Deteriorating Inventory Model When Demand Depends on Advertisement and Stock Display

Similar documents
An Inventory Model for Time Dependent Weibull Deterioration with Partial Backlogging

Production Inventory Model with Different Deterioration Rates Under Shortages and Linear Demand

Deteriorating Inventory Model with Time. Dependent Demand and Partial Backlogging

International Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 6, Nov-Dec 2015

An Inventory Model for Constant Deteriorating Items with Price Dependent Demand and Time-varying Holding Cost

Inventory Control of Perishable Items in a Two-Echelon Supply Chain

An Inventory Model of Repairable Items with Exponential Deterioration and Linear Demand Rate

International Journal of Industrial Engineering Computations

A Study of Inventory System with Ramp Type Demand Rate and Shortage in The Light Of Inflation I

On a Discrete-In-Time Order Level Inventory Model for Items with Random Deterioration

Key words: EOQ, Deterioration, Stock dependent demand pattern

EOQ Model with Time Induced Demand, Trade Credits and Price Discount on Shortages: A Periodic Review

A STUDY OF INFLATION EFFECTS ON AN EOQ MODEL FOR WEIBULL DETERIORATING/AMELIORATING ITEMS WITH RAMP TYPE OF DEMAND AND SHORTAGES

An Inventory Model with Variable Demand Rate for Deteriorating Items under Permissible Delay in Payments

International Journal of Supply and Operations Management

MANUFACTURER-SUPPLIER COOPERATIVE INVENTORY MODEL FOR DETERIORATING ITEM WITH TRAPEZOIDAL TYPE DEMAND

Inventory Models with Weibull Deterioration and Time- Varying Holding Cost

An EPQ Inventory Model with Variable Holding Cost and Shortages under the Effect of Learning on Setup Cost for Two Warehouses

Two New Uncertainty Programming Models of Inventory with Uncertain Costs

An Inventory Model for Weibull Time-Dependence. Demand Rate with Completely Backlogged. Shortages

An EOQ Model with Verhulst s Demand and Time-Dependent Deterioration Rate

Applying Genetic Algorithms for Inventory Lot-Sizing Problem with Supplier Selection under Storage Capacity Constraints

Optimal Replenishment Policy for Ameliorating Item with Shortages under Inflation and Time Value of Money using Genetic Algorithm

Research Article Order Level Inventory Models for Deteriorating Seasonable/Fashionable Products with Time Dependent Demand and Shortages

Research Article The Optimal Replenishment Policy under Trade Credit Financing with Ramp Type Demand and Demand Dependent Production Rate

Excel-Based Solution Method For The Optimal Policy Of The Hadley And Whittin s Exact Model With Arma Demand

An EOQ Inventory Model for Deteriorating Items with Linear Demand, Salvage Value and Partial Backlogging

Competitive and Cooperative Inventory Policies in a Two-Stage Supply-Chain

Errata (1 st Edition)

An Inventory Model with Time-Varying Demand for Non- Instantaneous Deteriorating Items with Maximum Life Time

CENTRALIZED VERSUS DECENTRALIZED PRODUCTION PLANNING IN SUPPLY CHAINS

Inventory Analysis and Management. Multi-Period Stochastic Models: Optimality of (s, S) Policy for K-Convex Objective Functions

CHAPTER 2 Signals And Spectra

Title: Leadtime Management in a Periodic-Review Inventory System: A State-Dependent Base-Stock Policy

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon

An introduction to the theory of SDDP algorithm

International Journal of Approximate Reasoning

Macroeconomic Theory Ph.D. Qualifying Examination Fall 2005 ANSWER EACH PART IN A SEPARATE BLUE BOOK. PART ONE: ANSWER IN BOOK 1 WEIGHT 1/3

Probabilistic Models for Reliability Analysis of a System with Three Consecutive Stages of Deterioration

Solutions to Assignment 1

EE 315 Notes. Gürdal Arslan CLASS 1. (Sections ) What is a signal?

