cost per unit time, the discount rate, the horizon length, the unit salvage value at the end of the horizon, and the ordering quantity before the sales horizon. However, an order placed from the supplier may be realized randomly: an ordering of units from the supplier will receive units of the item at the beginning of the horizon, where is a random variable whose distribution is independent of the ordering quantity . and denote the distribution and density functions of , respectively. Without loss of generality, we assume that with probability one and for . This form of random yield has been used by many other researchers, such as Gerchak et al. [3], Grasman et al. [17], Inderfurth [15], Agrawal and Nahmias [14], and Li and Zheng [7]. Let be the inventory level at time . Because we assume that the retailer stops selling once he/she sells out the item, we can derive for all . The deterioration rate at time is assumed to be , where is the deterioration factor at time . If , then there is no deterioration at time . Let be the unit selling price at time . The demand rate at time , denoted as , is a function both of price and time . Because the customers often prefer to purchase fresher and cheaper items, we assume that the demand rate, , is nonincreasing with and , respectively. Let be the finite upper bound of price , that is, for . Therefore, denotes that the item has been sold out and the trade is over.

If the retailer picks a pricing policy for , then the total demand during can be described as , and the total deterioration during can be described as . Therefore, for any fixed inventory level at the beginning of the horizon, we have the following constraint:

In fact, denotes the realized value of the ordering quantity at the beginning of the horizon. Constraint (1) states that the total of demand and deterioration during the horizon is never more than the initial inventory level .

For any initial inventory level and pricing policy , the retailer's profit obtained during the sales horizon, , is given by the sales revenue over the horizon, adding the salvage profit and subtracting the holding cost over the horizon

where the inventory level, , satisfies the following condition

By solving (3), the inventory level, , can be described as

Expression (4) implies that the inventory level at time is

Based on (4) and (5), we derive the following result which presents the closed form of . All proofs are presented in the appendix.

Proposition 1. , defined as (2), can be simplified as

where and are defined as follows:

Note that , defined as (7), is independent of the pricing policy ; therefore, for any given initial inventory level , the retailer's problem during the sales horizon can be equivalently described as follows:

s.t.

where . If , then and , that is, part of the items are unsold at time . Therefore, if , then denotes the time at which the retailer just sells out the items and the trade is over at time . If , then the retailer just sells out the items at time . If , then part of the items are unsold at time . From the expression of in (4), constraint (10) can be equivalently written as

In order to solve the optimization problem (9), we try to transform it into a standard control problem. For notational convenience, we define

Note that , we have and is increasing in . Therefore, the problem (9) can be transformed into the following standard control problem:

s.t.

where is regarded as a control variable and is regarded as a state variable. According to Sethi and Thompson (Section  3.3, page 65, [31]), the current-value Hamiltonian function with adjoint variable can be described as

and the current-value Lagrangian function with Lagrange multipliers and can be described as

where , , , and are all dependent on time . Following Theorem 8 and expression (3.41) in Sethi and Thompson [31], we present the following result.

Lemma 2. If is concave in , then according to Pontryagin’s maximum principle, the optimal control must satisfy the following necessary and sufficient conditions:

Based on Lemma 2, the optimal price at time is equivalently written as , where is determined by (17). For convenience in exposition, we define

where and . The following proposition characterizes the optimal pricing policy.

Proposition 3. If is concave in , then the optimal pricing policy can be described as follows.

(1)If , then is just the optimal pricing policy.(2)If , then the optimal pricing policy is given by , where is determined by

Proposition 3 implies that the optimal pricing policy is determined by the realized initial inventory level . If is large enough (i.e., ), then the optimal pricing policy can be described as . Otherwise, the optimal pricing policy is dependent on the value of , which can be determined by solving (20). Since the optimal pricing policy is dependent on the initial inventory level, , we denote the optimal pricing policy as for given initial inventory level . After the optimal pricing policy, , has been obtained for any initial inventory level , the retailer determines the optimal ordering quantity according to the following expression:

where denotes the realized initial inventory level at the beginning of the sales horizon. In expression (21), we assume that the retailer pays for the quantity effectively received, and the retailer is risk neutral. The results can be extended to the case in which the retailer pays for the ordering quantity.

