![Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017
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6. CONCLUSION
This paper deals with an EPQ model for deteriorating/imperfect quality items with linearly
decreasing demand and demand dependent production setup. In this model, imperfect quality
items are allowed and reworked to maintain as good quality and goodwill of the customers. This
model helps to management to determine number of production setup and optimum production
time by minimizing the total inventory cost. Sensitivity analysis shows that the number of
production setup decrease when holding cost of perfect quality items decreases and holding cost
of imperfect quality items increases and also it shows that both the number of production setups
and optimum production time are highly sensitive with deteriorating items. Hence the
management has to take vital decisions while maintaining inventory with deteriorating items.
A possible extension for further research may consider multi-production setups with
partial/complete backordering where holding cost and deterioration rate are time dependent. This
approach can also be extended to other problem by considered delay payment, inflation, unit cash
discount, stock-dependent demand and single-vendor single-buyer problem.
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items with multiple production setups and rework. International Journal of Production Economics,
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NOTES ON CONTRIBUTORS
Mr T.Sekar is a Part-Time research scholar, Department of Mathematics,
Gandhigram Rural Institute –Deemed University, Gandhigram – 624302, Tamil Nadu,
India. He received his B. Sc degree from Govt. arts college karur, Bharathidasan
University, M. Sc and M. Phil degree from National College, Trichy, Bharathidasan
University, Tamil Nadu, India. Currently, he is working in EBET Group of Institutions,
Tirupur, Tamil Nadu, India. His research interests are in the field of inventory management
and control, optimization techniques and their applications.
Dr R. Uthayakumar was born in Dindigul, India, in 1967. He received the PhD degree
from the Gandhigram Rural Institute –Deemed University, Gandhigram, Tamil Nadu,
India, in 2000. Currently, he is a Professor & Head, Department of Mathematics,
Gandhigram Rural Institute –Deemed University, Gandhigram. He has done research in
the area of functional analysis. His current research interests are primarily in the field of
optimization and the Theory of Fractal Analysis. He is also interested in finding out the
general mathematical model in the field of inventory management and supply chain. To his credit, he has
published many research articles in the area of inventory management and supply chain in reputed
international journals. Moreover, he is guiding many scholars in the above said areas.](https://image.slidesharecdn.com/4117oraj01-170307114152/85/A-MULTIPLE-PRODUCTION-SETUPS-INVENTORY-MODEL-FOR-IMPERFECT-ITEMS-CONSIDERING-SALVAGE-VALUE-AND-REDUCING-ENVIRONMENTAL-POLLUTION-16-320.jpg)
A MULTIPLE PRODUCTION SETUPS INVENTORY MODEL FOR IMPERFECT ITEMS CONSIDERING SALVAGE VALUE AND REDUCING ENVIRONMENTAL POLLUTION
- 1. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 DOI : 10.5121/oraj.2017.4101 1 A MULTIPLE PRODUCTION SETUPS INVENTORY MODEL FOR IMPERFECT ITEMS CONSIDERING SALVAGE VALUE AND REDUCING ENVIRONMENTAL POLLUTION R. Uthayakumar1 and T. Sekar* 1 Department of Mathematics, The Gandhigram Rural Institute – Deemed University, Gandhigram – 624 302, Dindigul, Tamilnadu, India, *Department of mathematics, EBET Group of institutions, Tirupur – 638 108, Tamil Nadu, India. ABSTRACT This paper presents an imperfect manufacturing system in which production ability can produce items in m production setups and rework the imperfect quality items in one rework setup. Rework is one of the main issues in reverse logistic and green supply chain to reduce production cost and environmental problem. The aim of this research is to minimize the total inventory cost by determining the optimal cycle time and the optimal number of production setups. The convexity of the inventory model is derived by using mathematical software. The result is illustrated with numerical example for the model. The effects of the problem parameters upon the optimal solution are examined numerically. This model can be applied to optimizing the total inventory cost for the business enterprises where production rate and demand rate are time dependent and salvage value is incorporated to the deteriorated items. KEYWORDS Deteriorating items, Rework, Salvage value, Linear time varying demand 1. INTRODUCTION: The Economic Production Quantity (EPQ) model is commonly used by practitioners in the fields of production, inventory control and management to assist them in making decision on production lot size. Many researchers have discussed on the EPQ model for the multi-production setups. But very few of them have discussed on multi-production setups with rework. Rework is common in Semiconductor, Pharmaceutical, Chemical, Food industries, Textile industries, Paper industries, Glass industries, Metal processing industries and Plastic industries. Barketau et al. (2008);