Stochastic Perishable Inventory Systems: Dual-Balancing and Look-Ahead Approaches

AN INVENTORY MODEL FOR DETERIORATING ITEMS WITH EXPONENTIAL DECLINING DEMAND AND PARTIAL BACKLOGGING

not to be republished NCERT MATHEMATICAL MODELLING Appendix 2 A.2.1 Introduction A.2.2 Why Mathematical Modelling?

Vehicle Arrival Models : Headway

Stochastic Model for Cancer Cell Growth through Single Forward Mutation

04. Kinetics of a second order reaction

Comparing Theoretical and Practical Solution of the First Order First Degree Ordinary Differential Equation of Population Model

Problem Set 5. Graduate Macro II, Spring 2017 The University of Notre Dame Professor Sims

The Optimal Stopping Time for Selling an Asset When It Is Uncertain Whether the Price Process Is Increasing or Decreasing When the Horizon Is Infinite

Evaluation of Mean Time to System Failure of a Repairable 3-out-of-4 System with Online Preventive Maintenance

Applying Genetic Algorithms for Inventory Lot-Sizing Problem with Supplier Selection under Storage Space

On Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature

International Journal of Scientific & Engineering Research, Volume 4, Issue 10, October ISSN

This document was generated at 7:34 PM, 07/27/09 Copyright 2009 Richard T. Woodward

Determining a production run time for an imperfect production-inventory system with scrap

Exponential Weighted Moving Average (EWMA) Chart Under The Assumption of Moderateness And Its 3 Control Limits

Exponentially Weighted Moving Average (EWMA) Chart Based on Six Delta Initiatives

Application of a Stochastic-Fuzzy Approach to Modeling Optimal Discrete Time Dynamical Systems by Using Large Scale Data Processing

On the Optimal Policy Structure in Serial Inventory Systems with Lost Sales

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle

Shiva Akhtarian MSc Student, Department of Computer Engineering and Information Technology, Payame Noor University, Iran

A Deterministic Inventory Model for Deteriorating Items with Price Dependent Demand and Time Varying Holding Cost under Trade Credit

Problem 1 / 25 Problem 2 / 20 Problem 3 / 10 Problem 4 / 15 Problem 5 / 30 TOTAL / 100

On Multicomponent System Reliability with Microshocks - Microdamages Type of Components Interaction

Intermediate Macro In-Class Problems

Solutions Problem Set 3 Macro II (14.452)

Georey E. Hinton. University oftoronto. Technical Report CRG-TR February 22, Abstract

Chapter 2. Models, Censoring, and Likelihood for Failure-Time Data

MATHEMATICAL DESCRIPTION OF THEORETICAL METHODS OF RESERVE ECONOMY OF CONSIGNMENT STORES

T L. t=1. Proof of Lemma 1. Using the marginal cost accounting in Equation(4) and standard arguments. t )+Π RB. t )+K 1(Q RB

Chapter 2. First Order Scalar Equations

Stability and Bifurcation in a Neural Network Model with Two Delays

T. J. HOLMES AND T. J. KEHOE INTERNATIONAL TRADE AND PAYMENTS THEORY FALL 2011 EXAMINATION

E β t log (C t ) + M t M t 1. = Y t + B t 1 P t. B t 0 (3) v t = P tc t M t Question 1. Find the FOC s for an optimum in the agent s problem.

SUPPLEMENTARY INFORMATION

Maintenance Models. Prof. Robert C. Leachman IEOR 130, Methods of Manufacturing Improvement Spring, 2011

ANSWERS TO EVEN NUMBERED EXERCISES IN CHAPTER 6 SECTION 6.1: LIFE CYCLE CONSUMPTION AND WEALTH T 1. . Let ct. ) is a strictly concave function of c

Modal identification of structures from roving input data by means of maximum likelihood estimation of the state space model

23.5. Half-Range Series. Introduction. Prerequisites. Learning Outcomes

The Production-Distribution Problem in the Supply Chain Network using Genetic Algorithm

The Fisheries Dissipative Effect Modelling. Through Dynamical Systems and Chaos Theory

Evaluation of maintenance policies for equipment subject to quality shifts and failures

Math 115 Final Exam December 14, 2017

Chapter 10 INDUCTANCE Recommended Problems:

Reserves measures have an economic component eg. what could be extracted at current prices?

