AN OPTIMIZING INTEGRATED INVENTORY MODEL WITH INVESTMENT FOR QUALITY IMPROVEMENT AND SETUP COST REDUCTION

International Journal of Information Technology, Control and Automation (IJITCA) Vol. 6, No.3/4, October 2016
DOI:10.5121/ijitca.2016.6401 1
AN OPTIMIZING INTEGRATED INVENTORY MODEL
WITH INVESTMENT FOR QUALITY IMPROVEMENT AND
SETUP COST REDUCTION
M. Vijayashree1*
, R. Uthayakumar2
1*
Full-Time Research Scholar, Department of Mathematics, The Gandhigram Rural
Institute – Deemed University, Gandhigram- 624 302, Tamilnadu, India.
2
Professor & Head, Department of Mathematics, The Gandhigram Rural Institute –
Deemed University, Gandhigram- 624302, Tamilnadu, India.
Abstract
This paper presents a vendor-buyer integrated inventory model. This paper considers the problem of a
vendor and buyer integrated production inventory model for the vendor and the buyer optimization model
under quality improvement investment and setup cost reduction in the production system such that the total
profit is maximized. The relationship between demand and price is considered as a linear. Entirety profit is
the supply chain presentation calculate and it is calculated as the dissimilarity among revenue from sales
and total cost, where the last is the sum of the vendor’s and buyer’s setup/order and inventory holding
costs, opportunity in setup cost and opportunity investment cost. This manuscript efforts to conclude the
optimal production run time and capital investments in setup cost reduction and process quality
improvement for production system such that the total profit is maximized. The main focus for this paper is
the setup cost reduction and investment for quality improvement. The proposed model is based on the
integrated total profit for both buyer and vendor which find out the optimal value of order quantity,
opportunity investment cost for quality improvement and setup cost reduction. The solution procedure is
developed in order to find the total profit of the vendor and the buyer which is to be maximized. To
conclude, a numerical example is given to demonstrate the solution procedure.
Keywords
Integrated inventory model, Price-sensitive demand, Investment for quality improvement, Setup cost
reduction.
Subject classification code: 90B05
*Corresponding author Tel.
+91-451-2452371, Fax: 91-451-2453071

2
1. INTRODUCTION
Most of the inventory model, researchers considered only the independent viewpoint. However,
in a supply chain environment, the coordination of all the partners is the key to efficient
management of a supply chain to achieve global optimality. Study on coordinating supply chains
is presently very popular. During the last few years, the concept of integrated vendor and the
buyer inventory management has attracted considerable attention, accompanying the growth of
Supply Chain Management (SCM). Recognizing the strategic the vendor and the buyer
partnerships, as a fundamental driver for the success of the supply chain, increasing attention has
been placed on the integrated vendor-buyer inventory models.
Inventory management is a science mainly about specifying the shape and assignment of stocked
goods. It is required at dissimilar locations within a facility or within lots of locations of a supply
network to precede the standard and intended course of manufacture and stock of materials.
Inventory management plays a significant role in businesses because it can help companies reach
the goal of ensuring prompt release, avoiding shortages, helping sales at spirited prices and so
forth. To control an inventory system, one cannot ignore demand monitoring since inventory is
partially driven by demand, and as suggested by Lau et al. [14] in many cases a small change in
the demand pattern may result in a large change in optimal inventory decisions. A manager of a
company has to investigate the factors that influence the demand pattern, because the customers’
purchasing behavior may be affected by factors such as the selling price, inventory level,
seasonality, and so on.
A supply Chain (SC) is a system among a corporation and its suppliers to create and hand out a
specific product, and the supply chain stand for the steps it takes to get the manufactured
merchandise or tune-up to the customer. Supply chain management (SCM) is a crucial process,
because an optimized supply chain results in lower costs and a faster production cycle.
Supply chain management (SCM) is the active streamlining of a business' supply-side activities to
capitalize on client value and gain a competitive advantage in the marketplace. SCM represents
an attempt by suppliers to develop and employ supply chains that are as well-organized and
economical as possible. Supply chains cover everything from production, to product
development, to the in sequence systems needed to direct these undertakings. The three main
flows of the supply chain are the produce run, the information run and the finances run. SCM
involves coordinating and integrating these flows both within and among companies.
The effectiveness of coordination in supply chains could be measured in two ways: reduction in
total supply chain costs and enhanced coordination services provided to the end customer and to
all players in the supply chain. The integrated vendor-buyer problem is called the Joint Economic
Lot Sizing (JELS) problem and can be considered as the building block for wider supply chain
systems. The global supply chain can be very complex and link-by-link understanding of joint
policies can be very useful.
The paper assumes a single product that flows along a two-level supply chain (vendor–buyer).
We assumed that the buyer faces a linear demand, ( ) δα baD −= ,( )0>> ba as a function of