- 2. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 2 Buscher et al. (2007); Chiu et al. (2007). Ca´rdenas-Barro´ n, L.E., (2008) presented rework with single stage of production system. Recently, rework process have attracted considerable attention because of the reduction of the natural resources and the rise of cost of the raw material. Rework process play an important role in eliminating waste and effectively controlling the cost of manufacturing in a production system. Therefore, determining optimal lot size in a system that allows rework is a useful objective to minimize the total inventory cost. Rework process reduces energy use and save more natural resources for the future generations. Therefore, the companies are contributing to sustainable development. In this paper, we consider a multi-production setups and one rework setup. By this EPQ model, it is determined that optimal production setup and optimal production time. Since production process in each production setup is imperfect because of human mistakes, (Drury and Prabhu, (1994)), non-perfect technology or many other factors, the imperfect items are produced during production period. In order to provide good service to customers, after each production setup, inspection is carried out to screen out the imperfect quality items and the deteriorating items. The imperfect quality items found is stocked separately in an inventory until reach the optimal production setup. The rework process starts immediately after determined production setup ends. The perfect quality items produced in each production setup is stored separately in an inventory and sold to customers immediately. Both perfect and imperfect items are considered as deteriorating items because their values go down with time. After determined production setup ends, the imperfect items are sent to rework. When the waiting time of the imperfective quality items exceeds the deterioration time limit, they cannot be recovered and must be disposed. The rework process manufactures all imperfect quality items as perfect quality items. We assume all imperfect items after rework are considered as new. This perfect quality items are sold to customers to satisfy the demand immediately. Production process with rework setup is shown in Fig. 1. The remainder of this paper is organized as follows. In Section 2, we give a literature review. In section 3, assumptions and notations are given. The mathematical formulation for this model is given in section 4. Numerical example and sensitivity analysis are given in Section 5, and conclusions are drawn in Section 6. Figure-1: Production process with rework setup.
- 3. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 3 2. LITERATURE REVIEW: Economic Production Quantity (EPQ) model is one of the prominent research topics in production, inventory control and management. By using EPQ model, optimal quantity of items and optimal production time can be obtained. Classical EPQ model was developed under various assumptions. Since then, researchers have extended the model by relaxing one or more of its assumptions. It was assumed that the items produced are of perfect quality items in the classical model. However, imperfect quality items may be produced in reality. Wee et al., (2007) extended the model by considering random defective rate. Jaber et al. (2008) assumed the percentage defective per lot reduces according to a learning curve. Mukhopadhyay et al. (2015) investigated an economic production quantity model for three types of imperfect items with rework. Vandana et al. (2015) presented an inventory model for non-instantaneous deteriorating items with quadratic demand rate and shortages under trade credit policy. Rezaei et al. (2001) considered a supply chain with multiple products and multiple suppliers. Chung et al. (2009) proposed an inventory model with two warehouses, where one of them was rented. Yassine et al. (2012) considered disaggregating the shipments of imperfect quality items in a single production run and aggregating the shipments of imperfect items over multiple production runs. Kumar et al. (2011) presented Economic Production Lot Size (EPLS) model with the stochastic demand and shortage partial backlogging rate under imperfect quality items, in which stochastic imperfect production was assumed. Singh et al. (2015) presented a mathematical production inventory model for deteriorating items with time dependent demand rate under the effect of the inflation and shortages. Rezaei et al. (2012) discussed an economic production quantity and purchasing price for items with imperfect quality when inspection shifts from buyer to supplier. Vandana et al. (2016) investigated an EOQ model for retailers partial permissible delay in payment linked to order quantity with shortages. Felix. et al. (2015) presented a modified EPQ model with deteriorating production system and deteriorating product where rework process was considered at the end of production setup. Mishra. V.K, et al. (2013) considered an inventory model for deteriorating items with time-dependent demand and time varying holding cost under partial backlogging. Shilpi Pal et al. (2015) proposed a production inventory model for deteriorating item with ramp type demand allowing inflation and shortages under fuzziness, in which, multi- production setup was considered without rework. Chandra et al. (2015) introduced the effect of deterioration on two-warehouse inventory model with imperfect quality items. Vandana et al. (2015) introduced an EPQ inventory model for non-instantaneous deteriorating item under trade credit policy. Mishra (2007) derived some problems on approximations of functions in Banach spaces. Deepmala (2014) proposed a study on fixed point theorems for nonlinear