# Fuzzy Constrained Probabilistic Inventory Models Depending on Trapezoidal Fuzzy Numbers

Authors: Mona F. El-Wakeel; Kholood O. Al-yazidi
Journal: Advances in Fuzzy Systems
 (2016)

---

## Abstract

We discussed two different cases of the probabilistic continuous review mixture shortage inventory model with varying and constrained expected order cost, when the lead time demand follows some different continuous distributions. The first case is when the total cost components are considered to be crisp values, and the other case is when the costs are considered as trapezoidal fuzzy number. Also, some special cases are deduced. To investigate the proposed model in the crisp case and the fuzzy case, illustrative numerical example is added. From the numerical results we will conclude that Uniform distribution is the best distribution to get the exact solutions, and the exact solutions for fuzzy models are considered more practical and close to the reality of life and get minimum expected total cost less than the crisp models.


---

## Body

## 1. Introduction

Inventory system is one of the most diversified fields of applied sciences that are widely used in a variety of areas including operations research, applied probability, computer sciences, management sciences, production system, and telecommunications. More than fifty years ago, the analysis of inventory system has appeared in the reference books and survey papers. Hadley and Whitin [1] are considered one of the first researchers who have discussed the analysis of inventory systems, where they displayed a method for the analysis of the mathematical model for inventory systems. Also, Balkhi and Benkherouf [2] have introduced production lot size inventory model in which products deteriorate at a constant rate and in which demand and production rates are allowed to vary with time. Inventory models may be either deterministic or probabilistic, since the demand of commodity may be deterministic or probabilistic, respectively. These cases were dealt with by Hadley and Whitin [1], Abuo-El-Ata et al. [3], and Vijayan and Kumaran [4].Some managers allow the shortage in inventory systems; this shortage may be backorder case, lost sales case, and mixture shortage case. Many authors are dealing with inventory problems with various shortage cases where the cost components are considered as crisp values which does not depict the real inventory system fully. For example, constrained probabilistic inventory model with varying order and shortage costs using Lagrangian method has been investigated by Fergany [5]. In addition, constrained probabilistic inventory model with continuous distributions and varying holding cost was discussed by Fergany and El-Saadani [6]. In 2006, several models of continuous distributions for constrained probabilistic lost sales inventory models with varying order cost under holding cost constraint using Lagrangian method by Fergany and El-Wakeel [7, 8] were discussed. Recently, El-Wakeel [9] deduced constrained backorders inventory system with varying order cost under holding cost constraint: lead time demand uniformly distributed using Lagrangian method. Also, El-Wakeel and Fergany [10] deduced constrained probabilistic continuous review inventory system with mixture shortage and stochastic lead time demand.Sometimes, the cost components are considered as fuzzy values, because, in real life, the various physical or chemical characteristics may cause an effect on the cost components and then precise values of cost characteristics become difficult to measure as the exact amount of order, holding, and especially shortage cost. Thus, in controlling the inventory system it may allow some flexibility in the cost parameter values in order to treat the uncertainties which always fit the real situations. Since we want to satisfy our requirements for such contradictions, the fuzzy set theory meets these requirements to some extent. In 1965, Zadeh [11] first introduced the fuzzy set theory which studied the intention to accommodate uncertainty in the nonstochastic sense rather than the presence of random variables. Syed and Aziz [12] have examined the fuzzy inventory model without shortages using signed distance method. Kazemi et al. [13] have treated the inventory model with backorders with fuzzy parameters and decision variables. Gawdt [14] presented a mixture continuous review inventory model under varying holding cost constraint when the lead time demand follows Gamma distribution, where the costs were fuzzified as the trapezoidal fuzzy numbers. The continuous review inventory model with mixture shortage under constraint involving crashing cost based on probabilistic triangular fuzzy numbers by Fergany and Gawdt [15] was discussed. A probabilistic periodic review inventory model using Lagrange technique and fuzzy adaptive particle swarm optimization was presented by Fergany et al. [16]. Fuzzy inventory model for deteriorating items with time dependent demand and partial backlogging is established by Kumar and Rajput [17]. Indrajitsingha et al. [18] give fuzzy inventory model with shortages under fully backlogged using signed distance method. Recently, Patel et al. [19] introduced the continuous review inventory model under fuzzy environment without backorder for deteriorating items.As we found earlier, many authors have studied the inventory models with different assumptions and conditions. These assumptions and conditions are represented in constraints and costs (constant or varying). Therefore, due to the importance of the inventory models we shall propose and study, in this paper, the mixture shortage inventory model with varying order cost under expected order cost constraint and the lead time demand follow Exponential, Laplace, and Uniform distributions. Our goal of studying the inventory models is to minimize the total cost. The order quantity and the reorder point are the policy variables for this model, which minimize the expected annual total cost. We evaluated the optimal order quantity and the reorder point in two cases: first case is when the cost components are considered as crisp values, and the second case is when the cost components are fuzzified as a trapezoidal fuzzy numbers, which is called the fuzzy case. Finally this work is illustrated by numerical example and we will make comparisons of all results and obtain conclusions.


