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Expected Value of Exponential Fuzzy Number and Its Application to Multiitem Deterministic Inventory Model for Deteriorating Items
 Totan Garai^{1},
 Dipankar Chakraborty^{2}Email author and
 Tapan Kumar Roy^{1}
https://doi.org/10.1186/s4046701700627
© The Author(s) 2017
 Received: 13 April 2017
 Accepted: 3 August 2017
 Published: 16 August 2017
Abstract
Possibility, necessity, and credibility measures play a significant role to measure the chances of occurrence of fuzzy events. In this paper, possibility, necessity, and credibility measures of exponential fuzzy number, and its expected value has been derived. A multiitem twowarehouse deterministic inventory model for deteriorating items with stockdependent demand has been developed. For the proposed inventory model, the different costs and other parameters are considered in exponential fuzzy nature. Solution methodology of this model using expected value has been discussed. A numerical example is considered to illustrate the multiitem twowarehouse deterministic inventory model. Finally, few sensitivity analyses are presented under different rates of deterioration to check the validity of the proposed model.
Keywords
 Exponential fuzzy variable
 Possibility
 Necessity
 Credibility
 Expected value
 Multiitem inventory
 Deteriorating items
Introduction
Commonly speaking, uncertainty is usual to all reallife problems, for example fuzziness and randomness. Since Zadeh [1] introduced the fuzzy set theory, it has been well developed and applied in a wide variety of reallife problems. Possibility theory was proposed by Zadeh [2] and developed by many researchers, e.g., Dubois and Prade [3], Klir [4], Yager [5] and others. A self dual measure called credibility measure was introduced by Liu and Liu [6]. The mean value of a fuzzy number was introduced by Dubois and Prade [7]. Thereafter, Carlsson and Fuller [8] defined a possiblistic mean and variance of fuzzy numbers. The expected value of fuzzy variable using possibility theory was proposed by Hilpern [9], and application of expected value operator called expected value model was introduced by Liu and Liu [6].
Classical economic order quantity model which was developed in 1965 had the specific requirement of deterministic cost and demand. Classical inventory models generally deal with a single item. But in realworld situations, a singleitem inventory rarely occurs and multiitem inventory is common. Ghare and Schrader [10] were the first researchers to develop an economic order quantity model (EOQ) for an item with exponential decay. A multiitem inventory model with constant demand and infinite replenishment under the restriction on total average shortage, shortage area, and total average inventory investment cost developed by Das et al. [11]. Maiti and Maiti [12] investigated a production policy for damageable items with variable cost function in an imperfect production process, and Mondal and Maiti [13] developed a multiitem EOQ model. Recently, many researchers studied on multiitem EOQ inventory model such as Mousavi et al. [14], Pasandideh et al. [15], Wee et al. [16], Lau and Lau [17], Nahmias and Schmidt [18], and Vairaktarakis [19].
In general, deterioration in the inventory system is defined as damage, decay, spoilage, evaporation, pilferage, obsolescence, etc.. The classical economic order quantity (EOQ) inventory model developed in 1915 had the specific requirements of deterministic cost and lack of deterioration of the stock item. A finite rate of production with a variable rate of deterioration inventory model was formulated by Misra [20]. Raafat [21] was the first researcher to develop a continuous deterioration of the onhand inventory model and later more discussion by Goyel and Giri [22]. From then on, the inventory models of deteriorating items in different manners were developed by many researchers such as Benkherouf [23], Cohen [24], Kang and Kim [25], Goyal and Gunasekaran [26].
The deterministic inventory model with two levels of shortage and infinite replenishment rates developed by Sarma [27] and this model extended by Murdeshwar and Sathe [28]. Pakkala and Achary [29] improved a deterministic inventory model for deteriorating items with two warehouses and finite replenishment rate. Bhunia and Maiti [30] proposed a twowarehouse inventory model for deteriorating items with a linear trend in demand and shortages. Goswami and Chaudhuri [31] first introduced the inventory model with two storage and stockdependent demand rates. A multiwarehouse inventory model for multiitems with timevarying demand and shortage was developed by Zhou [32].