contractions and its applications. Rework process is also one of the important issue in reverse logistics where used products are reworked to reduce total inventory cost, waste and environmental pollution. The earliest research that focused on rework and remanufacturing process was done by Schrady (1967). Since then, researchers on rework have attracted many researchers. Khouja (2000) considered direct rework for Economic Lot Sizing and Delivery Scheduling Problem (ELDSP). Koh et al. (2002) discussed on production inventory models where supplier can fill the demand in two alternatives: either orders new products externally or recovers defective items are reworked in the same cycle; and in the second policy, rework is completed after N cycles. Inderfuth et al. (2005) considered an EPQ model with rework and deteriorating recoverable products. Yoo et al. (2009) developed an EPQ model with imperfect production, imperfect inspection and rework. Widyadana et al. (2012) proposed an EPQ model for deteriorating items with rework which was performed after m
- 4. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 4 production setups. Tai (2013) proposed an EPQ model for deteriorating/imperfect product with rework which was performed after a production setup. Sarkara et al. (2014) assumed rework for single stage production system. Hsu et al. (2014) considered An EPQ model under an imperfect production process with shortages backordered. Singh et al. (2014) proposed an economic production model for time dependent demand with rework and multiple production setups where production is demand dependent. We notice that not many studies considered a model with multi-production setups, imperfect items, rework and salvage value is incorporated to the deteriorated items. In this paper, we intend to providing analytic results to solve the issues said above. 3. ASSUMPTIONS AND NOTATIONS 3.1 ASSUMPTIONS 1. Demand rate of finished products at any time ′′ݐ in (0, T) is D()ݐ and assumed to be linearly decreasing. 2. Production rate is demand dependent i. e, ܲ = ߣ)ݐ(ܦ where ߣ ≥ 1. 3. Rework and deterioration rate are constants. 4. There is a replacement for deteriorated items. 5. Shortages and stock outs are not allowed. 6. The production rate of perfect quality items and rework must be greater than the demand rate. 7. No machine breakdown occurs in the production run and rework period. 8. All demands are satisfied. 9. Inspection cost is negligible when compare with other costs. 10. Setup time for rework process is zero. 11. All the imperfect quality items can be reproduced to good quality. No imperfect quality items occurs during the rework process. 3.2 NOTATIONS D(t) Demand rate (unit/year) P(t) Production rate (unit/year) P୰ Rework process rate (unit/year) θ(t) Deterioration rate (unit/year) α Percentage of good quality items m Number of production setup in one cycle Di Total deteriorating units (unit) Ks Production setup cost ($/setup) Kr Rework setup cost ($/setup) hs Perfect quality items holding cost ($/unit/year) hr Imperfect quality items holding cost ($/unit/year) Dc Deteriorating cost ($/unit) I1 Inventory level of perfect quality items in a production period I2 Inventory level of perfect quality items in a non -production period Ir1 Inventory level of imperfect quality items in a production period Ir2 Inventory level of imperfect quality items in a non - production period
- 5. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 5 Ir3 Inventory level of imperfect quality items in a rework production period It1 Total inventory level of perfect quality items in a production period It2 Total inventory level of perfect quality items in a non - production period It3 Total inventory level of perfect quality items in a rework production period It4 Total inventory level of perfect quality items in a rework non-production Period TTIଵ Total inventory level of imperfect quality items in a production period Iv1 Total inventory level of imperfect quality items in m production periods TTIଶ Total inventory level of imperfect quality items in a non-production period Iv2 Total inventory level of imperfect quality items in m non - production period Iv3 Total inventory level of imperfect quality items in a rework setup production period TRI Total inventory level of imperfect quality items IMr Maximum inventory level of imperfect quality items in production setups IEr Maximum inventory level of imperfect quality items when rework process started T1 Regular production period T2 Non - production period T3 Rework process period T4 Rework non-process period TCT Total cost per unit time C Cost per unit γC Salvage value associated with deteriorated units during a cycle time (0 < ߛ < 1) 4. FORMULATION OF THE MODEL The inventory level of perfect quality items in three production setups is shown in Figure -1. The cycle begins with zero inventory and starts at time t = 0. Production is performed during T1 time period. Since the production quality is not perfect, a percentage (1-α) imperfect items is assumed to occur during the regular production process (T1). The amount of imperfect quality items produced per unit time is (1- α)P. The rework process starts after m-production setups. The rework process is performed in T3 time period. The Inventory level of perfect items in a production period can be formulated as: ௗூభ(௧భ) ௗ௧భ + ߠܫଵ(ݐଵ) = ߙܲ − ݐ(ܦଵ) 0 ≤ ݐଵ ≤ ܶଵ (1) where D()ݐ = ܽ − ܾݐ where ܽ is the intercept and ܾ is the slope of the linearly decreasing demand function.