PROOF FOR A CASE WHERE DISCOUNTING ADVANCES THE DOOMSDAY. T. C. Koopmans

RC, RL and RLC circuits

Semi-Competing Risks on A Trivariate Weibull Survival Model

5. Stochastic processes (1)

STATE-SPACE MODELLING. A mass balance across the tank gives:

Lab 10: RC, RL, and RLC Circuits

Single-Pass-Based Heuristic Algorithms for Group Flexible Flow-shop Scheduling Problems

Robust estimation based on the first- and third-moment restrictions of the power transformation model

Ann. Funct. Anal. 2 (2011), no. 2, A nnals of F unctional A nalysis ISSN: (electronic) URL:

Sliding Mode Extremum Seeking Control for Linear Quadratic Dynamic Game

t dt t SCLP Bellman (1953) CLP (Dantzig, Tyndall, Grinold, Perold, Anstreicher 60's-80's) Anderson (1978) SCLP

EXERCISES FOR SECTION 1.5

Navneet Saini, Mayank Goyal, Vishal Bansal (2013); Term Project AML310; Indian Institute of Technology Delhi

Energy Storage Benchmark Problems

Transcription:

Inernaional Journal of Operaions Research Inernaional Journal of Operaions Research Vol. 6, No. 2, 33 44 (29) Deerioraing Invenory Model When Demand Depends on Adverisemen and Sock Display Nia H. Shah, and Poonam Pandey 2 Deparmen of Mahemaics, Gujara Universiy Ahmedabad Gujara, India Corresponding Auhor: Dr. Nia H. Shah 2 Globsyn Business School Campus 97/A, Uvarsad, Off NH-8, Dis:Gandhinagar, Gujara Pin - 382422 India. Received December 6 h 28; Acceped Ocober 2 h 29 Absrac In his sudy, a mahemaical model is developed o obain opimal ordering policy of ime dependen deerioraing iem when demand rae is dependen on displayed sock level and frequency of adverisemen hrough media. Shorages are no allowed. The objecive is o minimize oal cos. The significan feaures and he resuls are sudied wih he help of a numerical example. The effec of changes in he demand parameer, deerioraion rae ( α -consan deerioraion, - ime dependen deerioraion), rae of frequency of adverisemens, sock dependen parameer and salvage parameer for deerioraed iems on oal cycle ime, oal cos and on procuremen quaniy is sudied numerically. Keyword Lo size, Time dependen deerioraion, Adverisemen frequency, Procuremen quaniy and oal cos.. INTRODUCTION Classical invenory Economic order quaniy (EOQ) model is based on he assumpion ha an iem in sock remains o is % efficiency for infinie ime. Acually iems like volaile liquids, blood, X ray plaes, medicines, elecronic componens, fashion goods, fruis and vegeable ec. looses heir uiliy afer some ime. The mos of he mos of he aricles were based on he fac ha hese deerioraed unis are complee loss o he invenory sysem. See review aricles by Raafa (99), Shah and Shah (2), Goyal and Giri (2). These days adverisemens and display of producs plays a vial role in aracing mass cusomer. The adverisemen hrough elecronic media, news paper, inerne, using innovaive ways of display of produc in he show room is he bes ool for he promoion of a produc. This araced researcher o analyze he invenory problem when demand depends on sock displayed. Refer o Baker and Urban (988), Mandal and Phaujdar (989), Daa and Pal (99), Padmanabhan and Vra (996), Giri e al. (996), Sarkar e al. (997) ec. The effec of adverisemen on he demand of he produc is sudied by Bhunia and Maii (997), Goyal and Gunasekaran (995), Pal e al. (996, 26) developed an invenory model for deerioraing iems by aking ino accoun he impac of markeing sraegies viz pricing, adverisemen and he displayed sock level of he demand rae of he sysem. These aricles opimize oal cos or ne profi per ime uni of an invenory sysem. Misra (979 a) sudied he invenory decisions under inflaionary condiions for EOQ model. Misra (979 b) derived an inflaion model for he EOQ, in which he ime value of money and differen inflaion raes were considered. Gurunani (983) gave he economic analysis of invenory sysems and claimed ha he discouning effecs on EOQ were subsanial. Relaed aricles are by Queyranne (985), Roundy (986), Federgruen e al. (992), Shah e al. (23, 24) In his paper, a mahemaical invenory model for ime dependen deerioraing iems is developed by considering demand o be funcion of adverisemen and he displayed sock level. Shorages are no allowed. The sorage capaciy of he invenory sysem is finie. The objecive is o minimize oal cos. 2. ASSUMPTIONS AND NOTATIONS The Mahemaical model is derived using he following assumpions and noaions 2. Noaions T : Cycle ime (decision variable) Corresponding auhor s e-mail: niahshah@gmail.com, shahniah@gmail.com 83-73X Copyrigh 29 ORSTW