In many cases, the parameters in inventory problems may not be crisp and be somewhat vague in nature. For example, the holding cost for an item is supposed to be dependent on the amount of storage. Similarly, the replenishment cost depends upon the total quantity to be produced in a scheduling period. Moreover, because of the inventory system, the total profit in a scheduling period may be uncertain, and uncertainties may be associated with these variables and the above goals and parameters are normally vague and imprecise, i.e., fuzzy in nature. Maximum total average profit is imprecise in a practical inventory problem. In these situations, fuzzy set theory can be used for the formulation of inventory models. Maity and Maiti [33] solved a multiitem fuzzy inventory model with possibility and necessity constraints. Yao and Lee [34] developed an inventory model considering fuzzy order quantity, fuzzy production quantity, and fuzzy demand. Nia et al. [35] developed a multiitem EOQ model under shortage with fuzzy vendor managed. Recently, many researchers such as Kar et al. [36] and Roy and Maiti [37] have developed several fuzzy inventory models.

Possibility, necessity, and credibility measures of exponential trapezoidal fuzzy number

Expected value of exponential trapezoidal fuzzy number based on credibility measure

Multiitem twowarehouse deterministic inventory model for deteriorating items with stock dependent demand

Expected value method to solve a multiitem inventory model for deteriorating items
The rest of the paper is organized as follows: In Section “Basic Preliminaries”, we present some basic knowledge of exponential trapezoidal fuzzy number and its arithmetic operations. Section “Possibility Necessity and Credibility Measures of Exponential Trapezoidal Fuzzy Number” provides the possibility, necessity, and credibility measures of exponential trapezoidal fuzzy number and its graphical representation. In Section “Expected Value of Exponential Trapezoidal Fuzzy Number”, expected value of exponential trapezoidal fuzzy number has been discussed. Section“Assumption and Notations” shows the assumptions and notations for the mathematical model. In Section“Mathematical Formulation”, mathematical formulations are derived and the solution procedure is discussed. Numerical examples are given in Section“Numerical Illustration” to validate the proposed method. In Section “Sensitivity Analysis”, sensitivity analysis is made for the change of deterioration and is depicted using figures. Finally, conclusion and the scope of future research come in Section “Conclusion”.
Basic Preliminaries
Definition 1
Definition 2
 (i)
\(\tilde {a}\oplus \tilde {b}=\{a_{1}+b_{1}, a_{2}+b_{2}, a_{3}+b_{3}, a_{4}+b_{4}\}\)
 (ii)
\(\tilde {a}\ominus \tilde {b}=\{a_{1}b_{4}, a_{2}b_{3}, a_{3}b_{2}, a_{4}b_{1}\}\)
 (iii)
\(\tilde {a}\otimes \tilde {b}\approx \{a_{1}b_{1}, a_{2}b_{2}, a_{3}b_{3}, a_{4}b_{4}\}\)
 (iv)
\(k\tilde {a}=\left \{ \begin {array}{ll} \left (ka_{1},ka_{2},ka_{3},ka_{4}\right) & \quad \text {if } k \geq 0\\ \left (ka_{4},ka_{3},ka_{2},ka_{1}\right) & \quad \text {if } k<0 \end {array}\right.\)
Definition 3
Definition 4
Possibility Necessity and Credibility Measures of Exponential Trapezoidal Fuzzy Number
Expected Value of Exponential Trapezoidal Fuzzy Number
Definition 5
Theorem 1
Proof
Since there 3 cases, let’s discuss every case in turn
□
Remarks 1
Definition 6
Definition 7
Theorem 2
Definition 8
 (i)
\(\tilde {a}\prec \tilde {b}\) iff \(E(\tilde {a})<E(\tilde { b})\)
 (ii)
\(\tilde {a}\succ \tilde {b}\) iff \(E(\tilde {a})>E(\tilde { b})\)
 (iii)
\(\tilde {a}\simeq \tilde {b}\) iff \(E(\tilde {a})=E(\tilde { b})\)
Example 1
Solution:
Case 1: Let \(\tilde {a_{1}}=(2,4,7,8), \tilde {a_{2}}=(2,3,4,6), \tilde {a_{3}}=(1.5,2.5,3.5,5.5), \tilde {a_{4}}=(1,2.5,4,4.5),\tilde {a_{5}}=(3,4,5,7), \tilde {a_{6}}=(1,1.5,2,2.5), \tilde {a_{7}}=(2,3.5,4,6.5)\) and \(\tilde {a_{8}}=(2,4,6,8)\) all are trapezoidal fuzzy numbers. Using the Theorem 2, optimum value of the objective function is Z=7.673 and optimum solution is x _{1}=1.461,x _{2}=0.
