Journal of Applied Mathematics

Volume 2013 (2013), Article ID 961258, 9 pages

http://dx.doi.org/10.1155/2013/961258

Research Article

Planning Horizon for Production Inventory Models with Production Rate Dependent on Demand and Inventory Level

Jennifer Lin,¹ Henry C. J. Chao,² and Peterson Julian²

¹Department of Transportation Logistics & Marketing Management, Toko University, Chiayi 61363, Taiwan

²Department of Traffic Science, Central Police University, Taoyuan 33334, Taiwan

Received 3 December 2012; Revised 1 March 2013; Accepted 2 March 2013

Academic Editor: Ching-Jong Liao

Copyright © 2013 Jennifer Lin et al. 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 discusses why the selection of a finite planning horizon is preferable to an infinite one for a replenishment policy of production inventory models. In a production inventory model, the production rate is dependent on both the demand rate and the inventory level. When there is an exponentially decreasing demand, the application of an infinite planning horizon model is not suitable. The emphasis of this paper is threefold. First, while pointing out questionable results from a previous study, we propose a corrected infinite planning horizon inventory model for the first replenishment cycle. Second, while investigating the optimal solution for the minimization problem, we found that the infinite planning horizon should not be applied when dealing with an exponentially decreasing demand. Third, we developed a new production inventory model under a finite planning horizon for practitioners. Numerical examples are provided to support our findings.

1. Introduction

Inventory models, in general, can be classified into two categories: infinite and finite planning horizon. For inventory models with the finite planning horizon, the goal is to minimize the total cost. On the other hand, without the present value, that is, not considering the time value of money, the total cost for the entire infinite planning horizon will go to infinity such that researchers are not able to compare the total cost for different inventory policies. The prevailing solution to this dilemma is to minimize the average cost of the first replenishment cycle because of a constant demand that implies an identical replenishment policy for the second replenishment cycle. As a result, the minimization of the average cost for the first replenishment cycle will lead to the optimal solution. The original paper of Wilson’s EOQ model [1] is an example of an infinite planning horizon problem.

It should be noted that practitioners in previous studies seemed to randomly decide whether to use an infinite planning horizon or a finite one. That is, they make their choice either by routine experience or by referencing other studies without explaining or considering the choice that fits the characteristics of date on hand. For examples, under the assumptions of time-vary demand, production, and deterioration rate, Goyal and Giri [2] developed two models by employing different modeling approaches over an infinite planning horizon. On the other hand, Goyal’s model [3] was considered as a finite planning horizon problem over time period , where the replenishment cycle did not repeat itself in the same manner. It infers that each replenishment cycle within the planning horizon has different optimal solution such that the solution finding process requires the minimization of the total cost over the entire time period. For both studies, the reasoning behind the selection of either planning horizons was not explained. The purpose of this paper is to point out that in practice, some inventory models work sensibly over an infinite planning horizon. Managers under a highly competitive environment should be making correct and coherent decisions toward the development of inventory models that fit the pursuit of effective cost control.

Many papers have also discussed production inventory models under different conditions. By viewing the production rate as a variable, Bhunia and Maiti [4] developed two inventory systems. In the first system, the production rate was dependent on the inventory level, while the production rate was dependent upon the demand in the second. Su and Lin [5] combined the two models creating a model where production rate is dependent on both inventory level and demand. Moreover, Su and Lin [5] assumed that shortages were allowed with complete backlog and an exponentially decreasing demand.

We will show that finding the minimum value of the first replenishment cycle is not reasonable with an exponentially decreasing demand since the optimal solution for the production period will go to infinity, implying that the average cost is decreasing to zero. In response, we have developed a finite planning horizon production inventory model.

There are two primary reasons that justify assuming that the demand will decrease exponentially. First, the numerous innovations in the field of technology contribute to the expedited release of new merchandises, tremendously decreasing demand for the existing products in the market. Second, rapid changes in consumer preferences also greatly impact the sales of current merchandise. For instance, less than a year after a new camera cellular phone is introduced, an even newer generation will hit the market, with higher dpi than the previous generation. As a result, the demand for the old cell phone will plunge drastically.

Su and Lin [5] tried to extend the findings of Bhunia and Maiti [4], but their derivation for the differential equations with boundary conditions contained questionable results. Moreover, they could not analyze how many local minimum points exist. Up to now, there have been four published papers that have referred to Su and Lin [5] in their studies, Chu and Chung [6], Alfares et al. [7], Feng and Yamashiro [8], and Kang [9]. However, none of these papers have been made aware of the fundamental flaw in Su and Lin [5].