34 Shah and Pandey: Deerioraing Invenory Model When Demand Depends on Adverisemen and Sock Display IJOR Vol. 6, No. 2, 33 44 (29) : ime poin when he sock level is S : ime poin when he sock level is S( S > S ) C : purchase cos per uni iem h : invenory holding cos per uni per uni ime G : cos of adverisemen O : ordering cos per order : rae of change of frequency of adverisemen γ : salvage parameer for deerioraed unis A : frequency of adverisemen in he cycle a : fixed demand b : rae of change of demand q (): invenory level a any insan of ime during cycle ime θ( ): θ() = α ( Weibull disribuion ) where α >, >. Here > is considered which means deerioraion increases wih ime. S : order quaniy per cycle K :oal cos per ime uni. 2.2 Assumpions. The invenory sysem deals wih a single iem. 2. Replenishmen rae is infinie. 3. Shorages are no allowed and lead ime is zero or negligible. 4. The planning horizon is finie. 5. The deerioraion rae of unis in invenory follows he Weibull disribuion funcion given by θ() = α where α >, >. Here > is considered which means deerioraion increases wih ime. 6. Deerioraed unis can neiher be repaired nor replaced during he cycle ime. 7. The demand rae RAq (, ) is a funcion of he frequency of adverisemen A and he displayed invenory level in he super mall funcional form as RAq (, ) = A( a+ bs ) for q > S = A ( a+ bq( )) for S < q S = A ( a+ bs ) for q > S where ab, >, a > b. 3. MATHEMATICAL MODEL In his model, he cycle sars wih on hand invenory level S a = afer clearing shorages. Then invenory level deplees coninuously and reaches o zero a = T due o demand and consan rae of deerioraion of unis. Meanwhile invenory reaches o S a some ime and i reaches o S a some ime as shown in he Figure.

35 Shah and Pandey: Deerioraing Invenory Model When Demand Depends on Adverisemen and Sock Display IJOR Vol. 6, No. 2, 33 44 (29) S Invenory level S S T Time Figure. Time invenory saus. The following differenial equaions represen he invenory level q () a any insan of ime. dq() d dq2() d ( ) q () A a bs, + α = + ( ) q () A a bq (), + α 2 = + 2 () (2) dq3() d ( ) + α 3() = +, q A a bs T wih boundary condiions q() = S, q2( ) = S, q3( ) = S, q3( T ) = (4) The soluion of differenial Eq () (3) using (4) is + α q() = S( α ) A ( a+ bs) (5) + (3) () ( α ( ) ( )) q = S ba aa α + + ( ) + α ( ) + + ( ) 2 2 2 2 2 α + ba (6) () ( ( )) ( ) α + + = α + + ( ) α ( ) q3 S A a bs (7) + Using boundary condiion (4), we have + α S = S( + α ) + ( a+ bs) + (8) + Now oal invenory from (, T) is T IT = qd+ qd+ qd (9) 2 3 Number of deerioraed during posiive invenory ime inerval is = = + 2 DU S A a A b( S S ( T )) A b q ( ) d ()