Case 2: Let \(\tilde {a_{1}}=(2,4,7,8), \tilde {a_{2}}=(2,3,4,6), \tilde {a_{3}}=(1.5,2.5,3.5,5.5), \tilde {a_{4}}=(1,2.5,4,4.5), \tilde {a_{5}}=(3,4,5,7),\, \tilde {a_{6}}=(1,1.5,2,2.5), \tilde {a_{7}}=(2,3.5,4,6.5)\) and \(\tilde {a_{8}}=(2,4,6,8)\) all are exponential trapezoidal fuzzy numbers. Using the Theorem 1, optimum value of the objective function is Z=8.644 and optimum solution is x _{1}=1.527,x _{2}=0.
Assumption and Notations
A multiitem deterministic inventory model is developed under the following assumptions and notations.
Assumption:
 (i)
The inventory system involve multi items, and rate of replenishment is infinite.
 (ii)
The time horizon of the inventory system is infinite and leadtime is zero.
 (iii)
Shortage are permitted and unsatisfied demand backlogged.
 (iv)
The capacity of rented warehouse (Y _{1} Y _{2}) is unlimited. But, the owned warehouse (OY _{1}) has a limited capacity of w _{ i } units.
 (v)
The deterioration cost of the ith items per unit time in OY _{1} is getter than in Y _{1} Y _{2}.
 (vi)
The goods of OY _{1} are wasted only after wasting the goods kept in Y _{1} Y _{2}.
Notations:
 (i)
n = number of items
 (ii)
D _{ i }(q _{ ji }(t)) = demand rate per unit time t for ith item (j=1,2,3)
where$$D_{i}(q_{ji}(t))=\left\{ \begin{array}{ll} a_{i}+b_{i}q_{ji}(t) & \quad \text{if } q_{ji}(t) > 0;\\ a_{i} & \quad \text{if } q_{ji} \leq 0; \end{array}\right. $$  (iii)
\(\tilde {A_{i}}\) = replenishment cost per order for ith item
 (iv)
M _{ i } = maximum inventory level per cycle of the ith item
 (v)
w _{ i } = capacity of the owned warehouse for ith item
 (vi)
Q _{ i } = ordering quantity per cycle for ith item
 (vii)
\(\tilde {c_{pi}}\) = purchasing cost per unit of the ith item
 (viii)
\(\tilde {c_{1i}}\) = holding cost per unit item per unit time of the ith item in OY _{1}
 (ix)
\(\tilde {c_{2i}}\) = holding cost per unit item per unit time of the ith item in Y _{1} Y _{2}
 (x)
\(\tilde {c_{3i}}\) = shortage cost per unit item per unit time for ith item
 (xi)
\(\tilde {s_{i}}\) = selling price per unit of the ith item
 (xii)
\(\tilde {R_{i}}\) = opportunity cost per unit for ith item
 (xiii)
t _{1i } = time at which the inventory level reaches zero in Y _{1} Y _{2}
 (xiv)
t _{2i } = time at which the inventory reaches zero in OY _{1}
 (xv)
t _{3i } = length of period during which shortages occurred
 (xvi)
T _{ i } = length of the inventory per cycle of the ith item, here T _{ i }=t _{2i }+t _{3i }
 (xvii)
ε _{1i } = deterioration rate of the ith item in Y _{1} Y _{2}, where 0≤ε _{1i }<1
 (xviii)
ε _{2i } = deterioration rate of the ith item in OY _{1}, where 0≤ε _{2i }<1
 (xix)
q _{1i }(t) = the inventory level of the ith item in Y _{1} Y _{2} at time t
 (xx)
q _{2i }(t) = the inventory level of the ith item in OY _{1} at time t
 (xxi)
q _{3i }(t) = the inventory level of the ith item at time period [ t _{2i },T _{ i }], where shortage occurred
 (xxii)
\(\tilde {B}\) = available budget for replenishment
 (xxiii)
\(\tilde {F}\) = available shortage space in this inventory system
 (xxiv)
TP(t _{2},t _{3}) = the total average profit per unit time in the twowarehouse case
Mathematical Formulation
CaseI: When shortages do not occur;
with boundary condition q _{1i }(t _{1i })=0.