The derivation of Su and Lin [5] for the inventory level of the third phase contained questionable results such that their findings for relations among decision variables and their objective function also had questionable results. Moreover, we showed that their model is not suitable for infinite planning horizon, and then we studied the inventory model with finite planning horizon. There are two closely related papers, Yang et al. [10] and Lin et al. [11], that are considered for the finite planning horizon. There two models are developed for the EOQ with one decision variable to show that the optimal replenishment policy is independent of the demand type. However, there are four decision variables in our EPQ inventory model. We find two relations among these decision variables of the optimal solution for the infinite planning horizon, then two independent decision variables are left. For the finite planning horizon, we proved that there is only one decision variable left. These two papers have significant contribution for the theoretical development of EOQ inventory models, but their findings cannot be applied to our EPQ inventory model.

There are four phases for an EPQ inventory model, and then we proved that there is an upper bound for the elapse time for the first phase. It is an important finding when we applied a program to locate the optimal solution. For the infinite planning horizon, we showed that four decision variables are related, so only two independent decision variables are left, and we find the relations among decision variables that reduced the tedious computation for the minimum value. For the finite planning horizon, in each replenishment cycle, we proved that there is only one independent decision variable that achieves the efficiency for computation. Our first main contribution is providing an analytical approach to solve the optimal solution such that the result from computer programs is supported by the mathematical theorem. Our second main contribution is to reduce the number of independent decision variables to its minimum such that for obtaining the optimal solution, computer programs can be executed effectively.

2. Notation and Assumptions

To avoid confusion, we will use the same assumptions and notation as Su and Lin [5]:

deterioration rate,: maximum inventory level,: unfilled order backlog,: setup cost for each new cycle,: the cost of a deteriorated item,: inventory carrying cost per unit time,: shortage cost per unit,: total average cost of the system.

The assumptions below are used.

A single item is considered over (a) an infinite planning horizon for the first model and (b) a finite planning horizon of units of time for the second model which is subject to a constant deterioration rate. Demand rate, , is known and decreases exponentially so that , where is the initial demand rate and is the decreasing rate of demand, . is the inventory level. Production rate, , depends on both the demand and the inventory level with , , and . Deterioration of the units is considered only after those units are received and put in inventory. There is no replacement or repair of deteriorated items. Shortages are allowed and fully backordered. Two extra conditions, and , are added (explained in Section 4).

Remark 1. Su and Lin [5] assumed that is a prescribed period of time and denoted by . Note that in the beginning, Su and Lin [5] tried to develop a production inventory model for a finite planning horizon, say . However, during their derivation, they considered the problem of minimizing the average cost for the first replenishment cycle in the infinite planning horizon. To clearly distinguish the difference between infinite and finite planning horizon, we will separate the problem into two cases.

Case (a). We minimize the average cost of the first replenishment cycle. It is a minimization problem with an infinite planning horizon.

Case (b). We minimize the total cost over a finite planning horizon of .

3. A Review of Su and Lin [5]

In Su and Lin [5], the first replenishment cycle can be divided into four phases based on the time interval:

the first phase : the production dominates demand and deterioration, and the inventory level accumulates, the second phase : no production activity takes place. Demand and deterioration dominate, and so the inventory level gradually drops to zero at ,the third phase : no production and no deterioration take place. The shortage accumulates to at ,the fourth phase : the production is resumed, shortages accumulated during the third phase are fully backordered, and the inventory level returns to zero at .

The differential equations developed by Su and Lin [5] for governing stock levels over the four different phases during the first replenishment cycle, , can be expressed as follows:

Under the boundary conditions,

Su and Lin [5] found that

However, the result Su and Lin [5] derived in (5) is false. The expression should be revised as

Owing to an error in (5) of their derivations for and , the relation between and , say , and the relation between and , say , all contain questionable results. It implies that their objective function, , is also false.

Su and Lin [5] derived the expression, , so that the objective function has two independent variables, and . They computed and . However, they could not analyze whether a system that is comprised of and has solution.

4. Our Improvement for Infinite Planning Horizon Model

It should be pointed out that the result of (3) is based on the condition . On the other hand, if , (3) should be revised as

Hence, if we try to provide a complete study for the production inventory model of Su and Lin [5], then our model should be divided into seven cases: case (1): , case (2): , case (3): , case (4): , case (5): , case (6): , and case (7): .

To focus on the investigation of a production inventory model where the production rate is dependent both on demand and inventory level, demand is exponentially decreasing, and shortages are fully backordered, we add two extra conditions: and .