Shah and Pandey: Deerioraing Invenory Model When Demand Depends on Adverisemen and Sock Display IJOR Vol. 6, No. 2, 33 44 (29) Hence, oal cos per ime uni T is KT ( ) = ( Invenory holding cos + Adverisemen cos + ordering cos + cos due o Deerioraion salvage value of he deerioraed unis)/t. = ( h IT + A G + OC + C DU C γ DU ) T The necessary condiion for KT ( ) o be minimum is KT ( ) = and solving i for T by a suiable mahemaical T sofware For obained T, KT ( ) is minimum only if 2 K > for all T >. 2 T 4. COMPUTATIONAL ALGORITHM The following seps are o be compleed for he opimal soluion Sep : sar wih A =. Sep 2: compue,, T, K( T ). Sep 3: Incremen A by. Sep 4: Perform sep 2 unil K( A,,, T) K( A,,, T) K( A+,,, T ). Sep 5: sop. 5. NUMERICAL EXAMPLE Consider an invenory sysem wih following parameers in proper unis : [ OhCG,,,,, ab,, γ, α,, S, S ] = [,,,,.3, 2,.3,.2,.,3.5,4,5]. The opimum value of frequency A is 3, =.898 years, = 2.52 years, T = 3.6685 years, KT ( ) = $ 672.22, S = 4479.4 unis (see Figure 2). The sensiiviy analysis is carried ou o sudy he effec of deerioraion of unis; α and, sock dependen parameer; b, rae of change of frequency;, and salvage parameer; γ of deerioraed unis on he objecive funcion are exhibied in following ables: 36 Figure 2. Convexiy of KT ( ).

37 Shah and Pandey: Deerioraing Invenory Model When Demand Depends on Adverisemen and Sock Display IJOR Vol. 6, No. 2, 33 44 (29) Table. Effec of.3.32.34.36 T 3.6685 3.657 3.646 3.635.898.896.8854.8792 2.52 2.486 2.472 2.4579 K 672.22 67.9 644.48 68.97 S 4479.4 458.36 4538.68 457.5 Table 2. Effec of α α..2.8.2 T 3.6684 3.5444 3.2935 3.2335.8898.8423.73.748 2.5 2.4286 2.28 2.2434 K 672.22 6276.4 6289.49 6997.95 S 4479.9 45.9 463.82 4672.89 Table 3. Effec of 3.3 3.5 3.7 4. T 3.8777 3.6685 3.4887 3.2623.959.898.8448.7767 2.588 2.52 2.4299 2.3396 K 5873.83 672.22 627.42 657. S 453.7 4479.4 4445. 4452.64 Table 4. Effec of γ γ..2.3.4 T 3.767 3.6684 3.628 3.5855.893.898.944.928 2.4937 2.52 2.583 2.585 K 6562.48 672.2 5576.38 574.64 S 4452.36 4479.4 452. 4554.8 Table 5. Effec of b b..5.2 T 4.2 3.8264 3.6684 2.252 2.49.898 3.462 2.8499 2.52 K 84.55 72.87 682.3 S 667 5678 4236

38 Shah and Pandey: Deerioraing Invenory Model When Demand Depends on Adverisemen and Sock Display IJOR Vol. 6, No. 2, 33 44 (29) Table 6. Effec of α & γ on decision variables γ α.3.32.34 2.94 2.875 2.8 2.8579 2.8487 2.8394. T 3.99 3.98 3.935 KT ( ) 5367.27 5367.27 5398.99 S 3977.77 3988.5 3999.24 2.37 2.36 2.242.2.4 2.7665 2.758 2.7493 T 3.8263 3.8527 3.843 KT ( ) 5566.32 5583.9 5599.77 S 49.99 43.7 442.2.992.985.9787 2.693 2.6833 2.6752 T 3.7594 3.75 3.745 KT ( ) 5747.7 5764.93 5782.58 S 46.3 47.89 483.43 Table 7. Effec of 3.3 3.5 3.7 & on decision variables..3.32.34 2.64 2.572 2.55 2.965 2.955 2.945 T 4.27 4.266 4.963 KT ( ) 52.77 537.6 554.7 S 3636.27 3979.37 425.38 2.94 2.874 2.8 2.8579 2.8487 2.8394 T 3.99 3.98 3.973 KT ( ) 5367.27 5383.4 5398.98 S 3977.78 3988.5 3999.24 2.333 2.269 2.27 2.7674 2.7589 2.753 T 3.795 3.7856 3.7763 KT ( ) 562.4 5635.24 565. S 43.5 42.7 422.33