with boundary conditions q _{2i }(t _{2i })=0.
Then \(\frac {dt_{2i}}{dt_{1i}}1<0\) holds.
CaseII: When shortage occurs;
with the boundary conditions q _{3i }(t _{2i })=0
where t _{3i }=T _{ i }−t _{2i }.
Now we calculate the different types of cost for ith item (i=1,2,...,n), which are based on previous equations.
The ordering cost in each cycle for ith item is A _{ i }
where t _{2}=(t _{21},t _{22},...,t _{2n })^{ T } and t _{3}=(t _{31},t _{32},...,t _{3n })^{ T }.
where t _{2}=(t _{21},t _{22},.....,t _{2n })^{ T } and t _{3}=(t _{31},t _{32},.....,t _{3n })^{ T } are decisions variables and \(Q_{i}=\frac {a_{i}}{\varepsilon _{1i}+b_{i}}\left [e^{(\varepsilon _{1i}+b_{i})t_{1i}}1\right ]+w_{i}+a_{i}\delta _{i}t_{3i}\).
Solution Methodology
where t _{2}=(t _{21},t _{22},.....,t _{2n })^{ T } and t _{3}=(t _{31},t _{32},.....,t _{3n })^{ T } are decision variables.
Numerical Illustration
To illustrate the proposed multiitem twowarehouse inventory model, we have considered an inventory problem with purchasing cost, shortage cost, available shortage space, selling price, capacity of owned warehouse, and holding cost. In most inventory problems in real life, we observed that the different costs and other parameters are normally vague and imprecise in nature. For example, the holding cost of an item is supposed to be dependent on the amount of storage. Similarly, the total shortage cost depends upon the amount of stock in a scheduling period, etc. So, in this inventory system, the total average profit in a scheduling period may be uncertain, and uncertainties may be associated with these variables, and the above goals and parameters are normally vague and increase. This uncertain and vague nature of a parameter can be capture by linear or nonlinear fuzzy numbers.
Input fuzzy parameters
ItemI  ItemII  

\(\tilde {c_{pi}}\)  (6.250, 9.560, 12.164, 13.450)  (6.123, 8.263, 11.560, 12.900)  
\(\tilde {c_{1i}}\)  (1.234, 1.370, 2.680, 3.250)  (1.234, 1.388, 2.750, 3.350)  
\(\tilde {c_{2i}}\)  (2.450, 2.569, 2.700, 3.450)  (1.234, 1.324, 2.950, 3.100)  
\(\tilde {c_{3i}}\)  (1.234, 1.486, 2.900, 3.200)  (1.150, 1.638, 2.950, 3.400)  
\(\tilde {s_{i}}\)  (10.250, 13.350, 14.500, 16.250)  (9.850, 14.609, 15.210, 17.230)  
\(\tilde {R_{i}}\)  (5.263, 6.957, 8.985, 10.452)  (4.563, 6.513, 7.925, 9.561)  
\(\tilde {A_{i}}\)  (95.520, 107.192, 108.500, 110.250)  (94.200, 101.654, 102.213, 105.120)  
\(\tilde {B}\)  (9100, 9450, 9550, 9700)  \(\tilde {F}\)  (1800, 1950, 2050, 2150) 
Input crisp parameters
a _{ i }  b _{ i }  ε _{1i }  ε _{2i }  δ _{ i }  w _{ i }  

ItemI  300  0.051  0.055  0.068  1.650  307 
ItemII  315  0.064  0.045  0.063  1.550  315 
Optimal solution for different values of ε _{1i } & ε _{2i }
ε _{1i }  ε _{2i }  t _{1i }  t _{2i }  t _{3i }  T _{ i }  Q _{ i }  Z ^{∗}  

ItemI  0.55×10^{−1}  0.68×10^{−1}  0.11013×10^{−6}  2.22391  0.24954  2.47345  430.5268  6194.915 
ItemII  0.45×10^{−1}  0.63×10^{−1}  0.47338×10^{−7}  2.16807  0.26176  2.42983  442.8061  
