A MODEL WITH STORAGE LIMITATION AND SIMULATED DEMAND AS FRESH MEAT INVENTORY MANAGEMENT SUPPORT



Similar documents
Conversion of Non-Linear Strength Envelopes into Generalized Hoek-Brown Envelopes

APPENDIX III THE ENVELOPE PROPERTY

Supply Chain Management Chapter 5: Application of ILP. Unified optimization methodology. Beun de Haas

Sequences and Series

16. Mean Square Estimation

Chapter 6 Best Linear Unbiased Estimate (BLUE)

6.7 Network analysis Introduction. References - Network analysis. Topological analysis

Average Price Ratios

In the UC problem, we went a step further in assuming we could even remove a unit at any time if that would lower cost.

Software Size Estimation in Incremental Software Development Based On Improved Pairwise Comparison Matrices

A Parallel Transmission Remote Backup System

CANKAYA UNIVERSITY FACULTY OF ENGINEERING MECHANICAL ENGINEERING DEPARTMENT ME 212 THERMODYNAMICS II HW# 11 SOLUTIONS

Paper Technics Orientation Course in Papermaking 2009:

SOME ASPECTS OF REPRESENTATION OF INDUCTANCE DISTRIBUTIONS IN DQ0-AXES IN A SALIENT POLE SYNCHRONOUS GENERATOR

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time.

Derivatives and Rates of Change

Bayesian Updating with Continuous Priors Class 13, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom

We will begin this chapter with a quick refresher of what an exponent is.

Credit Risk Evaluation of Online Supply Chain Finance Based on Third-party B2B E-commerce Platform: an Exploratory Research Based on China s Practice

Contention-Free Periodic Message Scheduler Medium Access Control in Wireless Sensor / Actuator Networks

OPTIMAL KNOWLEDGE FLOW ON THE INTERNET

Swarm Based Truck-Shovel Dispatching System in Open Pit Mine Operations

CREATE SHAPE VISUALIZE

Chapter Eight. f : R R

FUZZY PERT FOR PROJECT MANAGEMENT

Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology

Optimal replacement and overhaul decisions with imperfect maintenance and warranty contracts

The simple linear Regression Model

Credibility Premium Calculation in Motor Third-Party Liability Insurance

3.6. Metal-Semiconductor Field Effect Transistor (MESFETs)

Appendix D: Completing the Square and the Quadratic Formula. In Appendix A, two special cases of expanding brackets were considered:

Classic Problems at a Glance using the TVM Solver

GRADUATION PROJECT REPORT

Stock Index Modeling using EDA based Local Linear Wavelet Neural Network

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki

DISTANCE MEASURE FOR ORDINAL DATA *

An Integrated Honeypot Framework for Proactive Detection, Characterization and Redirection of DDoS Attacks at ISP level

Generalized Difference Sequence Space On Seminormed Space By Orlicz Function

Co-author: Jakub Mikolášek Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague.

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

Bishaash. o k j. k k k k k j. k k. k k k e j k k k j k k k j. - one's ask - ing if I know the spell - ing of "Help"...

An IMM Algorithm for Tracking Maneuvering Vehicles in an Adaptive Cruise Control Environment

MATHEMATICS FOR ENGINEERING BASIC ALGEBRA

Numerical Methods with MS Excel

Master Thesis Mathematical Modeling and Simulation On Fuzzy linear programming problems solved with Fuzzy decisive set method

n Using the formula we get a confidence interval of 80±1.64

1.- L a m e j o r o p c ió n e s c l o na r e l d i s co ( s e e x p li c a r á d es p u é s ).

Reasoning to Solve Equations and Inequalities

Optimal multi-degree reduction of Bézier curves with constraints of endpoints continuity

Lesson 2.1 Inductive Reasoning

Load and Resistance Factor Design (LRFD)

Proceeding of the 32nd International Conference on Computers & Industrial Engineering

Summation Notation The sum of the first n terms of a sequence is represented by the summation notation i the index of summation

Solution to Problem Set 1

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract

IMPLEMENTATION IN PUBLIC ADMINISTRATION OF MEXICO GOVERNMENT USING GAMES THEORY AND SOLVING WITH LINEAR PROGRAMMING

On Error Detection with Block Codes

Released Assessment Questions, 2015 QUESTIONS

Optimal Packetization Interval for VoIP Applications Over IEEE Networks

Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.