## 2. Model Development

To develop any model of inventory models we need to put some notations and assumptions represented in Notations section.


### 2.1. Assumptions

(1)
Consider that continuous review inventory model under order cost constraint and shortages are allowed.
(2)
Demand is a continuous random variable with known probability.
(3)
The lead time is constant and follows the known distributions.
(4)
γ is a fraction of unsatisfied demand that will be backordered while the remaining fraction (1
-
γ) is completely lost, where 0
≤
γ
≤
1.
(5)
New order with size (Q) is placed when the inventory level drops to a certain level, called the reorder point (r); assume that the system repeats itself in the sense that the inventory position varies between r and r
+
Q during each cycle.


## 2.1. Assumptions

(1)
Consider that continuous review inventory model under order cost constraint and shortages are allowed.
(2)
Demand is a continuous random variable with known probability.
(3)
The lead time is constant and follows the known distributions.
(4)
γ is a fraction of unsatisfied demand that will be backordered while the remaining fraction (1
-
γ) is completely lost, where 0
≤
γ
≤
1.
(5)
New order with size (Q) is placed when the inventory level drops to a certain level, called the reorder point (r); assume that the system repeats itself in the sense that the inventory position varies between r and r
+
Q during each cycle.


## 3. Model (I): The Mixture Shortage Model Where the Cost Components Are Considered as Crisp Values

In this section, we consider that the continuous review inventory model with shortage is allowed. Some customers are willing to wait for the new replenishment and the others have no patience; this case is called mixture shortage or partial backorders.The expected annual total cost consisted of the sum of three components:(1)
E
T
o
t
a
l
C
o
s
t
=
E
o
r
d
e
r
C
o
s
t
+
E
H
o
l
d
i
n
g
C
o
s
t
+
E
S
h
o
r
t
a
g
e
C
o
s
t
,
E
T
C
Q
,
r
=
E
O
C
+
E
H
C
+
E
S
C
,where(2)
E
S
C
=
E
B
C
+
E
L
Cand we assume the varying order cost function, where the order cost is a decreasing function of the order quantity Q. Then, the expected order cost is given by(3)
E
O
C
=
c
o
Q
D
-
Q
=
c
o
Q
-
β
D
-
Q
=
c
o
D
-
Q
-
β
-
1
,
E
H
C
=
c
h
H
-
=
c
h
Q
2
+
r
-
E
x
+
1
-
γ
S
-
r
,
E
B
C
=
c
b
γ
D
-
Q
S
-
r
,
E
L
C
=
c
l
1
-
γ
D
-
Q
S
-
r
.Our objective is to minimize the expected total costs [m
i
n
E
(
T
C
(
Q
,
r
)
)] with varying order cost under the expected order cost constraint which needs to find the optimal values of order quantity Q and reorder point r. To solve this primal function, let us write it as follows:(4)
E
T
C
Q
,
r
=
c
o
D
-
Q
-
β
-
1
+
c
h
Q
2
+
r
-
E
x
+
1
-
γ
S
-
r
+
c
b
γ
D
-
Q
S
-
r
+
c
l
1
-
γ
D
-
Q
S
-
r
=
c
o
D
-
Q
-
β
-
1
+
c
h
Q
2
+
r
-
E
x
+
c
b
γ
D
-
Q
S
-
r
+
c
h
+
c
l
D
-
Q
1
-
γ
S
-
r
(5)
S
u
b
j
e
c
t
t
o
:
c
o
D
-
Q
-
β
-
1
≤
K
.We use the Lagrange multiplier technique to get the optimal values Q
∗ and r
∗ which minimize (4) under constraint (5) as follows:(6)
G
Q
,
r
,
λ
=
c
o
D
-
Q
-
β
-
1
+
c
h
Q
2
+
r
-
E
x
+
c
b
γ
D
-
Q
S
-
r
+
c
h
+
c
l
D
-
Q
1
-
γ
S
-
r
+
λ
c
o
D
-
Q
-
β
-
1
-
K
.Putting each of the corresponding first partial derivatives of (6) equal to zero at Q
=
Q
∗ and r
=
r
∗, respectively, we get (7)
c
h
Q
∗
2
+
B
D
-
Q
∗
-
β
-
2
A
S
-
r
∗
=
0
,
R
r
∗
=
c
h
Q
∗
c
h
1
-
γ
Q
∗
+
A
,where(8)
A
=
D
-
c
b
γ
+
c
l
1
-
γ
,
B
=
2
c
o
-
β
-
1
1
+
λ
.Clearly, it is difficult to find an exact solution of Q
∗ and r
∗ of (7), so we can suppose that the lead time demand follows some distributions.