3
his/her unit retail price, which increases as the price decreases. furthermore , we utilize a mark-
up policy where selling price is set based on the unit purchasing prices c, plus a stable percentage
markup , i.e. linear demand, which is unspecified to be sensitive to price and mark-up policy.
This paper considers the problem of a vendor-buyer integrated production-inventory model. We
have developed an optimizing integrated inventory model with investment for quality
improvement and setup cost reduction. The model proposed, based on the integrated total relevant
profits of both buyer and vendor, finds out the optimal values of order quantity, investment for
quality improvement, setup cost reduction, using an analytical approach. Finally, a numerical
example will be provided to illustrate the proposed model. By the logarithm investment function,
the optimal investment quality improvement and setup cost reduction investment also are
obtained.
2.LITERATURE REVIEW
The extraordinary interest in supply chain management related investigate in the last decade has
been due to its important possible to improve the efficiency of operations and decrease of cost.
Each human being party in the supply chain can benefit from side to side closer collaboration
with other parties and through the integration of various decision processes. The single-vendor
and the single-buyer problem are considered as the building block of any supply chain. Many
load policies have been proposed in literature for this problem. Goyal [4] suggested a lot-for-lot
policy with the assumption of unlimited production rate. Banerjee [1] planned a lot-for-lot policy
with the assumption of finite production rate. Goyal [5] relaxed lot-for-lot assumption and
assumed that the vendor ships the lot in a number of equal size shipments. Goyal [6] developed a
policy where the shipment sizes increase by a factor increasing geometrically. Hill [8] generalized
the model developed by Goyal [6] by considering the geometric growth factor as a decision
variable. Hill [9] found the optimal solution of the problem without any assumptions about the
shipment policy. Goyal et al. [7] Considered a policy where the first shipment is small and the
following shipments are larger and of equal size. For comprehensive reviews to Goyal et al. [3]
and Ben-Daya et al. [2] for comprehensive reviews.
The systematic draw near to reduction or removal of waste, revise, and losses in production
process. Quality management is the act of overseeing all activities and tasks needed to maintain a
desired level of excellence. This includes the determination of a quality policy, creating and
implementing quality planning and assurance, and quality control and quality improvement. It is
also referred to as total quality management (TQM). Quality has been extremely emphasized in
modern production/inventory management systems. Also, it has been supported that the success
of Just-In-Tim (JIT) production is partly based on the belief that quality is a controllable factor,
which can be improved through various efforts such as worker preparation and dedicated tackle
acquisition. In the classical economic order quantity (EOQ) model, the quality-related issue is
often neglected; it implicitly assumes that quality is fixed at an optimal level (i.e., all items are
assumed with perfect quality) and not controllable. However, this may not be true. In real
production surroundings, we can often observe that there are defective items being produced.
These defective items must be discarded, fixed, revised, or, if they have reached the customer,
refunded; and in all cases, substantial costs are incurred.