- 6. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 6 Figure-2: Inventory level of perfect quality items in 3 production setups and 1 rework setup. Figure-3: Inventory level of imperfect quality items in 3 production setup and 1 rework setup. Since I1 (0) = 0,the inventory level in a production period is Iଵ(tଵ) = ఈఒିଵ ఏ ቂݐ(ܦଵ) + − ݁ିఏ௧భ ቀܽ + ఏ ቁቃ 0 ≤ ݐଵ ≤ ܶଵ (2) The total inventory in a production up time can be modeled as I୲ଵ(tଵ) = ߙߣ − 1 ߠ න ݐ(ܦଵ) + ܾ ܽ − ݁ିఏ௧భ ൬ܽ + ܾ ߠ ൰൨ ݀ݐଵ ்భ I୲ଵ = ఈఒିଵ ఏమ ቂߠܽܶଵ − ܾ ቀ ఏ்భ మ ଶ − ܶଵቁ + ቀܽ + ఏ ቁ ൫݁ିఏ்భ − 1൯ቃ For small value of ߠܶଵand using Taylor series approximation, we get I୲ଵ = (ఈఒିଵ)்భ మ ଶ . (3) The inventory level in a non-production period is represented by ௗூమ(௧మ) ௗ௧మ + ߠܫଶ(ݐଶ) = −ݐ(ܦଶ) 0 ≤ ݐଶ ≤ ܶଶ (4)
- 7. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 7 Since Iଶ(Tଶ) = 0 and using similar procedure we get the total inventory in a non-production period can be represented as I୲ଶ(tଶ) = ቂ (்మ) ఏ + ఏమቃ ݁ఏ(்మି ௧మ) − ቂ (௧మ) ఏ + ఏమቃ (5) I୲ଶ = ୈ(మ)మ మ ଶ . (6) Since Iଵ = Iଶ when ݐଵ = ܶଵ and ݐଶ = 0, we get the total inventory in a non-production period can be represented as ߙߣ − 1 ߠ ݐ(ܦଵ) + ܾ ܽ − ݁ିఏ்భ ൬ܽ + ܾ ߠ ൰൨ = ܶ(ܦଶ) ߠ + ܾ ߠଶ൨ ݁ఏ்మ − ܽ ߠ + ܾ ߠଶ൨ ܶଶ ≅ (ఈఒିଵ)ൣଶ்భି (ఏା)்భ మ൧ ଶ (7) Using similar steps as above, the total inventory level of perfect quality items in a rework production period and the total inventory level of perfect quality items in a rework non- production period time are derived as follows: I୲ଷ = (౨ି ) య మ ଶ (8) I୲ସ = (்ర) ర మ ଶ (9) Since Iଷ = Iସ when ݐଷ = ܶଷ and ݐସ = 0, we get ܶସ ≅ (ೝି)்యି ഇయ మ మ ൨ା ್య మ మ (10) The inventory level of imperfect quality items is shown in Figure-2. The inventory level of imperfect quality items in a production period can be modeled as ௗூೝభ(௧ೝభ) ௗ௧ೝభ + ߠܫଵ(ݐଵ) = (1 − ߙ)ܲ 0 ≤ ݐଵ ≤ ܶଵ (11) Since I୰ଵ(0) = 0, the inventory level of imperfect quality items in a production period is
- 8. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 8 ܫଵ(ݐଵ) = ߣ(ߙ − 1) ቄቀ ఏ + ఏమቁ ൫1 − ݁ିఏ௧ೝభ൯ − ௧ೝభ ఏ ቅ 0 ≤ ݐଵ ≤ ܶଵ (12) Using Taylor series approximation, the total inventory level of imperfect quality items in a production up time in one setup is TTIଵ = ఒ(ଵି ఈ)்భ మ ଶ (13) Since there are ݉ production setups in one cycle, the total inventory level of imperfect quality items in one cycle is: I୴ଵ = ఒ(ଵି ఈ) ்భ మ ଶ (14) The initial inventory level of imperfect quality items in each production setup is equal to I୰ and it can be modeled as: I୰ = ߣ(1 − ߙ) ቂቀ + ఏమቁ ൫1 − ݁ିఏ்భ൯ − ఏ ܶଵቃ Using Taylor series approximation, we get I୰ = ߣ(1 − ߙ) ቂܽܶଵ − (ܽߠ + ܾ) ்భ మ ଶ ቃ (15) The inventory level of imperfect quality items in a non-production period as: ௗூೝమ(௧ೝమ) ௗ௧ೝమ + ߠܫଶ(ݐଶ) = 0 0 ≤ ݐଶ ≤ (݉ − 1)ܶଵ + ݉ܶଶ (16) Since the inventory level ܫଶ(0) = I୰ , the inventory level of imperfect quality items in a non-production time for each production setup