### 3.1. Lead Time Demand Follows Exponential Distribution

Supposing that the lead time demand follows the Exponential distribution with parametersν, then its probability density function is given by(9)
f
x
=
ν
e
-
ν
x
;
x
≥
0
,
ν
>
0
w
i
t
h
E
x
=
1
ν
,
R
r
=
e
-
ν
r
,
S
-
r
=
1
ν
e
-
ν
r
.The optimal order quantity and the optimal reorder level which minimize the expected relevant annual total cost can be obtained by substituting (9) into (7). Solving them simultaneously we get(10)
ν
c
h
2
1
-
γ
Q
∗
3
+
ν
c
h
A
Q
∗
2
-
2
c
h
A
Q
∗
+
ν
c
h
1
-
γ
B
D
-
Q
∗
1
-
β
+
ν
A
B
D
-
Q
∗
-
β
=
0
,
r
∗
=
-
1
ν
ln
⁡
c
h
Q
∗
c
h
1
-
γ
Q
∗
+
A
,which give exact solutions for model (I).


### 3.2. Lead Time Demand Follows Laplace Distribution

If the lead time demand follows the Laplace distribution with parametersμ
,
θ, the probability density function will be (11)
f
x
=
1
2
θ
e
-
x
-
μ
/
θ
;
-
∞
<
x
<
∞
,
θ
>
0
w
i
t
h
E
x
=
μ
,
R
r
=
1
2
e
-
r
-
μ
/
θ
,
S
-
r
=
θ
2
e
-
r
-
μ
/
θ
.The optimal order quantity and the optimal reorder level which minimize the expected relevant annual total cost can be obtained by substituting (11) into (7), and, solving them simultaneously, we obtain(12)
c
h
2
1
-
γ
Q
∗
3
+
c
h
A
Q
∗
2
-
2
c
h
θ
A
Q
∗
+
c
h
1
-
γ
B
D
-
Q
∗
1
-
β
+
A
B
D
-
Q
∗
-
β
=
0
,
r
∗
=
μ
-
θ
ln
⁡
2
c
h
Q
∗
c
h
1
-
γ
Q
∗
+
A
,which give exact solutions for model (I).


### 3.3. Lead Time Demand Follows Uniform Distribution

Similarly, suppose that the lead time demand follows the Uniform distribution over the range from 0 tob, that is, x
~
U
n
i
f
o
r
m
(
0
,
b
); then its probability density function is given by(13)
f
x
=
1
b
;
0
≤
x
≤
b
w
i
t
h
E
x
=
b
2
,
R
r
=
1
-
r
b
,
S
-
r
=
1
2
b
r
-
b
2
.The optimal order quantity and the optimal reorder level which minimize the expected relevant annual total cost can be obtained by substituting (13) into (7). Solving them simultaneously, we find(14)
c
h
3
1
-
γ
2
Q
∗
4
+
2
c
h
2
1
-
γ
A
Q
∗
3
+
c
h
A
A
-
b
c
h
Q
∗
2
+
c
h
2
1
-
γ
2
B
D
-
Q
∗
2
-
β
+
2
c
h
1
-
γ
A
B
D
-
Q
∗
1
-
β
+
A
2
B
D
-
Q
∗
-
β
=
0
,
r
∗
=
b
1
-
c
h
Q
∗
c
h
1
-
γ
Q
∗
+
A
,which give exact solutions for model (I).Thus, the exact solution for constrained continuous review inventory model with mixture shortage and varying order cost can obtained by solving previous equations for each distribution separately at different values ofβ and varying λ until the smallest positive value is found such that the constraint holds.


## 3.1. Lead Time Demand Follows Exponential Distribution

Supposing that the lead time demand follows the Exponential distribution with parametersν, then its probability density function is given by(9)
f
x
=
ν
e
-
ν
x
;
x
≥
0
,
ν
>
0
w
i
t
h
E
x
=
1
ν
,
R
r
=
e
-
ν
r
,
S
-
r
=
1
ν
e
-
ν
r
.The optimal order quantity and the optimal reorder level which minimize the expected relevant annual total cost can be obtained by substituting (9) into (7). Solving them simultaneously we get(10)
ν
c
h
2
1
-
γ
Q
∗
3
+
ν
c
h
A
Q
∗
2
-
2
c
h
A
Q
∗
+
ν
c
h
1
-
γ
B
D
-
Q
∗
1
-
β
+
ν
A
B
D
-
Q
∗
-
β
=
0
,
r
∗
=
-
1
ν
ln
⁡
c
h
Q
∗
c
h
1
-
γ
Q
∗
+
A
,which give exact solutions for model (I).