4
Porteus [21] and Rosenblatt et al. [22] are the first to openly complicated on the important
association among quality imperfection and lot size. specially, Porteus [21] extended the EOQ
model to include a situation where the production process is imperfect, and based on this model
he further considered the effects of investment in quality improvement by introducing the
additional investing options. Since Porteus, several authors proposed the quality improvement
models under various settings, see e.g. Keller et al. [13], Hwang et al. [12], Moon [16], Hong et
al. [10] and Ouyang et al. [18]. We note that in the body of literature [10, 12, 13, 14, 15, 16], a
common approach utilized to develop the total cost of quality improvement model is adding the
investment cost necessary for quality improvement to the system operating costs, where the
investment amount is further charged a fixed opportunity cost instead of modeling the system
with discounted costs. However, in practice, the opportunity cost rate (e.g., interest rate) may not
be fixed; it may slightly change from time to time, particularly, in an unstable environment.
We think that the association connecting setup cost reduction (or process quality improvement)
and capital investment can be described by the logarithmic investment function. This logarithmic
investment function which has been used in earlier researchers by Paknejad et al. [20], Nasri et al.
[15], Sarker et al. [21], and Hofmann [11], In addition, a procedure is provided to find the optimal
production runs. Ouyang et al. [17] discussed a lot size, reorder point inventory model with
controllable lead time and setup cost. Porteus [21] proposed an inventory model with optimal lot
sizing, process quality improvement and setup cost reduction. Ouyang et al. [19] talk about
quality improvement setup cost and lead time reduction in lot size reorder point models with an
imperfect production process.
Setup costs are bringing upon you when production or assembly lines are changed for example,
when the manufacturing department has to change equipment for a different product or part to be
manufactured. It is easier for businesses to understand and be pleased about the costs involved in
manufacture setups than the costs of ordering objects from a vendor. Manufacturing companies
are often too aware of the costs of changing the manufacturing line from creating one item for
creating another. There have often been much conversation and analysis of the best way to
minimize the occasion and price of changing production on the shop floor. But with setup costs,
there are still two component costs; fixed and variable.
In a manufacture setup the fixed costs will include the costs of the capital equipment used in
tearing down the production line used for the old items and setting up machine for the new items.
The variable costs in production setup include the personnel costs in changing overproduction, as
well as the consumable material used in the tear down and setup. The longer the production tears
down and setup takes, the higher the variable costs. Vijayashree and Uthayakumar [29] have
developed an integrated vendor and buyer inventory model with investment for quality
improvement and setup cost reduction. Vijayashree and Uthayakumar [27] developed an
integrated inventory model with controllable lead time and setup cost reduction for defective and
non-defective items.
In the context of Economic Order quantity EOQ model, Porteus [21] primary considered a
situation where the production process can go 'out-of-control' with a given probability θ each
time it produces another item. Once the process is 'out-of-control', it remains that until the
remainder of the lot has been produced. Rosenblatt et al. [22] analyzes the case when the system

5
deteriorates during the production process and produces some proportion of defective items.
Yang and Pan [31] considered variable lead time and quantity improvement investment with
normal distributional insist in the model. In addition, Rosenblatt et al. [22] assume that the
elapsed time until the production process shift is a random variable and is exponentially
distributed, and derive an approximated optimal manufacture run time in their models, showed
that the resulting production lot size should be smaller than that of the classical EPQ formula, and
thus there would be an incentive to produce smaller lots. Moreover, Porteus [21] initiated the
notion of a joint investment in process quality improvement and setup cost reduction in the EOQ
model. Porteus [21], Hong et al. [10] considered the economic benefits of reducing setup cost and
improving process quality by joint capital investment under a budget constraint.
Vijayashree and Uthayakumar [28] have considered integrated inventory model with controllable
lead time involving investment for quality improvement in supply chain system. Vijayashree and
Uthayakumar [24] have presented inventory models involving lead time crashing cost as an
exponential function. Vijayashree and Uthayakumar [26] have presented a two stage supply chain
model with selling price dependent demand and investment for quality improvement. Vijayashree
and Uthayakumar [25] have discussed vendor-buyer integrated inventory model with quality
improvement and negative exponential lead time crashing cost. Vijayashree and Uthayakumar
[30] have developed two-echelon supply chain inventory model with controllable lead time.
To best our knowledge, we develop an integrated inventory model system consisting of a vendor
and buyer under investment for quality improvement and setup cost reduction. The objective of
this paper is to maximize the total profit for the vendor and buyer. We analyze how the
coordination between two stage supply chain models is affected when the customer demand is
price sensitive.
The paper is organized as follows: In section 3, notations are discussed. In section 4, assumptions
are given. Section 5 is discussed with model development for the buyer and the vendor integrated
model and investment for quality improvement and setup cost reduction. In section 6, solution
procedure is presented. In section 7, an algorithm procedure is developed to find the optimal
solution to the integrated inventory model. In section 8, a numerical example is offered. Finally,
we draw conclusions and further researches are summarized in Section 9.
3. NOTATIONS
To establish the mathematical model, the following notations are used as follows
D Demand rate as a function of unit selling price
P Production rate of the vendor
Q Optimal order quantity (Decision Variable)
vA Vendor’s setup cost per setup (Decision variable).
0vA Original ordering cost (before any investment is made)
bA Buyer’s ordering cost
δ The buyer unit selling price