3. An Important Case

While the general formulation discussed in the above section is useful to obtain the optimal solution for the complex real world application, we can gain stronger results and insights into the problem for specific cases. In this section, we mainly concentrate on analyzing a special case in which the demand rate at time is

where ,   and are constants. In expression (22), implies that if for all , then the demand rate is decreasing in . That is, if the price keeps constant during the sales horizon, the customers always prefer to purchase fresher items. Demand (22) is derived from the linear demand form: , which has been used by many researchers, such as Chao et al. [8]. From (22), we derive . In what follows, we assume for all , where is a nonnegative number. In order to avoid trivial cases, we present the following assumption which always holds in the following analysis.

Assumption 4. Consider the following:

Remark 5. From (4), the holding cost of storing unit item from time zero to can be described as

where is the leavings of unit product at time . Therefore, the first inequality of Assumption 4 states that it is not profitable for the retailer to purchase more products for disposing with salvage at the end of the horizon, and the second inequality of Assumption 4 states that it is profitable to purchase products for selling at the end of the sales horizon.

3.1. Optimal Pricing Policy

Note that and for , then from (15), (7), and (8), the current-value Hamilton function , and can be reduced to the following expressions:

Taking second-order partial derivative of with respect to , we have

That is, is strictly concave in . Therefore, we can obtain the optimal pricing policy by Proposition 3. From and expression (25), can be written as

By solving above equation, we derive

From (30) and , the optimal pricing policy can be described as

Note that is defined as (27), then after simplifying, the optimal pricing policy can be written as

where

In reality, the selling price of any product is always nonnegative. Therefore, we assume that for any and , otherwise, there exists some time interval on which the optimal price is zero. That is, we assume that the parameters (, , , , , , , and ) obtained from the real life guarantee for any and . From (33), can be written as

Since in the optimal situation, expression (34) is equivalent to for , where

The following result presents several conditions under which for .

Lemma 6. If or , then for ; if and , then for , where and are defined as and , respectively.

Based on , Assumption 4 and for , we present the following result which formulates the relationship between the value of and the structure of the optimal pricing policy.

Lemma 7. (1) If

then the optimal pricing policy can be described as for .

(2) If

then the optimal pricing policy can be described as for and for , where satisfies .

(3) If

then the optimal pricing policy can be described as for , that is, the retailer will not order and sell the product in the optimal situation.

Lemma 7 states that the optimal pricing policy can be determined by the value of . That is, once the value of has been determined, the optimal pricing policy is also obtained. In order to guarantee that it is better to sell the product during the horizon than to salvage the product at the end of the horizon, we assume that in the rest of this paper. Next, we want to determine the optimal value of . For notational convenience, we define

In expression (40), we assume that . If , then we set in expression (40). From expression , we obtain that the optimal value of is dependent on the initial inventory level. Note that the optimal pricing policy can be determined by the value of , therefore, the optimal pricing policy can be determined by the initial inventory level. Because the initial inventory level is fixed at the beginning of the sales horizon, we can determine the optimal value of by analyzing the relationship between the value of and the initial inventory level. The next result formulates the optimal value of and the structure of the optimal pricing policy based on the initial inventory level .

Theorem 8. (1) If , then , and the optimal pricing policy can be described as

(2) If , then , and the optimal pricing policy can be described as

(3) If , then , and the optimal pricing policy can be described as

for and for , where , , and are defined as

Theorem 8 implies that the structure of the optimal pricing policy can be determined by the initial inventory level. If the initial inventory level is more than , that is, , then the optimal price just maximizes the instantaneous profit at each time and products are left for salvage at the end of horizon. If , then the products are just sold out at the end of the horizon. If , then the product has been sold out before the end of the sales horizon. Moreover, the optimal price at each time is decreasing in the initial inventory level. That is, the higher the initial inventory level, the more the total sales quantity during the sales horizon.

3.2. Optimal Ordering Quantity

Based on the structure of the optimal pricing policy obtained in the previous section, we will determine the optimal ordering quantity in this section. Because the optimal pricing policy can be completely determined by the initial inventory level, the retailer's problem is to maximize its expected profit by choosing an appropriate ordering quantity. As mentioned in Section 2, the optimal ordering quantity can be determined by

where is the optimal pricing policy when the realization of ordering quantity is at the beginning of the horizon. For any realization of , we have obtained the closed form of optimal pricing policy in Theorem 8. Therefore, expression (46) is only dependent on the ordering quantity , and we can determine the optimal ordering quantity by analyzing the characterization of expression (46). For notational simplicity, we define

Then the optimal ordering quantity can be determined by

Note that and for , then we can obtain that if is concave on and strictly concave on an interval , then is strictly concave in , where is a positive number. If is strictly concave in , the optimal ordering quantity is not only unique but also can be obtained by solving (48). The following result not only states that is concave on interval and strictly concave on interval but also presents the closed forms of . Therefore, we can obtain the optimal ordering quantity by solving (48).