## 3.2. Lead Time Demand Follows Laplace Distribution

If the lead time demand follows the Laplace distribution with parametersμ
,
θ, the probability density function will be (11)
f
x
=
1
2
θ
e
-
x
-
μ
/
θ
;
-
∞
<
x
<
∞
,
θ
>
0
w
i
t
h
E
x
=
μ
,
R
r
=
1
2
e
-
r
-
μ
/
θ
,
S
-
r
=
θ
2
e
-
r
-
μ
/
θ
.The optimal order quantity and the optimal reorder level which minimize the expected relevant annual total cost can be obtained by substituting (11) into (7), and, solving them simultaneously, we obtain(12)
c
h
2
1
-
γ
Q
∗
3
+
c
h
A
Q
∗
2
-
2
c
h
θ
A
Q
∗
+
c
h
1
-
γ
B
D
-
Q
∗
1
-
β
+
A
B
D
-
Q
∗
-
β
=
0
,
r
∗
=
μ
-
θ
ln
⁡
2
c
h
Q
∗
c
h
1
-
γ
Q
∗
+
A
,which give exact solutions for model (I).


## 3.3. Lead Time Demand Follows Uniform Distribution

Similarly, suppose that the lead time demand follows the Uniform distribution over the range from 0 tob, that is, x
~
U
n
i
f
o
r
m
(
0
,
b
); then its probability density function is given by(13)
f
x
=
1
b
;
0
≤
x
≤
b
w
i
t
h
E
x
=
b
2
,
R
r
=
1
-
r
b
,
S
-
r
=
1
2
b
r
-
b
2
.The optimal order quantity and the optimal reorder level which minimize the expected relevant annual total cost can be obtained by substituting (13) into (7). Solving them simultaneously, we find(14)
c
h
3
1
-
γ
2
Q
∗
4
+
2
c
h
2
1
-
γ
A
Q
∗
3
+
c
h
A
A
-
b
c
h
Q
∗
2
+
c
h
2
1
-
γ
2
B
D
-
Q
∗
2
-
β
+
2
c
h
1
-
γ
A
B
D
-
Q
∗
1
-
β
+
A
2
B
D
-
Q
∗
-
β
=
0
,
r
∗
=
b
1
-
c
h
Q
∗
c
h
1
-
γ
Q
∗
+
A
,which give exact solutions for model (I).Thus, the exact solution for constrained continuous review inventory model with mixture shortage and varying order cost can obtained by solving previous equations for each distribution separately at different values ofβ and varying λ until the smallest positive value is found such that the constraint holds.