Public Auditing Based on Homomorphic Hash Function in

I n la n d N a v ig a t io n a co n t r ib u t io n t o eco n o m y su st a i n a b i l i t y

Comparing plans is now simple with metal plans. What Does it Mean to Have a 6-Tier Pharmacy Plan? Tie. Individual Health Insurance

SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0

Outline. Numerical Analysis Boundary Value Problems & PDE. Exam. Boundary Value Problems. Boundary Value Problems. Solution to BVProblems

Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), January Edition, 2011

THE MELLIN-BARNES TYPE CONTOUR INTEGRAL REPRESENTATION OF A NEW MITTAG-LEFFLER TYPE E-FUNCTION

Green Master based on MapReduce Cluster

Exam FM/2 Interest Theory Formulas

Security Analysis of RAPP: An RFID Authentication Protocol based on Permutation

Experiment 6: Friction

Integrating Production Scheduling and Maintenance: Practical Implications

THE well established 80/20 rule for client-server versus

A. Description: A simple queueing system is shown in Fig Customers arrive randomly at an average rate of

of the relationship between time and the value of money.

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R =

The Casino Experience. Let us entertain you

JGPLI-94ZWQSVE ACILIM TURKCE DERS KITAB 1 STUDY TURKISH TAMU. JGPLI-94ZWQSVE PDF 103 Pages KB 10 Apr, 2014

Chapter 13 Volumetric analysis (acid base titrations)

How To Make A Supply Chain System Work

Capacitated Production Planning and Inventory Control when Demand is Unpredictable for Most Items: The No B/C Strategy

Analyzing and Evaluating Query Reformulation Strategies in Web Search Logs

Analysis of Two-Echelon Perishable Inventory System with Direct and Retrial demands

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :

n. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom.

m n Use technology to discover the rules for forms such as a a, various integer values of m and n and a fixed integer value a.

RUSSIAN ROULETTE AND PARTICLE SPLITTING

FINANCIAL MATHEMATICS 12 MARCH 2014

Simple Linear Regression

CIS603 - Artificial Intelligence. Logistic regression. (some material adopted from notes by M. Hauskrecht) CIS603 - AI. Supervised learning

Transcription:

ISSN 330-74 U 637.5.033.00 A MOEL WITH STORAGE LIMITATION AN SIMULATE EMAN AS FRESH MEAT INVENTORY MANAGEMENT SUPPORT Gord ukć ),. ukć ), M. Ser ) Orgl cetfc pper SUMMARY A mportt pect of retl outlet wc offer dfferet kd of fre met te creful plg of vetory. I t cotet, vetory mgemet clude eurg te reured utty of met, torg t deute wy, d lo mmzg te etup cot, oldg cot, ortge cot d pole loe tt mgt occur f te good rem uold. By devg d pplyg pproprte model compe c gfctly mprove ter deco mkg coected to rmozg te demd d dfferet vetory cot. T pper preet vetory model wt lmted torge pce, crcterzed y te etmte of met demd ed o multed vlue geerted y computer from et dtruto. If te determed utte eceed te vlle torge pce, te Lgrge multpler wc llow for multeou decree of te orglly etled vlue, could e ued te model. T evetully reult te optmum order utty of dfferet met product, well te octed totl torge cot. ey-word: vetory model wt torge lmtto, computer multed demd, fre met vetory, vetory cot, optmzto INTROUCTION Ivetory plg mportt ue te operto of retl outlet tt trde fre met product. Te m of vetory mgemet t ce to eure te reured utte of dfferet kd of fre met, wt mmum etup cot d oldg cot. To ceve t, t ecery to udertd detl ll te cot relted to vetory. fferet cot curred te proce of purcg met product ll elog to etup cot. Holdg cot clude ot oly te cot of deute torge of fre met product, ut lo te epedture rg from fud eg ted up vetory. I ddto to te pect tted ove, te mgemet eed to tke to ccout te loe rg from uffcet vetory,.e. ortge cot. I t cotet pecl prolem pper to e te lo of coumer cofdece. I order to mmze ter come, retl outlet eed to mke pproprte etmte of optmum order utty for dfferet kd of fre met, pyg prtculr tteto to te elf-lfe of uc product d te cot rg from product deterorto. I order to mprove te deco-mkg te dom of vetory mgemet, everl model ve ee developed. T pper wll preet te vetory model wt torge lmtto. I te ce of fre met retl outlet, torge lmtto determed y te cpcty of ter cold torge for uc product. Tkg to ccout te vlle torge, dly demd, etup cot, oldg cot d te pce reured for ter torge, te model yeld te optmum utty of dfferet kd of fre met to e eld vetory, d octed totl vetory cot. If t tur out tt te torge pce reured for keepg te determed met utte lrger t te vlle cold torge, we keep decreg multeouly te tlly determed vlue y cge Lgrge multpler utl torge lmtto tfed. MATERIAL AN METHOS ) P. Gord ukć; P. rko ukć - ABACUS Tuto, Reerc d Bue Coultcy, Moork 8, 3000 Ojek, Crot; ) Mte Ser, P. Studet - Jop Jurj Stromyer Uverty of Ojek, Fculty of Agrculture, Trg v. Trojtv 3, 3000 Ojek, Crot