Proposition 9. is concave and strictly concave on the intervals , and respectively. Moreover, the maximizer of belongs to the interval .

From Proposition 9, we obtain for and for . Note that , then from (48), the optimal ordering quantity has to satisfy . Next, we discuss the optimal ordering quantity in two cases: and . Because and is strictly decreasing in , we can derive the following conclusions. If , then we have , and can be determined by

From and , expression (49) can be written as

If , then we have , and can be determined by

From , , and , expression (51) can be written as

Because does not have a closed form, we cannot obtain a closed form of the optimal ordering quantity by solving (50) and (52). Next, we present a numerical example to analyze the effects of the parameters on the optimal ordering quantity.

3.3. Numerical Example

In this section, we mainly concentrate on analyzing the optimal ordering quantity by a numerical example in which the random yield is normally distributed, that is, . Following Agrawal and Nahmias [14], we also assume that . Such assumption can guarantee that is between and with probability exceeding 99%. The values of parameters in this example are given as follows: , , , , , , and . In Tables 1 and 2, Figures 1 and 2, we present the effects of parameters , , , and on the optimal ordering quantity, respectively. In Table 1, we set , and . From Table 1, we can see that the optimal ordering quantity is first decreasing in and then increasing in . This result implies that the optimal ordering quantity under deterministic yield is not necessarily less than the optimal ordering quantity under random yield. In Table 2, we set , and . From Table 2, we can see that , and are all increasing in . That is, the larger the deterioration factor, the more the optimal ordering quantity. In Figure 1, we set , and . From Figure 1, we can see that the optimal ordering quantity is decreasing in . This result can be interpreted by the fact that the demand rate is decreasing in and the optimal ordering quantity is in direct proportion to the total demand during the horizon. From Figure 1, we imply that if the customers prefer to purchase fresher products, then the retailer should order less products. In Figure 2, we set , and . From Figure 2, we see that the optimal ordering quantity is decreasing in . That is, the larger the mean of random yield, the less the optimal ordering quantity. This result is intuitive.

Table 1: Effect of on the optimal ordering quantity.

0.12
0.14
0.16

759.3
754.1
750.6
749.1
749.3
750.3
752.2

Table 2: Effect of on the optimal ordering quantity.

0.01
0.012
0.015

624
634
644
654
670
680
697

510.8301
521.0969
531.6423
542.4748
559.2811
570.8704
588.8557

226.2675
234.16
242.1696
250.3023
262.7455
271.213
284.1886

Figure 1: Effect of on the optimal ordering quantity.

Figure 2: Effect of on the optimal ordering quantity.

4. Conclusion

In this paper, we have studied an EOQ model for a perishable product with random yield, price- and time-sensitive demand. First, by transforming the primal problem into a standard control problem, we present a general way to analyze the optimal pricing policy and the optimal ordering quantity. Second, we use Pontryagin’s maximum principle to analyze a special case and obtain the structure of the optimal pricing policy. Based on the structure of the optimal pricing policy, we can obtain the optimal ordering quantity by solving an equation. Finally, we do some sensitivity analysis about the optimal ordering quantity with parameters , , , and by a numerical example. From the numerical example, we obtain several intuitive results. For example, the more the customers prefer to purchase fresh products, the less the retailer orders products before the horizon and the larger the probability that the retailer sets higher price. One potential research direction is to study the optimal policies on stochastic demand, for example, the demand process can be assumed to be a diffusion process with price control. Another potential research direction is to study the situation in which the retailer has several ordering opportunity over a fixed horizon.

Appendix

Proof of Proposition 1. Substituting (4) and (5) into (2) and simplifying, we can obtain the following expression:

According to integration by parts, the third part of the right hand of expression (A.1) can be transformed into the following expression:

Substituting (A.2) into (A.1) and calculating, we have

With the help of (7) and (8), we can derive the result.