## 4. Model (If): The Mixture Shortage Model Where the Cost Components Are Considered as Fuzzy Numbers

Consider continuous review inventory model similar to model (I), but assuming that the cost componentsc
o
,
c
h
,
c
b, and c
l are all fuzzy numbers, to control various uncertainties from various physical or chemical characteristics where there may be an effect on the cost components.We represent these costs by trapezoidal fuzzy numbers as given below:(15)
c
~
o
=
c
o
-
δ
1
,
c
o
-
δ
2
,
c
o
+
δ
3
,
c
o
+
δ
4
,
c
~
h
=
c
h
-
δ
5
,
c
h
-
δ
6
,
c
h
+
δ
7
,
c
h
+
δ
8
,
c
~
b
=
c
b
-
θ
1
,
c
b
-
θ
2
,
c
b
+
θ
3
,
c
b
+
θ
4
,
c
~
l
=
c
l
-
θ
5
,
c
l
-
θ
6
,
c
l
+
θ
7
,
c
l
+
θ
8
,where δ
i and θ
i
,
i
=
1,2
,
…
,
8 are arbitrary positive numbers and should satisfy the following constraints:(16)
c
o
>
δ
1
>
δ
2
,
δ
3
<
δ
4
,
c
h
>
δ
5
>
δ
6
,
δ
7
<
δ
8
.Similarly,(17)
C
b
>
θ
1
>
θ
2
,
θ
3
<
θ
4
,
c
l
>
θ
5
>
θ
6
,
θ
7
<
θ
8
.We can represent the order cost as a trapezoidal fuzzy number as shown in Figure 1 and similarly for the remaining costs.Figure 1
Order cost as a trapezoidal fuzzy number.Note that the membership function ofc
~
o is 1 at points c
o
-
δ
2 and c
o
+
δ
3, decreases as the point deviates from c
o
-
δ
2 and c
o
+
δ
3, and reaches zero at the endpoints c
o
-
δ
1 and c
o
+
δ
4.The left and right limits ofα – c
u
t of c
o
,
c
h
,
c
b, and c
l are given as follows:(18)
c
~
o
v
α
=
c
o
-
δ
1
+
δ
1
-
δ
2
α
,
c
~
o
u
α
=
c
o
+
δ
4
-
δ
4
-
δ
3
α
,
c
~
h
v
α
=
c
h
-
δ
5
+
δ
5
-
δ
6
α
,
c
~
h
u
α
=
c
h
+
δ
8
-
δ
8
-
δ
7
α
,
c
~
b
v
α
=
c
b
-
θ
1
+
θ
1
-
θ
2
α
,
c
~
b
u
α
=
c
b
+
θ
4
-
θ
4
-
θ
3
α
,
c
~
l
v
α
=
c
l
-
θ
5
+
θ
5
-
θ
6
α
,
c
~
l
u
α
=
c
l
+
θ
8
-
θ
8
-
θ
7
α
.The expected annual total cost for this case under the expected order cost constraint and when all cost components are fuzzy can be expressed as follows:(19)
E
~
c
~
o
,
c
~
h
,
c
~
b
,
c
~
l
=
c
~
o
D
-
Q
-
β
-
1
+
c
~
h
Q
2
+
r
-
E
x
+
1
-
γ
S
-
r
+
c
~
b
γ
D
-
Q
S
-
r
+
c
~
l
1
-
γ
D
-
Q
S
-
r
=
c
~
o
D
-
Q
-
β
-
1
+
c
~
h
Q
2
+
r
-
E
x
+
c
~
b
γ
D
-
Q
S
-
r
+
c
~
h
+
c
~
l
D
-
Q
1
-
γ
S
-
r
(20)
S
u
b
j
e
c
t
t
o
:
c
~
o
D
-
Q
-
β
-
1
≤
K
.We use the Lagrange multiplier technique to find the optimal values Q
∗ and r
∗ which minimize (19) under constraint (20) as follows:(21)
G
~
c
~
o
,
c
~
h
,
c
~
b
,
c
~
l
=
c
~
o
D
-
Q
-
β
-
1
+
c
~
h
Q
2
+
r
-
E
x
+
c
~
b
γ
D
-
Q
S
-
r
+
c
~
h
+
c
~
l
D
-
Q
1
-
γ
S
-
r
+
λ
c
~
o
D
-
Q
-
β
-
1
-
K
.We can obtain the form of left and right α – c
u
t of the fuzzified cost function (21), respectively, as follows:(22)
G
~
c
~
o
,
c
~
h
,
c
~
b
,
c
~
l
v
α
=
c
~
o
v
D
-
Q
-
β
-
1
+
c
~
h
v
Q
2
+
r
-
E
x
+
c
~
b
v
γ
D
-
Q
S
-
r
+
c
~
h
v
+
c
~
l
v
D
-
Q
1
-
γ
S
-
r
+
λ
c
~
o
v
D
-
Q
-
β
-
1
-
K
,
G
~
c
~
o
,
c
~
h
,
c
~
b
,
c
~
l
u
α
=
c
~
o
u
D
-
Q
-
β
-
1
+
c
~
h
u
Q
2
+
r
-
E
x
+
c
~
b
u
γ
D
-
Q
S
-
r
+
c
~
h
u
+
c
~
l
u
D
-
Q
1
-
γ
S
-
r
+
λ
c
~
o
u
D
-
Q
-
β
-
1
-
K
.Since G
~
v
α and G
~
u
α exist and are integrable for α
∈
0,1, as in Yao and Wu [20], we have(23)
d
G
~
,
0
~
=
1
2
∫
0
1
G
~
v
α
+
G
~
u
α
d
α
.We get the defuzzified value of  G
~
(
c
~
o
,
c
~
h
,
c
~
b
,
c
~
l
)
(
α
) by using (23) for (22) as follows:(24)
d
G
~
,
0
~
=
A
1
D
-
Q
-
β
-
1
+
A
2
Q
2
+
r
-
E
x
+
A
3
γ
D
-
Q
S
-
r
+
A
2
+
A
4
D
-
Q
1
-
γ
S
-
r
+
λ
A
1
D
-
Q
-
β
-
1
-
K
,where(25)
A
1
=
4
c
o
-
δ
1
-
δ
2
+
δ
3
+
δ
4
4
,
A
2
=
4
c
h
-
δ
5
-
δ
6
+
δ
7
+
δ
8
4
,
A
3
=
4
c
b
-
θ
1
-
θ
2
+
θ
3
+
θ
4
4
,
A
4
=
4
c
l
-
θ
5
-
θ
6
+
θ
7
+
θ
8
4
.Similarly, as in model (I), to get the optimal values Q
∗ and r
∗ put each of the corresponding first partial derivatives of (24) equal to zero at Q
=
Q
∗ and r
=
r
∗, respectively; we obtain(26)
2
A
1
-
β
-
1
D
-
Q
∗
-
β
1
+
λ
+
A
2
Q
∗
2
-
2
A
3
γ
D
-
S
-
r
-
2
A
4
1
-
γ
D
-
S
-
r
=
0and the probability of the shortage is(27)
R
r
∗
=
A
2
Q
∗
A
2
1
-
γ
Q
∗
+
A
3
γ
D
-
+
A
4
1
-
γ
D
-
.Clearly, there is no closed form solution of (26) and (27). We can solve these equations by using the same manner as in model (I).