Te propoed model t org te vetory model wt torge lmtto. A uc, t ed o utttve optmto metod. It pecfc feture te etmte of demd for met y me of computer multo. Gve tt te demd for met multed te model, t elog to te group of toctc model. Te multed demd vlue re geerted from et dtruto. Suc procedure gfctly ccelerted y ug deute computer d oftwre upport. Te pper wll prtculrly empze t pect of te model. I ddto, fter teoretcl eplcto of te model we preet mple emple order to e etter prctcl polte of t pplcto. RESULTS AN ISCUSSION Teoretcl feture of te model Te trtg umpto of te vetory model wt torge lmtto ould ve t let two dfferet kd of product to e tored lmted torge pce. It furtermore umed tt ec tem repleed tteouly. For te ke of mplcty, t c e umed tt etup cot rem te me regrdle of te utty ordered. I t ce, te vetory model c e preeted follow: mmze C,..., uject to ) S > 0, for ll Were Numer of tem Optmum order utty of te t tem Setup cot of te t tem emd rte per ute tme of te t tem Holdg cot per ute tme of te t tem Storge re reuremet per ute of te t tem S Mmum torge re I te tted model optmum vlue of oted y mmzg te ojectve fucto, were te cotrt reflect te codto tt totl ordered utty of ll kd of product ould ft to vlle torge pce. Here t umed tt te optmum utty of ec product mut e ove zero. To determe te optmum order utty ccomped y mmum cot, te ojectve fucto eed to e derved y ec of te vrle, d te te prtl dervtve oted t wy eed to e eulzed to zero: C 0 I t ce we get te followg formul for clcultg te optmum order utty of t tem: ) If te determed um of optmum utte of ll tem eceed te vlle torge pce, t ecery to etl ew optmum vlue. Wt t m te Lgrge fucto defed follow:

3 S ),,..., L I te tted epreo repreet te Lgrge multpler woe vlue elow zero. A te precedg ce, te optmum vlue of 0 d L d c e foud y eutg te oted prtl dervtve to zero: S L 0 Te ecod euto mple tt te um of optmum order utte mut e eul wt vlle torge pce, were te frt euto yeld te formul for clcultg te optmum order utty of t tem: ) I determg te optmum utte Β ) ), ) ) ) f te Lgrge multpler decree utl te vlle torge pce ued to te full. A pecfc feture of te decred model multo of demd for met o te of et dtruto woe prmeter re d. Te et dtruto te prolty dety fucto:,, 0 >,. I te tted formul repreet te mmum, d te mmum rge lmt, were Β,) repreet te et fucto. Smulto of demd for met product c e ed o oter teoretcl dtruto well, ut crctertc of et dtruto jutfy t electo. Amog oter tg, deute determto of rge lmt for t dtruto prevet egtve demd vlue te multo proce. I ddto, t more lkely tt ger umer of coumer wll wt to uy mller utte of fre met, d mller umer wll ted to uy lrger utte. It terefore reltc to ume tt te demd for fre met doe ot follow te rule of oe of ymmetrc dtruto. Sce prmeter d llow te et dtruto to e defed potvely ymmetrcl, t tu tfe t codto of multo model well. I te multo proce, te frt tg to e determed for ec kd of met te mllet ) d te lrget ) dly utty demded. Suc etmte c e ed o te met le dt recorded gve tme tervl. Prmeter d re lo eed o te of t dt: ), ), Were Smple me, Smple vrce.