39 Shah and Pandey: Deerioraing Invenory Model When Demand Depends on Adverisemen and Sock Display IJOR Vol. 6, No. 2, 33 44 (29) Table 8. Effec of γ.3.32.34 & γ on decision variables..2.3 2.856 2.94 2.55 2.8638 2.8579 2.85 T 4.352 3.99 3.9425 KT ( ) 585.7 5367.27 4876.5 S 3999.82 3977.78 3952.49 2.874 2.792 2.986 2.8486 2.854 2.8424 T 3.98 4.25 3.9329 KT ( ) 5866.88 5383.4 4892.69 S 3988.5 5866.88 3965.3 2.729 2.8 2.5 2.8443 2.8394 2.89 T 4.5 3.973 3.8649 KT ( ) 5882.6 5398.99 499.8 S 47.6 3999.24 3923.97 Table 9. Effec of b.3.32.34 b & on decision variables.2.3.4 2.279 2.94 2.395 3.47 2.8579 2.727 T 4.36 3.99 3.96 KT ( ) 63.98 5367.27 499.85 S 4483.36 3977.78 3697.3 2.286 2.875 2.34 3.328 2.8487 2.774 T 4.26 3.98 3.924 KT ( ) 649.49 5383.4 4922.5 S 45.8 3988.5 376.72 2.995 2.8 2.284 3.248 2.8394 2.778 T 4.99 3.973 3.893 KT ( ) 664.79 5398.99 4935.24 S 456.88 3999.24 376.27

4 Shah and Pandey: Deerioraing Invenory Model When Demand Depends on Adverisemen and Sock Display IJOR Vol. 6, No. 2, 33 44 (29) Table. Effec of b 3.3 3.5 3.7 & b on decision variables.2.3.4 2.295 2.64 2.6 3.63 2.965 2.884 T 4.377 4.27 4.328 KT ( ) 5835.49 52.78 4689. S 4474.55 3967.44 3694.35 2.279 2.94 2.395 3.47 2.8579 2.727 T 4.36 3.99 3.96 KT ( ) 63.98 5367.27 499.85 S 4483.36 3977.78 3679.3 2.58 2.333.988 2.9326 2.7674 2.6467 T 3.9262 3.795 3.728 KT ( ) 6398.4 562.4 536.53 S 456.9 43.5 375.25 Table. Effec of α & on decision variables α 3.3 3.4 3.5 2.64 2.94 2.33 2.965 2.8579 2.7674..2.4 T 4.27 3.99 3.795 KT ( ) 52.77 5367.27 562.4 S 3967.44 3977.78 43.5 2.4 2.375.982 2.859 2.7665 2.6829 T 4.732 3.8624 3.688 KT ( ) 535.3 5566.32 5834.22 S 3999.36 49.99 454.26 2.495.996.94 2.7792 2.693 2.63 T 3.9574 3.7594 3.5884 KT ( ) 5473.37 5747.7 627.63 S 43.54 46.3 4.65