can be modeled as: ܫଶ(ݐଶ) = I୰ eି୲౨మ 0 ≤ ݐଶ ≤ (݉ − 1)ܶଵ + ݉ܶଶ (17) Using Taylor series expansion, the total inventory of imperfect quality items in a production down time in one setup is: TTIଶ = ∑ I୰ ቄሾ(k − 1)Tଵ + kTଶሿ − ሾ(୩ିଵ)భା ୩మሿమ ଶ ቅ ୀଵ (18) The total inventory of imperfect quality items in m production periods can be modeled as follows: I୴ଶ = ∑ IMr ቄሾ(k − 1)T1 + kT2ሿ − θሾ(k−1)T1+ kT2ሿ2 2 ቅ ୀଵ (19) Inventory level of imperfect quality items in the end of production cycle is equal to maximum
- 9. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 9 inventory level of imperfect quality items in a production setup reduced by deteriorating rate during production up time and down time. The maximum inventory level of imperfect quality items can be formulated as follows: I୰ = ∑ I୰ eିሾ(୩ିଵ)భା ୩మሿ ୀଵ (20) Using Taylor series expansion and then substituting I୰ we get I୰ = ∑ ቄλ(1 − α)(ܽܶଵ − (ܽߠ + ܾ) ்భ మ ଶ ቅ ൝ 1 − θሾ(k − 1)Tଵ + kTଶሿ + ሼሾ(୩ିଵ)భା ୩మሿሽమ ଶ ൡ ୀଵ (21) The inventory level of imperfect quality items in a rework period can be formulated as: ௗூೝయ(௧ೝయ) ௗ௧ೝయ + ߠܫଷ(ݐଷ) = − ܲ 0 ≤ ݐଷ ≤ ܶଷ (22) The inventory level of imperfect quality item in a rework period is: ܫଷ(ݐଷ) = ೝ ఏ ൣ݁ఏ(்యି ்ೝయ) − 1൧ (23) The total inventory of imperfect quality items in a rework period is: ܫଷ(ݐଷ) = ೝ ఏ ൣ݁ఏ(்యି ௧ೝయ) − 1൧ ݀ݐଷ ்య ௧ೝయୀ (24) Using Taylor series expansion, we get I୴ଷ = ౨య మ ଶ (25) When ݐଷ = 0, the number of imperfect quality items inventory is equal to I୰. Equation (24) becomes I୰ = ೝ ఏ ൣ݁ఏ்య − 1൧ (26) Since ߠܶଷ ≪ 1 and using Taylor series expansion results in: ܶଷ = ୍ు౨ ೝ (27) Substitute I୰ we have ܶଷ = ଵ ೝ ∑ ቄλ(1 − α)(ܽܶଵ − (ܽߠ + ܾ) ்భ మ ଶ ቅ ൝ 1 − θሾ(k − 1)Tଵ + kTଶሿ + ሼሾ(୩ିଵ)భା ୩మሿሽమ ଶ ൡ ୀଵ (28) The total inventory level of imperfect items is
- 10. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 10 ܴܶܫ = I୴ଵ + I୴ଶ + I୴ଷ ܴܶܫ = ఒ(ଵି ఈ) ்భ మ ଶ + ∑ I୰ ቄሾ(k − 1)Tଵ + kTଶሿ − ሾ(୩ିଵ)భା ୩మሿమ ଶ ቅ ୀଵ + ౨య మ ଶ (29) The number of deteriorating item is equal to the number of items produced minus the number of total demands. The total deteriorating units can be modeled as: ܦ = (݉ߙߣܶଵ ݂()ݐ + ܲ ܶଷ) − )ݐ(ܦሾ݉(ܶଵ + ܶଶ) + ܶଷ + ܶସሿ (30) The total inventory cost consists of production setup cost, rework setup cost, perfect items inventory cost, imperfect quality items inventory cost and deteriorating cost. The total inventory cost per unit time can be modeled as follows: ܶ,݉(ܶܥ ܶଵ) = ೞା ೝା ೞሾ(It1+ It2)+ It3+ It4ሿା ೝ(்ோூ)ା ି ఊ ሾ(T1+ T2)+ T3+ T4ሿ (31) The optimal solution must satisfy the following condition: డሾ்்(,்భ)ሿ డ்భ = 0 (32) And the optimal solution of m, denoted by m*, must satisfy the following condition: ܶ݉(ܶܥ∗ − 1, ܶଵ) ≥ ܶ݉(ܶܥ∗ , ܶଵ) ≤ ܶ݉(ܶܥ∗ + 1, ܶଵ) (33) Since the cost function equation (31) is a nonlinear equation and the second derivative of equation (31) with respect to ܶଵ is extremely complicated, closed form solution