ItemI  0.64×10^{−1}  0.74×10^{−1}  0.74232×10^{−7}  2.21691  0.25374  2.47065  432.6060  6187.219 
ItemII  0.49×10^{−1}  0.69×10^{−1}  00000.00000  2.16191  0.26484  2.42675  444.3113  
ItemI  0.66×10^{−1}  0.80×10^{−1}  0.38212×10^{−8}  2.20997  0.25793  2.46790  434.6750  6179.556 
ItemII  0.54×10^{−1}  0.75×10^{−1}  00000.00000  2.15580  0.26791  2.42371  445.8107  
ItemI  0.71×10^{−1}  0.88×10^{−1}  00000.00000  2.20309  0.26209  2.46518  436.7364  6171.924 
ItemII  0.58×10^{−1}  0.81×10^{−1}  0.51075×10^{−7}  2.14975  0.27097  2.42072  447.3045  
ItemI  0.77×10^{−1}  0.95×10^{−1}  00000.00000  2.19626  0.26623  2.46249  438.7878  6164.325 
ItemII  0.63×10^{−1}  0.88×10^{−1}  0.28856×10^{−8}  2.14371  0.27402  2.41773  448.7924  
ItemI  ε _{1i }→0  ε _{2i }→0  00000.00000  2.29729  0.20647  2.50376  409.2066  6273.692 
ItemII  00000.00000  2.23230  0.23026  2.46256  427.4288 
Using the parameters given in Tables 1 and 2, problem (26) have been solved by using a soft computing technique, generalized reduced gradient (GRG) method. We have also considered problem (26) for nondeteriorating items and solve it using the parameters of Tables 1 and 2 and the results are given in Table 3.
Sensitivity Analysis
 (i)
If the value of deterioration rate ε _{ ki }(k=1,2) increases, then the total average profit Z ^{∗} decreases (cf. Figs. 9 and 10).
 (ii)
Table 3 shows that length of the inventory per cycle T _{ i } decreases with increase of ε _{1i }(cf. Fig. 10)
 (iii)
Again Table 3 shows that the order quantity per cycle Q _{ i } increases when the deterioration rate increases (cf. Fig. 12).
Conclusion
For the first time, possibility, necessity, and credibility measures of exponential trapezoidal fuzzy numbers and its expected value are presented here. In Example 1, we have shown the advantage of the expected value operator technique for exponential trapezoidal fuzzy numbers. The multiitem deterministic inventory model is developed for deteriorating items with stockdependent demand, permitting shortage and finite warehouse capacity. In this model we have considered different deterioration costs and holding costs for OY _{1} and Y _{1} Y _{2} because of different conservation conditions. Finally, this model is also formulated under exponential trapezoidal fuzzy environment and solved using a soft computing technique, generalized reduced gradient method. The model is also discussed for nondeteriorating items as a special case of the deteriorating items and results are given in Table 3. To show the validity of the proposed model, few sensitivity analyses with respect to the different rates of deterioration have been carried out. The proposed method can also be applied for multiobjective, multiconstraint, and multisupplier inventory problem with nonlinear demand, which may be areas of future work.
Declarations
Competing Interests
The authors declare that they have no competing interests.
Authors’ contributions
All authors contributed to the research equally. All authors read and approved the final manuscript.
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