4 Shah and Pandey: Deerioraing Invenory Model When Demand Depends on Adverisemen and Sock Display IJOR Vol. 6, No. 2, 33 44 (29) Table 2. Effec of γ α..2.4 α & γ on decision variables..2.3 2.856 2.94 2.55 2.8638 2.8579 2.85 T 4.352 3.99 3.9425 KT ( ) 585.82 3977.78 3952.49 S 3999.82 3977.78 3952.49 2.293 2.37 2.477 2.7752 2.7665 2.7563 T 3.98 3.8624 3.842 KT ( ) 688.3 5566.32 537.93 S 454.83 49.99 398.22.9843.996 2.5 2.723 2.693 2.6786 T 3.857 3.7549 3.77 KT ( ) 633.66 5747.7 584.24 S 46.7 46.3 47.6 Table 3. Effec of b α..2.4 α & b on decision variables.2.3.4 2.279 2.94 2.395 3.47 2.8579 2.727 T 4.36 3.99 3.96 KT ( ) 63.98 5369.27 499.85 S 4483.36 3977.78 3697.3 2.65 2.37.9823 2.9338 2.7664 2.554 T 3.9992 3.862 3.786 KT ( ) 633.55 5566.32 54.9 S 456.28 49.99 3742.84 2.44.996.9367 2.846 2.693 2.5772 T 3.892 3.7594 3.6848 KT ( ) 6472.74 5747.7 5283.22 S 4528.6 46.3 3783.57

42 Shah and Pandey: Deerioraing Invenory Model When Demand Depends on Adverisemen and Sock Display IJOR Vol. 6, No. 2, 33 44 (29) Table 4. Effec of γ 3.3 3.5 3.7 & γ on decision variables..2.3 2.55 2.64 2.76 2.9646 2.965 2.9558 T 4.2652 4.27 2.9558 KT ( ) 557.69 52.76 4663.26 S 398.24 3967.44 395.56 2.856 2.94 2.55 2.8638 2.8579 2.85 T 4.35 3.99 3.9425 KT ( ) 585.7 5367.27 4876.5 S 3999.82 3977.78 3952.49 2.254 2.33 2.44 2.775 2.7674 2.758 T 3.8375 3.795 3.75 KT ( ) 64.53 562.4 593.7 S 3389.32 43.5 3967.87 Table 5. Effec of γ & b on decision variables b γ.2.3.4..2.3 2.997 2.856 2.338 3.56 2.8638 2.7279 T 4.77 4.352 3.9554 KT ( ) 676.63 585.7 5332.3 S 4548.55 3999.82 37.35 2.279 2.94 2.395 3.47 2.8579 2.727 T 4.36 3.99 3.96 KT ( ) 63.98 5367.27 499.85 S 4483.36 3977.78 5498.8 2.2438 2.55 2.47 3.233 2.85 2.726 T 4.86 3.9425 3.8653 KT ( ) 554.87 4876.5 448.65 S 44.22 3952.49 3693.83