of (31) cannot be derived. However, by means of Maple mathematical software, one can indicate that equation (31) is convex for a small value of ܶଵ. The optimal ܶଵ value can be obtained using Maple mathematical software. Fig-4 shows that the total cost (TC1) per unit time is convex for small values of T1. The optimal total cost is equal to $ 1446.17 when T1 * = 0.03186 and ݉∗ = 3. Figure-4: Total cost per unit time in varies of T1
- 11. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 11 5. NUMERICAL EXAMPLE AND SENSITIVITY ANALYSIS In this section, a numerical example and sensitivity analysis are given to illustrate the model. Let Ks = $ 8 per production setup, Kr = $ 5 per rework setup, Pr = 10 units per unit time , C = $ 1000 per unit, hs = $4 per unit per unit time, hr = $2 per unit per unit time, Dc = $8 per unit, a = 100, b = 0.2, ߣ = 8, γ = 0.3, α = 0.8, θ = 0.06. The total cost, TC1 =TCT(m, T1) per unit time for varying T1 is shown in Figure-4. Fig-4 shows that the total cost per unit time is convex for small values of T1. The optimal total cost is equal to $1446.17 when ݉∗ = 3. The sensitive analysis is performed by changing each of the parameters by -60%, -40%, -20%, +20%, +40% and +60%. One parameter is taken at a time and the remaining parameters are kept unchanged. The m and T1 values for different values of parameters are shown in Table- 1.Table-1 shows that the number of production setup is sensitive to the changes in parameters hs, hr and θ. The number of production setup(m) increases with increasing hs and decreases when the value of parameters hr and θ increase. But the optimal production setup (݉∗ ) is not sensitive to other parameters. The optimal production time (T1 * ) decreases with the increasing Pr, a, hr and Dc values and it increases when the value of parameters Ks, b, γ, hs and θ increase. The optimal production period for varying parameters is shown in Fig-5. The figure shows that the optimal production period is sensitive to changes in θ, temperately sensitive to changes in hr and insensitive to changes in the other parameters. The optimal total cost per unit time for varying parameters is shown in Table-2. The Table-2 shows that the total cost per unit time increases when the value of parameters Ks, Kr, a, γ increases and decreases when the value of parameters Pr, b, Dc increases. But there is a fluxuation when changing the parameters hr, hs and θ. The optimal total cost is sensitive to θ, hs, hr and moderately sensitive to changes in a, γ and insensitive to the changes in the other parameters. Fig-6 shows that the total inventory cost per unit time for varying parameters. The total cost per unit time sensitive to changes in the parameters θ, γ, hs and moderately sensitive to changes in the parameters a and b. the inventory cost is insensitive with the other parameters.