The reasoning behind the addition of an extra condition, , is as follows: when , the demand rate , the inventory level , and the production rate . For the accumulation of inventory during the first phase , it implies that for . For the special case of , we know that is valid. Therefore, we derive that when .

If , then the domain of has a lower bound satisfying the expression, , such that the domain of should be changed from to .

Moreover, Su and Lin [5] assumed in their numerical example that and . Their assumption provides support for our extra condition of . They also assumed that , , and which provides evidence that our condition, , is reasonable. Moreover, the condition of will focus on the development of a production inventory that is compatible with the numerical examples in Su and Lin [5] and to avoid tedious discussion for different inventory models with different relations among , and .

Based on (3), (4), (6), and (7) and the boundary conditions of (2), we derive that

From (9), we find the relation between and and then the relation among , , and :

We will simplify a four-variable problem, , , , and , to a two-variable problem of and . During , production, demand, and deterioration interact with each other to accumulate items that will be consumed and thus deteriorate during such that, trivially, is dependent on . We will derive the detailed relation between and , that is, and . During , the shortages will accumulate to be backlogged during so that naturally is dependent on . We will derive the detailed relation between and , which is the relation of (i) and (ii) with . Due to the fact that demand is varied, will influence the shortage during .

In the following, we will prove that can be uniquely decided if is given. When and are given, by using the relation in (11), the unique value of can be derived if is also given. Hence, we will simplify a four-variable problem to a two-variable problem. Let us rewrite (10) as

We tried to find the condition of under which there is a solution to with , satisfying (12). For the later discussion, given that , we denote the unique solution of that satisfies (12) as . We will prove that the feasible domain of is bounded, guaranteeing the existence of .

Motivated by (12), we assume the following auxiliary function, , to be

Taking the derivative of with respect to yields

Under the conditions and , it follows that

showing that is an increasing function from to , since

There is a unique point, say , that satisfies .

From (3) and (13), we have

From , the inequality, , is held. It follows that

By referring to (12), we obtain

According to (19), we showed that must go to so that will go to as well. We will express the result as and summarize our findings in the next lemma.

Lemma 2. and  .

From Lemma 2, we know that the feasible domain of should be set as

Given , with , then so that there is a unique , say , that satisfies

We may explicitly express as

We will summarize our findings in the following lemma.

Lemma 3. If , then there is a unique , say , as in (22) so that (10) is satisfied.

Next, we consider the relation among , , and by rewriting (11) as

Motivated by (23), we assume the following auxiliary function:

We find that

Under our assumptions of and , it can be inferred that increases from to .

The relation below,

holds since . Therefore, if and are decided, with the restriction , then there is a unique that satisfies (10). Also, from (26) and the increasing function , we know that for a given under the condition, , there is a unique point explicitly denoted as , simply say , that satisfies the condition, , such that the following expression,

satisfies (23). We will summarize our results in the next lemma.

Lemma 4. If and are given with and , then there is a unique , denoted as that satisfies (11).

Up to this point, the corrected objective function below can be provided as

with the conditions .

Given , with and (22), can be derived. Given a that satisfies , then, by (23), can also be obtained. We have learned from above discussion that only and are independent variables.

Hence, the problem becomes the minimization of under two restrictions:

We have derived a two-variable minimum problem of and under the conditions of (29), for the infinite horizon minimum cost inventory model. The findings are concluded in the next theorem.

Theorem 5. For the production inventory model with infinite planning horizon, if one minimizes the average cost for the first replenishment cycle, then there are two necessary conditions, and , for the production inventory model of Su and Lin [5].

5. Numerical Examples for Infinite Planning Horizon Inventory Model

We will employ the same numerical examples as Su and Lin [5] for comparison purposes where , , , , , , , , , and . Some computation results are showed in Table 1 with arranged according to a sequence of different values of .

Table 1: The results for average cost of the first cycle for infinite planning horizon.