43 Shah and Pandey: Deerioraing Invenory Model When Demand Depends on Adverisemen and Sock Display IJOR Vol. 6, No. 2, 33 44 (29) Observaions As deerioraion parameer α and increases oal cycle ime decreases whereas oal cos increases. Toal cos and cycle ime boh decreases as salvage parameer γ increases. This is because here is some savings by selling deerioraed iems insead of jus hrowing i away. As sock displayed parameer b increases oal cos and cycle ime boh decreases. The reailer will have o pu orders frequenly resuling increase in he oal cos. Cycle ime decreases whereas oal cos increases as rae a which adverisemen is displayed increases. All hese are criical facors in deciding opimal ordering policy o minimize he objecive funcion oal cos. This sensiiviy analysis done here shows ha sock dependen demand increases oal cos decreases significanly. Frequen adverisemen is going o increase in oal cos bu if salvage parameer increases significanly incremen in oal cos can be checked. 6. CONCLUSIONS In marke, he unis deeriorae due o vaporizaion; damages while loading and unloading, perishabliliy and many oher facors. So he deerioraion effec should no be ignored while compuing invenory cos of he reailer. For producs like food grains, blood componens, vegeables and fruis, pharmaceuicals ec. uiliy decreases wih passage of ime and hence reailer needs o find rade off among invenory carrying cos and deerioraion cos. On he oher hand, reailer uses media for he sale of he produc hrough adverisemen and displayed sock o arac he cusomers. This concep encourage auhor o develop propose model. This model can be exended o he incorporae selling price and adverisemen dependen demand. Acknowledgemens The auhors are hankful o anonymous reviewers for consrucive suggesions. REFERENCES. Baker, R. C. and Urban, L. A. (988). Deerminisic invenory sysem wih an invenory level dependen demand rae, Journal of he Operaional Research Sociey, 29: 823 83. 2. Bhunia, A. K., Maii, M. 997. An invenory model for decaying iems wih selling price, frequency of adverisemen and linearly ime dependen demand wih shorages. IAPQR Transacions, 22: 4 49. 3. Daa, T. K. and Pal, A. K. (99). A noe on an invenory model wih invenory - level - dependen demand rae, Journal of he Operaional Research Sociey, 4: 97 975. 4. Federgruen, A., Queryranne, M. and Zheng, Y. S. (992). Simple power of wo policies are close o opimal in general class of producion / disribuion neworks wih general join seup coss, Mahemaics of Operaions Research, 7(4): 95 963. 5. Giri, B. C., Pal, S., Goswami, A., Chaudhuri, K. S. 996. An invenory model for deerioraing iems wih sock dependen demand rae. European Journal of Operaions Research, 95: 64 6. 6. Goyal, S. K., Gunasekaran, A. 995. An inegraed producion- invenory-markeing model for deerioraing iems. Compuers and Indusrial Engineering, 28: 755 762. 7. Goyal, S. K. and Giri, B. C. (2). Recen rends in modeling deerioraed invenory. European Journal of Operaional Research, 34: 6. 8. Gurnani, C. (983). Economic analysis of invenory sysems, Inernaional Journal of Producion Research, 2 (2): 26 277. 9. Mandal, B. N. and Phaujdar, S. (989). An invenory model of deerioraing iems and sock dependen consumpion rae, Journal of Operaional Research Sociey, 4: 483 488.. Misra, R. B. (979 - a). A sudy of inflaion effecs of invenory sysem, Logisics Specrum, 9: 26 268.. Misra, R. B. (979 - b). A noe on opimal invenory managemen under inflaion, Naval Research Logisics, 26: 6 65. 2. Padmanabhan, G. and Vra, P. (995). EOQ models for perishable iems under sock dependen selling rae, European Journal of Operaional Research, 86: 28 292. 3. Pal, S., Goswami, A., Chaudhuri, K. S., Pal, A. K. (996). An invenory model wih wo componen demand rae and shorages. Journal of he Operaional Research Sociey, 47: 29 36. 4. Pal, A. K., Bhunia, A. K. and Mukharjee, R. N. (26). Opimal lo size model for deerioraing iems wih demand rae dependen on displayed sock level (DSL) and parial backordering. European Journal of Operaional Research, 75: 977 99. 5. Queryanne, M. (985). A polynomial - ime sub-modular exension o Roundy s 98 % effecive heurisic for producion / invenory sysems, working paper no.36, Faculy of Commerce and Business Adminisraion, Universiy of Briish Columbia, Vancouver, B. C. Canada.

Shah and Pandey: Deerioraing Invenory Model When Demand Depends on Adverisemen and Sock Display IJOR Vol. 6, No. 2, 33 44 (29) 6. Raafa, F. (99). Survey of lieraure on coninuously deerioraing invenory models, Journal of he Operaional Research Sociey, 4: 27 37. 7. Roundy, R. (986). A 98 % - effecive lo sizing rule a muli produc, muli sage producion invenory sysem, Mahemaics of Operaions Research, : 699 727. 8. Sarker, B. R., Mukharjee, S. and Balan, C. V. (997). An order level of lo size invenory model wih invenory level dependen demand and deerioraion, Inernaional Journal of Producion Economics, 48: 227 236. 9. Shah, Nia H., and Shah, Y. K. (2). Lieraure survey on invenory model for deerioraing iems, Economic Annals, XLIV: 22 237. 2. Shah, Nia H., Shah B. J. and Shah, Y. K. (23). Presen value formulaion of economic lo size model for invenory sysem wih variable deerioraing iems, Modeling Measuremen and Conrol (France), 24(2): 7 28. 2. Shah, Nia H., Shah B. J. and Shah, Y. K. (24). An order level lo size model wih sock dependen demand for ime dependen rae of deerioraion of iems under permissible delay in paymens, For Eas Journal of Theoreical Saisics, 2(2): 2 22. 44