6
c The buyer unit purchasing price
α Mark-up percentage
vh Inventory holding cost for the vendor per year
bh Inventory holding cost for the buyer per year
g Cost incurred by producing a defective item (for rework and related operations)
0θ Original percentage of defective products produced once the system is in the out of
control state prior to investment
θ Percentage of defective products produced once the system is in the out of control state
(Decision Variable)
)(θq The investment required to reduce the out-of-control probability θ to 0θ
)( vAq Capital investment required to achieve setup cost vA to 0vA
i The fractional per unit time opportunity cost of capital (the opportunity cost rate)
ε Percentage decrease in θ per dollar increase investment in )(θq and )( vAq .
ITP Integrated total profit for the vendor and the buyer
4. ASSUMPTIONS
To establish the mathematical model, the following the assumptions of the model are summarized
as follows
1. The integrated system of single–vendor and single-buyer for a single product is considered.
2. The buyer faces a linear demand as a function of the selling price ( ) δα baD −= ,
)0( >> ba .
3. Selling price is set based on the unit purchasing price plus a constant percentage mark-up,
c)1( αδ +=
4. The inventory is continuously reviewed and replenished.
5. Shortage is not allowed.
6. A finite production rate for the vendor is considered, which is greater than the demand rate.
7. The logarithm investment cost function employed to describe the relationship between
)(θq and θ that is 





=
θ
θ
θ 0
ln1)( qq )0( 0θθ ≤< where 0θ is the original out-of-control
probability; 





=
ε
1
1q withε denoting the percentage decrease in θ per dollar increase
in )(θq .

7
8. We assume that the capital investment )( vAq , in reducing setup cost is a logarithm
function of the setup cost ,vA that is for 





=
v
vo
v
A
A
qAq ln)( 2 , for 00 vv AA ≤< , where






=
ε
1
2q withε denoting the percentage decrease in vA per dollar increase in )( vAq .
9. The inventory holding cost at the buyer is higher than that at the vendor, i.e. vb hh > .
10. All defective items produced are detected after the production cycle is over, and rework
cost for defective items will be incurred.
11. Defective item revise cost per unit time, the anticipated number of defective items in a run
of size Q with a given probability of θ that the procedure can go out of control is
2
2
θQ
.
Thus, the defective cost per unit time is given
2
θSDQ
.
12. Investment cost required for quality improvement 





θ
θ0
1 lniq .
13. Opportunity cost of setup cost reduction = 





v
v
A
A
iq 0
2 ln .
5. MODEL DEVELOPMENT
The optimal order quantity and profit margin of the incorporated system is derived in this section.
We first obtain the optimal policies if each supply chain member tries to maximize its benefit.
Then, the policies and profits are compared with the case of an integrated system when they
cooperate with each other.
We assume that the buyer faces a linear demand, ( ) δδ baD −= (a > b > 0), as a function of
his/her unit retail price, which increases as the price decreases. Moreover, we employ a mark-up
pricing policy where the selling price is set based on the unit purchasing price, c, plus a constant
percentage mark-up, i.e. ( )cαδ += 1 . Since ( ) bcbcaD αα −−= the maximum percentage
mark-up is 1/ −bca .
The total profit for the vendor and the buyer is equal to the gross revenue minus the sum of the
purchasing cost, the order processing cost, and inventory holding cost, defective cost and
opportunity investment cost for quality improvement and investment for setup cost.
Consequently the total profit is going to be maximized, i.e.

8
( ) ( )
( )
Q
AAbcbca
bcbcacAQITPmizem b
AQ
)(
1),,,(axi v
v
),,,( v
+−−
−−−+=
α
ααθα
θα
( ) ( )[ ]Pbcbcahbcbcagh
Q
/
2
vb ααθ −−+−−+−






−





−
v
v
A
A
iqiq 0
2
0
1 logln
θ
θ
(1)
for oθθ ≤<0 and 0
0 vv AA ≤< , where i is the unfinished opportunity cost of capital per unit
time.
6. SOLUTION PROCEDURE
It can effortlessly be shown that the total organization profit is concave in Q for the known
values of the buyer percentage mark-upα .
Taking the first order partial derivatives of ),,,( vAmQTC θ with respect toQ ,θ and vA
equating them to zero, we get
( )
( )
( )