1.4683
2.2261
2.3148
2.3885
88.8785

2.4280
4.6512
4.9220
4.9876
102.8907

3.2554
9.1627
9.9433
9.9794
108.9192

3.6192
17.6260
19.9899
19.9985
77.4016

3.6626
25.4135
29.9900
29.9912
54.0973

3.6695
37.6621
477.5669
477.5670
3.4965

3.6696
39.0597
1745.8406
1745.8407
0.9889

3.6698
46.3253
5929.4429
5929.4434
0.2818

3.6698
46.3253
14903.3271
14903.3281
0.1158

3.6698
46.3253
19998.2832
19998.2852
0.0879

From the numerical examples in Table 1, it reveals that if we prolong the replenishment cycle, then the average cost will eventually decrease. The rationale is that with a negatively exponential demand function, the market demand will dramatically decrease, especially in a longer inventory horizon, which will in turn significantly bring down the corresponding average holding and shortage costs. On the other hand, when we prolong the shortage phase with (the personal computer’s computational limit) in order to reduce the average cost, the ordinary customers may lose patience when waiting for the backorder. Hence, a full backorder cannot be performed. In other words, it is impossible to simultaneously achieve full backorder and minimize average inventory cost. We may conclude that for the negatively exponential demand, , the infinite planning horizon production inventory model is not adequate. Therefore, we stop the discussion of case (a) in the infinite planning horizon.

6. Our Proposed Production Inventory Model with Finite Planning Horizon

Next, we consider case (b) with a finite planning horizon, denoted by . To simplify the discussion, we assume that there is one replenishment cycle during the finite planning horizon. Our results can be easily extended to several replenishment cycles. In this setting, (28) should be revised as follows:

Here, we will derive a stronger condition than for the feasible domain of . For a given , from (10), since is an increasing function of , there is a unique point, say with . Under the condition

the desired result is achieved, since is an increasing function of .

Lemma 6. For a given , the feasible domain of is , implying that with .

In the following, when is given, if we take a that satisfies (31), then we will prove that there is a unique that satisfies (23).

Based on (23), let us assume other two auxiliary functions, and , where

With a restricted domain, is related to our previous auxiliary function of (24) and

From , under the conditions and , is an increasing function which implies that

On the other hand, the expression

shows that is an increasing function. If we apply (33) and (34), then increases from

to

Therefore, there is a unique point, say , that satisfies

When is given, based on the previous discussion, if is given with , then we have . From (38), there exists a unique point, , with such that , , and satisfy (23). Hence, for a finite-horizon minimum cost inventory model, we have simplified a four-variable problem to a one-variable problem.

Hence, in the following, if we only consider those s that satisfy the condition of (31), then

By (23) and (38), the relation, , implies that

where , defined in Lemma 4, satisfies (11).

The objective function becomes a one-variable problem

We will summarize our findings in the next theorem.

Theorem 7. For the production inventory model with the finite planning horizon, , if one only considers one replenishment cycle, there is a natural restriction , creating a one-variable minimum problem.

From Theorem 7, computer program as MathCAD can be adopted to locate the optimal solution. We may point out that the benefits to the simplified production inventory model that we have proposed include (a) easy to use for decision makers, (b) reduction of the solution space (computation time) in determining the parameter setting, and (c) reduction of the model complexity.

7. Numerical Example for the Finite Planning Horizon

For the finite planning horizon production inventory model with the same data, , , , , , , , , , , and , we find the optimal solution, . With (12), it shows that . With (38), it shows that . Finally, with (41), we find that the minimum cost is . The above discussion is based on the preset condition that there is one replenishment cycle. However, under the multiple replenishment cycles, the total setup cost will be at least , and then the average cost during is more than one hundred that is larger than the result of one replenishment cycle. It specifies that the average cost for multiple replenishment cycles is much larger than that of one replenishment cycle. Hence, for this numerical example, we only consider one replenishment cycle.

Particle swarm optimization is applied to check our findings. Both approaches have the same optimal solution.

8. Conclusion and Further Direction

We have shown that with an exponentially decreasing demand, the goals of simultaneously minimizing the average cost for the first replenishment cycle and fully backordering the shortage items cannot be applied for an infinite-horizon minimum cost inventory model. The result of our investigation explicitly reveals that using the infinite planning horizon model is inappropriate in practice. For the finite planning horizon, we have shown that the four-phase production inventory model can be converted to a single variable problem in order to find the minimum solution. Our study not only provides a sound operational formulation but also offers a practical and efficient approach in the location of the optimal solution.

The study we have carried out can probably be viewed as the first attempt to solve a finite planning horizon production inventory model. In the future, it would be interesting to show that our objective function is convex to ensure the existence of a local minimum. Moreover, the issues of how to decide the optimal solution under several replenishment cycles and how to verify the convexity of the minimum value under multiple replenishment cycles deserve further study.

Acknowledgments

This paper is partially supported by the National Science Council of Taiwan, Taiwan, with Grant NSC 101-2410-H-015-02. The authors want to express their gratitude to Sophia Liu (sophia0liu@hotmail.com) for her English revisions of their paper and Professor Shih-Wei Lin for his help with particle swarm optimization.

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