Proof of Proposition 3. Solving (17), we obtain . Substituting into (15) and simplifying, we have

Note that is concave in and , then from (18), the optimal control is given by , which is defined as (19). Note that in the optimal situation, we discuss the optimal pricing policy in two cases: and . Suppose that in the optimal situation, then the optimal control is . Substituting into , we derive

(1) If , then satisfies the sufficient conditions and is just the optimal pricing policy. (2) If , then is not feasible in the optimal situation. That is, and in the optimal situation. Note that , defined as (19), is the optimal pricing policy, then we can determine the value of by solving equation , that is,

After determining by (A.6), we can obtain the optimal pricing policy .

Proof of Lemma 6. Taking derivatives of with respect to , we derive

Then is strictly convex in . The solution to equation can be described as

and the minimum of can be described as

where is the minimizer of . Because

we derive the following conclusion: if or , then for all ; if and , then for all .

Proof of Lemma 7. Because for all , the optimal pricing policy can be written as

In order to simplify the optimal pricing policy (A.11), we analyze the solution to the equation , that is,

From (A.12), we can obtain the following conclusions: (1) If , then (A.12) has no solution, and the optimal pricing policy can be described as for all . (2) If , then (A.12) has a unique solution on interval , and the optimal pricing policy can be described as for all . (3) If , then (A.12) has a unique solution on interval , and the optimal pricing policy can be described as for and for . (4) If , then (A.12) has a unique solution on interval , and the optimal pricing policy can be described as for all . In other words, the retailer will not order and sell the product in this case. Because in the optimal situation and , the result holds.

Proof of Theorem 8. From Lemma 7, we have known that if , the total sales quantity during the whole horizon can be described as

Note that and , then the total sales quantity, denoted as (A.13), satisfies the following expression:

Therefore, if , there exists a unique such that and is optimal. From Lemma 7, the optimal pricing policy can be described as (42). If , then the optimal value of must be zero, and the total sales quantity is . If , then from Lemma 7, the total sales quantity during the horizon is

From expression (A.12), is the unique solution to the equation

Substituting (A.16), (39) into (A.15) and simplifying, the total sales quantity can be written as

Taking derivative of with respect to and simplifying, we derive

Then is increasing in . Note that , then we have

Therefore, if , there exists a unique such that the total sales quantity is , and the optimal pricing policy can be described as (43), where is defined as

Proof of Proposition 9. Based on the optimal pricing policy obtained in Theorem 8, we prove the concavity of in three cases: , , and . If , then from Theorem 8, (6), and (26), can be described as

where is defined as (40). Taking derivative of with respect to , we derive

Then is linearly decreasing in on the interval . Therefore, is concave in on the interval . If , then from Theorem 8, (6), and (26), can be described as

Taking derivative of with respect to , we derive

Then is strictly concave in on the interval . From (A.13) and (A.14), we have

Thus, from (A.24) and (A.25), we have

From (A.22) and (A.27), we derive . Then is concave on the interval . If , then from Theorem 8, (6), and (26), can be described as

where is defined as (A.20). Taking derivative on both sides of expression (A.20) with respect to and simplifying, we obtain

From Theorem 8 and (44), we have

Then expression (A.28) can be written as

and the first derivative of with respect to can be described as

From the definition of , we have

Therefore, by taking derivative on both sides of expression (A.31) with respect to and simplifying, we have

From (A.29), we obtain that is strictly increasing in . Therefore, is strictly decreasing in on the interval . That is, is strictly concave in on the interval . From (A.19), we have . Therefore, from (A.34), we derive

and is strictly increasing on the interval . From (A.26) and (A.35), we have . Hence, is concave and strictly concave on the intervals and , respectively. Because is strictly increasing on and strictly decreasing on , the maximizer of belongs to the interval . Moreover, from (A.24), the maximizer of can be described as

Substituting (A.36) into (A.23) and simplifying, the maximum of can be described as

The proof is completed.

Acknowledgments

The authors would like to thank the editor and two anonymous referees, whose comments improved our work significantly. This work was partially supported by National Natural Science Foundation of China under Grant nos. 71201027 and 71272085, Humanity and Social Science Youth Foundation of Ministry of Education of China under Grant no. 12YJC630260, Guangdong Natural Science Foundation under Grant no. S2012040007919, foundation for Distinguished Young Talents in Higher Education of Guangdong under Grant no. LYM11121, and the Open Fund of Chongqing Key Laboratory of Logistics under Grant no. CQKLL12003.

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