 −−
+−−+−
+−−
=
∂
∂
P
bcbcah
bcbcagh
Q
AAbcbca
Q
ITP α
αθ
α v
b2
vb
2
1)(
(2)
θ
α
θ
1
2
)( iqbcbcaQSITP
+
−−
−=
∂
∂
(2)
vA
iq
Q
bcbca
A
ITP 2
v
)(
+
−−
−=
∂
∂ α
(3)
( )





 −−
+−−+
+−−
=
P
bcbcah
bcbcagh
AAbcbca
Q
)(
)(
)(2*
v
b
vb
α
αθ
α
(4)
)(
2* 1
bcbcaQS
iq
α
θ
−−
= (5)
)(
* 2
bcbca
Qiq
Av
α−−
= (6)

9
Substituting expression (4), (5) and (6) into the cost function (1), we get
Further, based on the convexity and concavity behavior of the objective function with respect to
the decision variable, the following algorithm is designed to find the optimal values of order
quantity Q , process quality θ and setup cost vA which maximize the integrate total profit
),,( vAQITP θ .Therefore we establish the following iterative algorithm to obtain the optimal
solution.
7. ALGORITHM
Step 1. Begin with 0θθ = and 0vv AA = .
(i).Substituteθ and vA into equation (4) estimateQ .
(ii).Utilizing Q find out θ and vA from equations (5) and (6).
(iii).Repeat step (i) - (ii) until no modify occurs in the values of, θ,Q , and vA . Denote the
solution by ),,( '''
vAQ θ .
Step 2. Compare ,&and,& 0
'
0
'
vv AAθθ correspondingly.
If ,and 0
'
0
'
vv AA <<θθ then the key found in step 1 is optimal solution.
` We indicate the optimal solution by ).,,(
***
vAQ θ If ).,,(
***
vAQ θ = ),,( '''
vAQ θ , then
go to step (7), otherwise go to step (3).
Step 3. If 0
*
0
*
and vv AA ≥<θθ go to step 4. If 0
*
0
*
and vv AA <≥θθ go to step 5. If 0
*
θθ ≥
,and 0
*
vv AA ≥ then go to step 6.
Step 4. Let vv AA =
*
and exploit equations (4) and (5) to find out the new ),( ''
θQ by a
procedure similar to the one in Step 1, the result is denoted by ).,(
..
θQ If ,0
.
θθ < then the
optimal solution is obtained, i.e., if ).,,(
***
vAQ θ = ).,,( 0
..
vAQ θ then go to step (7),
otherwise go to step (iv).
(i) let 0
*
θθ = and exploit equation (4) to find out the new '
Q , then go to step (7).

10
Step 5. Let 0
*
θθ = and utilize equations (4) and (6) to find out the new ),( ''
vAQ by a method
similar to the one in Step 1, the result is denoted by ).,(
..
vAQ If ,0
.
vv AA < then the optimal
solution is obtained, i.e., if ).,,(
***
vAQ θ = ).,,(
.
**
vAQ θ then go to step (7), otherwise go
to step (v).
(ii) 0
*
vv AA = And utilize equation (4) to find out the new '
Q , then go to step (7).
Step 6. Let 0
*
vv AA = and 0
*
θθ = , and utilize equation (4) to find out the new '
Q , then go to
step (7).
Step 7. Utilize equation (1) to calculate the corresponding integrated to compute the integrated
total profit ),,( vAQITP θ . Then go to step 8.
Step 8. Set ),,(),,(
******
vv AQAQ θθ = , then ),,(
***
vAQ θ is the optimal solutions.
8. NUMERICAL EXAMPLE
Consider an inventory system with the following characteristics
year/3200=P unit,/400$=vA unit,/25$=bA unit,/5$=bh unit,/4$=vh
unitS /15=
year,/2.0$=i 0002.0=θ .ln400)( 0