Smulted demd vlue re oted y foudg et dtruto vlue wt ccompyg prolte, determed y me of rdom umer geertor. Te vlue multed t wy re te ued to clculte te optmum order utte of fre met wc tfy te codto of torge lmtto. To ceve more ccurte emet of optmum utte d totl cot, t dvle to repet te multo procedure my tme pole. Tkg to ccout te cplte of tody' computer, t o prolem t ll. Wt t pper tryg to empze precely te eed to etl deute dte o fre met le, d to ue tte-of-te-rt formto d commucto tecologe order to mprove vetory mgemet. Reterto of te decred procedure yeld lrger umer of oluto. Optmum utte of met product to e eld vetory c te e determed me of ll te oluto oted t mer. A ypotetcl emple of devg d olvg te model Let t e umed tt we eed to tore of tree kd of fre met te cold torge pled for torg S40 coter wt cpcty of 0 kg ec. T me tt 3 coter. Let t furter e umed tt te etup cot mout to 0 MU moetry ut), 5 MU d 3 30 MU, were te dly oldg cot MU, 3 MU d 3 MU. O te of le dt, te mgemet etled te mout of mmum d mmum dly demd for met, well te vlue of me dly demd wt ccompyg vrce. Te dt re ow kg, d re ued to clculte te prmeter d tle ). Tle. Hypotetcl demd dt reured for clcultg te prmeter d Item Mmum Mmum Smple Smple demd demd me vrce 50 300 00 800.750 3.500 00 350 50 700.048 4.095 3 00 00 30 400.75.975 By me of computer multo wc reure deute oftwre, te vlue of dly demd for ll tree kd of met ve ee geerted from te et dtruto. For te frt kd of met we got te multed demd of 94.4 kg, for te ecod 4.73 kg, d for te trd 5.568 kg. Sce met tored coter wt 0 kg cpcty, dly demd for te frt kd of met 0, for te ecod, d for te trd dly demd 3 8 coter. Optmum order utty for te frt kd of met, clculted y me of formul ) 4.4, for te ecod 0.954, d for te trd 3.909 coter. Sce tee optmum order utte eceed te vlle pce cold torge y 7.005 coter, we eed to pply formul ). Te utte of vetory tt tfy te torge lmtto re oted for -0.85. For t vlue of Lgrge multpler te determed utte re.476, 0.04, d 3 7.485 coter. By ertg te ecery vlue to te ojectve fucto we c get totl vetory cot, wc t ce, mout to 83.963 MU. To mprove te emet of optmum order utty, te multo of dly demd vlue ould e crred out my tme pole. Repetg, te decred procedure wll produce dtruto of optmum utte for tree kd of met product. Ter me wll t ce repreet optmum order utty. Nturlly, y oluto repreetg te umer of coter epreed o-teger ould e rouded d epreed wole umer. CONCLUSION Fre met vetory mgemet retl outlet very etve ue. Ideute emet of t vetory c reult gfct fcl loe. It terefore very mportt to mke correct emet of te vetory level tt would tfy te demd for fre met d mmze te etup cot d oldg cot. T pper preeted te vetory model wt torge lmtto ot teoretclly d troug emple. A mportt pot of referece t model te etmte of demd for fre met ed o computer multo. Te multo crred out te frmework 4

of et dtruto woe crctertc jutfy t uge te multo proce. Smlrly to y trct cotructo of relty, te preeted model cot fully elmte te loe coected wt vetory oldg, ut t c certly mprove te proce of vetory mgemet to gfct degree. REFERENCES. Aggou, L., Bekerouf, L., Tdj, L.: O Stoctc Ivetory Model wt eterortg Item, www.dw.com/getpf.p?do0.55/s067000334. Brkovć,. 997): Opercjk tržvj, Ekoomk fkultet Ojek, Ojek, 379-383. 3. Bet truto, ttp://www.tl.t.gov/dv898/dook/ed/ecto3/ed366.tm 4. Bo, C.P., Hum, W.H., Berm, H.Jr. 997): Qutttve Aly for Mgemet, Nt Edto, Irw/McGrw-Hll, Boto, 344-358. 5. Etrup, M.L. 005): Advced Plg Fre Food Idutre: Itegrtg Self Lfe to Producto Plg Cotruto to Mgemet Scece), Pyc-Verlg, Hedelerg, 58-63. 6. Hller, F.S., Leerm, G.J. 005): Itroducto to Operto Reerc, Egt Edto, McGrw-Hll, New York, 833-889. 7. Lev, R., Pál, M., Roudy, R., Smoy,.B. 005): Appromto Algortm for Stoctc Ivetory Cotrol Model. I: Iteger Progrmmg d Comtorl Optmzto: Proceedg of t Itertol IPCO Coferece Lecture Note Computer Scece), Berl, Germy, 306-30. 8. Lew, C.. 998): emd Forectg d Ivetory Cotrol: A Computer Aded Lerg Approc Olver Wgt Mufcturg), Jo Wley & So, Ic., New York, 8-90. 9. Oowk N.R.: Ivetory Vluto eco d Strtegy Aly, ttp://www.et.odk.edu/~edept/emc/oowk-te.pdf 0. Ro, S.M. 003): Itroducto to Prolty Model, Egt Edto, Acdemc Pre, S ego, 657-658.. T, H.A. 995): Operto Reerc - A Itroducto, Fft Edto, Pretce Hll Itertol, Sgpore, 49-494.. Tro, M., Helm, C.M.: Te Effect of Eprto te o te Purcg Bevor for Grocery Store Perle, ttp://www.commerce.vrg.edu/fcultyreerc/reerc/pper/helm_perle_trdro ud.pdf Receved o 4 Aprl 007; ccepted o My 007) 5