## 5. Special Cases

(1)
Lettingγ
=
0, β
=
0 and K
→
∞
⇒
C
o
Q
=
c
o and λ
=
0, thus A
=
c
l
D
-, B
=
-
2
c
oand hence (7) reduces to(28)
Q
∗
=
2
D
-
c
o
+
c
l
s
-
r
∗
c
h
,
R
r
∗
=
c
h
Q
∗
c
h
Q
∗
+
c
l
D
-
.This is an unconstrained lost sales continuous review inventory model with constant units of costs, which are the same results as in Hadley and Whitin [1].
(2)
Lettingγ
=
1, β
=
0 and K
→
∞
⇒
C
o
Q
=
c
o and λ
=
0, thus A
=
c
b
D
-, B
=
-
2
c
o; thus (7) reduces to(29)
Q
∗
=
2
D
-
c
o
+
c
b
s
-
r
∗
c
h
,
R
r
∗
=
c
h
Q
∗
c
b
D
-
.This is an unconstrained backorders continuous review inventory model with constant units of costs, which are the same results as in Hadley and Whitin [1].(i)
Equations (10) give unconstrained backorders continuous review of inventory model with constant units of cost and the lead time demand follows the Exponential distribution, which are the same results as in Hillier and Lieberman [21].
(ii)
Equations (12) give unconstrained backorders continuous review inventory model with constant units of cost and the lead time demand follows the Laplace distribution, which agree with results of Nahmias [22].
(iii)
Equations (14) give unconstrained backorders continuous review inventory model with constant units of cost and the lead time demand follows the Uniform distribution, which are the same results as in Fabrycky and Banks [23].