- 12. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 12 Table-1: Sensitivity analysis of m and T1. Para- meter - 60 % changed - 40 % changed - 20 % changed + 20 % changed + 40 % changed +60 % changed m T1 m T1 m T1 m T1 m T1 m T1 Ks 3 0.03185 3 0.03185 3 0.03186 3 0.03186 3 0.03187 3 0.03187 Kr 3 031860 3 0.03186 3 0.03186 3 0.03186 3 0.03186 3 0.03186 Pr 3 0.03189 3 0.03188 3 0.03187 3 0.03185 3 0.03184 3 0.03183 a 3 0.03265 3 0.03221 3 0.03199 3 0.03177 3 0.03171 3 0.03166 b 3 0.03153 3 0.03164 3 0.03175 3 0.03197 3 0.03208 3 0.03219 γ 3 0.03142 3 0.03157 3 0.03171 3 0.0320 3 0.03215 3 0.03229 hs 2 0.03171 2 0.03263 3 0.03116 3 0.03252 3 0.03314 3 0.03373 hr 3 0.0368 3 0.03431 3 0.03285 3 0.03115 2 0.03273 2 0.03221 Dc 3 0.03187 3 0.03187 3 0.03186 3 0.03186 3 0.03185 3 0.03185 θ 3 0.02248 3 0.02521 3 0.02835 2 0.0381 1 0.04744 1 0.05189 Figure-5: T1 sensitivity analysis Table-2: Sensitivity analysis for the total cost per unit time($) 0 0.01 0.02 0.03 0.04 0.05 0.06 Ks Kr Pr a b γ hs hr Dc
- 13. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 13 Para- meter -60 % changed - 40 % changed - 20 % changed + 20 % changed + 40 % changed +60 % changed Ks 1423.630768 1431.144844 1438.658467 1453.682105 1461.192045 1468.701608 Kr 1441.475399 1443.040361 1444.605323 1447.735248 1449.30021 1450.865173 Pr 1496.999113 1480.084067 1463.141156 1429.171361 1412.144288 1395.088969 a 89.34710077 544.936701 996.3801418 1895.135588 2343.629474 2791.830108 b 1899.920936 1749.304135 1598.054555 1293.649444 1140.490171 986.69063 γ 696.4724061 947.5418524 1197.432327 1693.782443 1940.291696 2185.722348 hs 817.6149026 1992.216038 225.3036874 2642.342372 3816.210375 4969.760648 hr 4636.914177 3678.203827 2594.017105 260.9441034 2999.770418 2339.404367 Dc 1466.020425 1459.404589 1452.78819 1439.552382 1432.933049 1426.313076 θ 11977.94625 8656.874909 5172.802685 1541.711816 4644.169125 3221.004949 Figure-6: Total cost per unit time sensitivity analysis 0 2000 4000 6000 8000 10000 12000 14000 Ks Kr Pr a b γ hs hr
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- 16. Operations Research and Applications : An International Journal (ORAJ), Vol.4, No.1, February 2017 16 [33] Wee, H.M., Yu, J., Chen, M.C., ( 2007). Optimal inventory model for items with imperfect quality and shortage backordering. Omega, 35(1), 7–11. [34] Widyadana, G. A., Wee, H. M. (2012). An economic production quantity model for deteriorating items with multiple production setups and rework. International Journal of Production Economics, 138(1), 62–67. [35] Yassine, A., Maddah, B., Salameh, M., (2012). Disaggregation and consolidation of imperfect quality shipments in an extended EPQ model. International Journal of Production Economics, 135(1), 345–52. [36] Yoo, S.H., Kim, D.S., Park, M.S., (2009). Economic production quantity model with imperfect- quality items, two-way imperfect inspection and sales return. International Journal of Production Economics 121, 255–265. NOTES ON CONTRIBUTORS Mr T.Sekar is a Part-Time research scholar, Department of Mathematics, Gandhigram Rural Institute –Deemed University, Gandhigram – 624302, Tamil Nadu, India. He received his B. Sc degree from Govt. arts college karur, Bharathidasan University, M. Sc and M. Phil degree from National College, Trichy, Bharathidasan University, Tamil Nadu, India. Currently, he is working in EBET Group of Institutions, Tirupur, Tamil Nadu, India. His research interests are in the field of inventory management and control, optimization techniques and their applications. Dr R. Uthayakumar was born in Dindigul, India, in 1967. He received the PhD degree from the Gandhigram Rural Institute –Deemed University, Gandhigram, Tamil Nadu, India, in 2000. Currently, he is a Professor & Head, Department of Mathematics, Gandhigram Rural Institute –Deemed University, Gandhigram. He has done research in the area of functional analysis. His current research interests are primarily in the field of optimization and the Theory of Fractal Analysis. He is also interested in finding out the general mathematical model in the field of inventory management and supply chain. To his credit, he has published many research articles in the area of inventory management and supply chain in reputed international journals. Moreover, he is guiding many scholars in the above said areas.