=
θ
θ
θq unit,/50.0$=α .ln1500)( 0






=
v
v
v
A
A
Aq Besi
de we take, ,1500=a ,10=b unit,/60$=c applying the solution procedure we have optimal
order quantity units284* =Q , 70$
*
=vA , 000012790.0* =θ , Total profit .52224$=TP
9. CONCLUSION
The main purpose of this paper is to present the vendor and the buyer optimizing integrated
inventory model with investment for quality improvement and effectively rising investment to
reduce the setup cost. We developed an integrated inventory model for integrated optimization.
The buyer faces a linear demand as a function of the selling price. Selling price is set based on the
unit purchasing price plus a constant percentage mark-up. The paper assumes a single product
that flows along a two-level supply chain (vendor–buyer). The buyer faces a linear demand,
which is assumed to be sensitive to price and mark-up percentage performance.
In this paper, we have used the logarithmic function to obtain the investment for quality
improvement and setup cost reduction. In the analysis, we assumed that the setup cost and
process quality are functions of capital expenditure, respectively. In addition, we offer a method

11
to find the optimal production run time, setup cost and process quality improvement level. By the
logarithm investment function, the optimal quality improvement and setup cost reduction
investment also are obtained. In the present work we have developed an integrated production,
inventory model in which the objective is to maximize the total profit of the buyer and the vendor
by optimizing the optimal order quantity, setup cost reduction and investment for quality
improvement. A mathematical model is developed and solved to determine the optimal solution.
Developing the model to the multi-supplier case is also proposed for the future research.
ACKNOWLEDGEMENT
The authors would like to thank the editors and anonymous reviewers for their valuable and
helpful comments, which led to a major improvement in the revised manuscript. The first author
research work is supported by DST INSPIRE Fellowship, Ministry of Science and Technology,
Government of India under the grant no. DST/INSPIRE Fellowship/2011/413A dated 22.12.2015
and UGC–SAP, Department of Mathematics, The Gandhigram Rural Institute – Deemed
University, Gandhigram – 624302, Tamilnadu, India.
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[18] Ouyang, L.Y., Chuang, H.C. (2002). “Lot size reorder point inventory model with controllable lead
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BIOGRAPHICAL NOTES
M. Vijayashree is a full time research scholar in the Department of
Mathematics at The Gandhigram Rural Institute – Deemed University,
Gandhigram, India. She received her B.Sc in Mathematics from M.V.M Govt
Women’s Arts College, Dindigul,Tamilnadu, India in 2007 and M.Sc. Tech
(Industrial Mathematics with Computer Applications)from The Gandhigram
Rural Institute-Deemed University, Gandhigrm, Dindigul, Tamilnadu,India in
2010. She has received university gold medalist in M.Sc. Currently, she is a
Senior Research Fellow (SRF) under DST INSPIRE, New Delhi in the
Department of Mathematics, The Gandhigram Rural Institute – Deemed
University, Gandhigram, India. She has published about 8 papers in International
journals. Her research interests include the following fields: Operations research,
Inventory Management and Control, Supply Chain Management.
Dr. R. Uthayakumar was born in Dindigul, Tamilnadu, India, in 1967. He
received his B.Sc. degree in Mathematics from G.T.N. Arts College, Dindigul in
1987, M.Sc. degree in Mathematics from American College,Madurai in 1989,
M.Phil., degree in Mathematics from Madurai Kamaraj University, Madurai in
1991, B.Ed. degree in Mathematics from Madurai Kamaraj University,Madurai
in 1992 and Ph.D. degree in Mathematics from The Gandhigram Rural Institute
– Deemed University, Gandhigram, India in 2000. Currently, he is professor &
Head of the Department of Mathematics at The Gandhigram Rural Institute –
Deemed University,Gandhigram, India. He has published about 157 papers in
International and National journals. He is working as the Principal Investigator
of the Major Research Project. One Research Projects Completed and Four Research Projects are ongoing.
He is working as the Deputy Coordinator of Department Level Research Project. He is member of

13
Scientific Bodies (Life member in the Ramanujan Mathematical Society), Board of studies, Board of
Examiners and Question Paper Setters, Selection Committee and Inspection Committee. His research
interests include thefollowing fields: Mathematical Modeling, Fractal Analysis, Operations Research,
Inventory Management & Control and Supply Chain Management.

AN OPTIMIZING INTEGRATED INVENTORY MODEL WITH INVESTMENT FOR QUALITY IMPROVEMENT AND SETUP COST REDUCTION

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AN OPTIMIZING INTEGRATED INVENTORY MODEL WITH INVESTMENT FOR QUALITY IMPROVEMENT AND SETUP COST REDUCTION