## 6. Numerical Example

Consider an inventory system with the following data:D
-
=
1050 units per year,
c
o
=
70 SR per unit ordered,
c
h
=
25 SR per unit per year,
c
b
=
7 SR per unit backorder,
c
l
=
15 SR per unit lost,
the backorder fraction has the valuesγ
=
0.1, γ
=
0.3, and γ
=
0.7,
letK
=
140 SR,and take(30)
δ
1
=
60
,
δ
2
=
48
,
δ
3
=
10
,
δ
4
=
50
,
δ
5
=
19
,
δ
6
=
10
,
δ
7
=
1
,
δ
8
=
2
,
θ
1
=
6
,
θ
2
=
4
,
θ
3
=
2
,
θ
4
=
4
,
θ
5
=
12
,
θ
6
=
7
,
θ
7
=
1
,
θ
8
=
2
.DetermineQ
∗ and r
∗ for both cases of the previous model, when the lead time demand has the following distributions:(i)
Exponential distribution withν
=
0.077 units.
(ii)
Laplace distribution withμ
=
13 and θ
=
10 units.
(iii)
Uniform distribution withb
=
26 units.Depending on the above data, we can obtain all results by solving the previous deduced equations at different values ofβ, λ, and γ as shown in the Tables 1, 2, and 3 which give the optimal values of Q
∗ and r
∗ that minimize the expected total cost, when the lead time demand follows Exponential, Laplace, and Uniform distribution, respectively, for model (I) and model (I
f).Table 1
The exact solutions andmin
⁡
E(TC) for model (I) and model (I
f) at Exponential distribution.
γ
β
Crisp case
Fuzzy case
Q
∗
r
∗
min
⁡
E(TC)
Q
∗
r
∗
min
⁡
E(TC)
0.1
0.1
297.092
13.8608
4199.84
250.412
15.426
2741.44
0.2
184.893
18.4055
2910.93
158.061
20.016
1972.09
0.3
123.76
22.6467
2252.78
107.107
24.237
1578.8
0.4
87.6955
26.5107
1898.63
76.6735
28.054
1367.98
0.5
65.1132
29.9807
1703
57.4583
31.4585
1253.06
0.6
50.1308
33.1065
1593.97
46.7207
33.9499
1189.84
0.7
41.8943
35.2866
1533.94
43.0554
34.9436
1146.05
0.8
39.0054
36.1611
1491.79
39.9907
35.846
1112.46
0.3
0.1
297.079
11.8026
4148.23
250.456
13.6144
2708.31
0.2
184.905
16.4977
2863.37
158.08
18.3589
1941.59
0.3
123.759
20.8394
2207.59
107.132
22.6783
1550.15
0.4
87.7063
24.768
1855.18
76.7397
26.5535
1340.67
0.5
65.1619
28.2749
1660.8
57.4816
30.0074
1226.34
0.6
50.1482
31.4365
1552.36
46.8468
32.4964
1163.56
0.7
41.9983
33.6078
1492.73
43.1748
33.498
1119.93
0.8
39.1053
34.4876
1450.74
40.1052
34.4067
1086.48
0.7
0.1
297.118
6.33535
4012.01
250.446
8.99977
2622.85
0.2
184.851
11.5619
2739.35
158.081
14.2364
1865.33
0.3
123.742
16.2344
2092.27
107.118
18.864
1479.48
0.4
87.6963
20.3768
1745.29
76.7206
22.9372
1273.64
0.5
65.1605
24.0241
1554.52
57.4204
26.5318
1161.7
0.6
50.2168
27.266
1448.65
47.2184
28.9822
1100.36
0.7
42.327
29.4105
1390.22
43.5262
30.0069
1057.21
0.8
39.4206
30.3066
1348.72
40.4411
30.9342
1024.17Table 2
The exact solutions andmin
⁡
E(TC) for model (I) and model (
I
f) at Laplace distribution.
γ
β
Crisp case
Fuzzy case
Q
∗
r
∗
min
⁡
E(TC)
Q
∗
r
∗
min
⁡
E(TC)
0.1
0.1
297.123
16.7406
4197.53
250.409
17.9466
2732.79
0.2
184.844
20.2428
2881.62
158.099
21.4789
1944.2
0.3
123.72
23.5092
2199.23
107.093
24.7322
1532.59
0.4
87.7213
26.4792
1823.44
76.7474
27.6615
1305.96
0.5
65.081
29.1581
1607.46
57.4314
30.2959
1176.14
0.6
50.1314
31.5604
1480.65
44.6116
32.6421
1100.83
0.7
39.9135
33.6954
1405.74
40.0084
33.6658
1052.46
0.8
35.8352
34.715
1357.91
36.8761
34.4363
1014.82
0.3
0.1
297.09
15.1563
4157.53
250.449
16.5519
2707.33
0.2
184.858
18.7738
2845.06
158.053
20.2063
1920.28
0.3
123.74
22.1162
2164.62
107.069
23.536
1510.28
0.4
87.6956
25.141
1789.72
76.6717
26.5228
1284.38
0.5
65.0979
27.8493
1574.89
57.4223
29.1839
1155.52
0.6
50.2117
30.2628
1448.85
44.5733
31.5604
1080.66
0.7
39.8244
32.4508
1374.04
40.0876
32.5659
1032.46
0.8
35.9
33.4388
1326.43
36.9506
33.3419
994.931
0.7
0.1
297.084
10.9477
4052.24
250.405
12.9997
2641.24
0.2
184.853
14.9711
2749.93
158.116
17.0285
1862.01
0.3
123.776
18.5665
2076.28
107.095
20.5958
1456.08
0.4
87.7171
21.7564
1705.32
76.744
23.7273
1233.15
0.5
65.084
24.5783
1492.99
57.4997
26.4847
1106.02
0.6
50.1989
27.0667
1368.85
44.5736
28.9432
1032.24
0.7
39.9021
29.2867
1295.45
40.3191
29.9172
984.487
0.8
36.1029
30.2592
1248.3
37.1679
30.7094
947.275Table 3
The exact solutions andmin
⁡
E(TC) for model (I) and model (I
f) at Uniform distribution.
γ
β
Crisp case
Fuzzy case
Q
∗
r
∗
min
⁡
E(TC)
Q
∗
r
∗
min
⁡
E(TC)
0.1
0.1
297.124
17.0568
4067.24
250.44
18.0722
2623.71
0.2
184.926
19.6971
2697.71
158.084
20.4323
1791.22
0.3
123.756
21.4539
1955.07
107.098
21.978
1333.88
0.4
87.7442
22.6221
1519.47
76.748
22.9994
1062.47
0.5
65.1225
23.415
1246.58
57.4541
23.6935
890.457
0.6
50.187
23.9661
1066.66
44.6672
24.1744
776.304
0.7
39.8908
24.3597
942.705
35.7373
24.5207
696.802
0.8
32.4803
24.6502
853.887
29.23
24.7787
639.579
0.3
0.1
297.121
15.5207
4048
250.42
16.8872
2612.58
0.2
184.862
18.7021
2684.53
158.097
19.6747
1784.32
0.3
123.725
20.7762
1946.25
107.066
21.4673
1328.91
0.4
87.7476
22.1372
1513.44
76.7256
22.6354
1058.95
0.5
65.132
23.0538
1242.15
57.475
23.421
888.053
0.6
50.1549
23.6892
1062.94
44.6562
23.9647
774.318
0.7
39.8284
24.1411
939.565
35.7041
24.3546
695.179
0.8
32.4725
24.4703
851.602
29.2817
24.6397
638.328
0.7
0.1
297.1
10.0378
3979.21
250.426
12.9987
2576.66
0.2
184.862
15.3252
2642.32
158.11
17.311
1762.56
0.3
123.72
18.5524
1918.4
107.112
19.9169
1314.92
0.4
87.7011
20.5852
1493.56
76.6763
21.5568
1048.64
0.5
65.1351
21.9128
1227.92
57.4479
22.6277
880.563
0.6
50.1559
22.8182
1052.06
44.6218
23.3576
768.557
0.7
39.8823
23.4508
931.288
35.6999
23.873
690.713
0.8
32.4596
23.9138
844.584
29.3328
24.2447
634.709From Table1 we have thatatγ
=
0.1, we will make backorders by 10% of new orders quantity;
atγ
=
0.3, we will make backorders by 30% of new orders quantity;
atγ
=
0.7, we will make backorders by 70% of new orders quantity.After comparison of the crisp case and fuzzy case for Exponential distribution, we can deduce that the leastmin
⁡
E
(
T
C
) was obtained at γ
=
0.7. We can draw the minimum expected total cost for model (I) and model (I
f) against β for the Exponential distribution at γ
=
0.7 as shown in Figure 2.Figure 2
The comparison between the crisp and fuzzy cases for Exponential atγ
=
0.7.From Table2 we have thatatγ
=
0.1, we will make backorders by 10% of new orders quantity;
atγ
=
0.3, we will make backorders by 30% of new orders quantity;
atγ
=
0.7, we will make backorders by 70% of new orders quantity.After comparison of the crisp case and fuzzy case for Laplace distribution, we can deduce that the leastmin
⁡
E
(
T
C
) was obtained at γ
=
0.7. We can draw the minimum expected total cost for model (I) and model (I
f) against β for the Laplace distribution at γ
=
0.7 as shown in Figure 3.Figure 3
The comparison between the crisp and fuzzy cases for Laplace atγ
=
0.7.From Table3 we have thatatγ
=
0.1, we will make backorders by 10% of new orders quantity;
atγ
=
0.3, we will make backorders by 30% of new orders quantity;
atγ
=
0.7, we will make backorders by 70% of new orders quantity.After comparison of the crisp case and fuzzy case for Uniform distribution, we can deduce that the leastmin
⁡
E
(
T
C
) was obtained at γ
=
0.7. We can draw the minimum expected total cost for model (I) and model (I
f) against β for the Uniform distribution at γ
=
0.7 as shown in Figure 4.Figure 4
The comparison between the crisp and fuzzy cases for Uniform atγ
=
0.7.


## 7. Conclusion

In this study we discussed two cases for mixture shortage inventory model under varying order cost constraint when lead time demand follows Exponential, Laplace, and Uniform distributions. We have evaluated the exact solutions ofQ
∗ and r
∗ for each value of β andλ
∗ which yields our expected order cost constraint and then obtain the minimum expected total cost by using Lagrangian multiplier technique.By comparing between the minimum expected total cost for model (I) and model (I
f) at each distribution, we can deduce that the least min
⁡
E
(
T
C
) was obtained when the lead time demand follows Uniform distribution and equals 844.584 SR with order quantity Q
∗
=
32.4596 and reorder point r
∗
=
23.9138 for model (I), while the minimum expected annual total cost for model (I
f) is 634.709 SR with order quantity Q
∗
=
29.3328 and reorder point r
∗
=
24.2447 as shown in Table 3. This means that we can conclude that the minimum expected total cost in fuzzy case is less than in the crisp case, which indicates that the fuzziness is very close to the actuality of life and gets minimum expected total cost less than the crisp case.For the results of the numerical example, we note that whenβ increases, r
∗ increases, and thus Q
∗ decreases which indicate that the min
⁡
E
(
T
C
) decreases.Also, the different values ofβ lead to changes of Q
∗ in each distribution separately. But in all distributions we note that values of Q
∗ are almost fixed, due to the constraint on the varying order cost. Also, we note that when γ increases, min
⁡
E
(
T
C
) decreases; this indicates that 70% of the shortages can be met at the lowest possible cost.Finally, our study in particular provides the ample scope for further research and exploration. For instance, we have considered probabilistic mixture shortage inventory model under varying order cost constraint. This work can be further developed by considering an ample range of different assumptions and conditions represented in constraints and costs (constant or varying), such as varying two costs under two constraints or varying two costs under constraint or varying one cost under two constraints. Also, we can study some of the inventory models with the system multiechelon-multisource.



---

*Source: 3673267-2016-09-22.xml*