The Application of Multi Shifts and Break Windows in Employees Scheduling

Size: px
Start display at page:

Download "The Application of Multi Shifts and Break Windows in Employees Scheduling"

Transcription

1 The Applicaion of Muli Shifs and Brea Windows in Employees Scheduling Evy Herowai Indusrial Engineering Deparmen, Universiy of Surabaya, Indonesia Absrac. One mehod for increasing company s performance excellence is by opimizing he uilizaion of company s human resources hrough applying a good scheduling sysem. A company wih a very long service hours usually use muliple shifs. To mainain he cusomer s service wihin res ime, he employees should no res simulaneously in a ime inerval called brea window..ineger Linear Programming model for opimal shif scheduling wih muliple shifs and brea windows is used o deermine he opimal number of employees needed in every shif and brea assignmen. These opimal numbers are used o schedule he company s employees hrough 6 woring days wih wellplanned brea assignmens. Compared wih he radiional sysem, he proposed sysem and model shows several advanages, such as scheduling process required by he new model is faser and easier, employee s defici and surplus are well disribued, he brea assignmens are arranged while mainaining he cusomer s services. Knowing he opimal number of employees will help he manager in developing recruimen plan. Keywords: Employees scheduling, Brea windows, Muli shifs, Ineger Programming 1. Inroducion The shifs scheduling problem arises in a variey of service organizaions such as elephone companies, hospials, deparmen sores and involved scheduling he employees o mee he demand ha changes over days and hours. The company usually serves he cusomer for more han 8 hours per day (he normal woring hours) bu here is a flucuaion in company s service aciviies. This flucuaion happens in weely order, daily and hours, where here are busy days and hours and on he oher hand here are an idle in days and hours. The company can increase cusomer saisfacion by reducing cusomer s waiing ime. There are several alernaives ha can be used o reduce waiing ime. The firs alernaive is by increasing he number of employees bu his alernaive will cause grea increasing of labor cos and high percenage of employee idle ime (especially in idle days and hours). Oher alernaive o overcome his problem is shif allocaion mehod wih good scheduling procedure so ha he employee uilizaion is no oo low in idle ime and no oo high in busy days and hours. This problem will be more complicaed during employee's brea ime because he number of employee is no enough o serve he cusomer. Therefore, he company should arrange he brea ime so ha he employees can enjoy heir res wihou disurbing he service o cusomer. I can be achieved by scheduling he employee's brea ime no simulaneously in Brea Windows. Brea windows are ime inervals wihin which employees mus sar and complee heir breas. The problem is geing complicaed for company who serves 7 days a wee because almos all of he privae companies in Indonesia operae 6 days a wee. The ineger programming model for shif scheduling was originally suggesed by Danzig [1954], bu i does no consider cusomer service in brea ime. The shif scheduling problem wih muliple brea windows was sudied by many researchers, among hem is Ayin T [1996, 1998]. Worforce allocaion in cyclical scheduling problems was suggesed by Baer [1976] and he our scheduling problem involved boh shif scheduling for wor days in a wee and days-off scheduling discuss abou how o schedule he employees who wors 5 days a wee. This concep needs o be modified due he 6 woring days implemened in Indonesia. This paper used ineger programming model formulaed by Ayin T (1996, 1998) combined wih he modificaion of cyclical scheduling inroduced by Baer. In he nex secion, we discuss an example illusraing he proposed approach and formulaion for a specific shif scheduling problem and he analysis of scheduling resuls obained. 2. Problem Formulaion To illusrae our approach, consider a real case involving hree shifs and an hour lunch brea. The company provided seven service days a wee from o Thus, here are 25 ime-inervals a day. The lengh of each inerval is 30 minues. The shifs and brea windows are shown in Table 1 and Figure 1. Table 1. The shifs and brea windows Woring hours Brea window Shif Shif Shif No res Proceedings of he Inernaional Conference on Compuer and Indusrial Managemen, ICIM, Ocober 29-30, 2005, Bango, Thailand 13.1

2 The busle of he company is very flucuaing. Many cusomers come in he firs wee of he monh, during Saurday and Sunday, and during Bu, during only a few cusomers come. According o Figure 1, Shif 3 is no a pleasan shif for employees. Therefore, he company should give exra ransporaion cos for hem. This paper will focus on developing scheduling model which considers breaing ime of he employees and busy/idle ime of he company. Shif 1 Shif 2 Shif Figure 1. The shifs and brea windows The seps used o conduc his research are described below: 1. Developing a mahemaical model o deermine he number of employees in each ime inerval in planning period by considering busy/idle period and employees brea ime. 2. Deermining a scheduling algorihm for he employees based on: a. The opimal number of employees needed for each shif. b. The scheduling rule which appropriae o company s condiion. c. The number of employees available in he company. 3. Creaing compuer sofware o suppor scheduling process. 3. Model Developmen 3.1 Mahemaical Model in deermining Opimal numbers of employees Ineger Programming Model Minimize: Subjec o : z = C X (1) K a X M ( 1) K TM 1 TM M BM M for all T (2) b X = 0, for all K (3) X, M 0 and Ineger for all and (4) This model is solved wih Branch and Bound mehod using Lingo Opimizaion Sofware. The opimizaion resuls are he opimal numbers of employees in shif 1, 2 and 3 wih heir brea ime schedule. The oal number of employees needed per day is deermined from he oal number of employees needed from shif 1, 2 and 3. This process is carried ou from Monday o Sunday. 3.2 Employees scheduling 6 wor days during 7 service days a wee. To be able o serve he cusomer coninuously, he schedule should be arranged so ha he employees wih 6 woring days a wee can give 7 service days a wee. Firsly we should deermine he algorihm rule hen consruc he scheduling algorihm in accordance wih his rule. The employees can reques in which day hey can be off from wor (one day of 7 woring days). Bu here is a limiaion so ha he scheduling sysem could be well implemened The Employee s Scheduling rule 1. Each employee is scheduled o wor 6 days a wee wih one day-off deermined by he company. 2. The employees assignmen o shif 1 and shif 2 is leveled. 3. The employee's assignmen o shif 3 is made as minimal as possible. 4. The surplus number of employees from he opimal requiremen is allocaed o each day, and allocaed again in every shif along wih his brea regulaion. The shif allocaion prioriy is shif 2, shif 1, and shif The employees can pu forward reques on heir schedule The Scheduling algorihm Based on he scheduling rule, here are 2 sages of he scheduling algorihm, ha is: Sage 1: The day-off deerminaion. a. Each employee is scheduled every day, from Sunday, Saurday, Friday up o Monday, excluding he day having he smalles number of employees demand. If here are wo days or more having he same smalles demand of employees, choose one as he day-off. b. Updae he employees demand/day hen bac o sep a. The resul from his Off-deerminaion sage is he employees day-off in one wee. Sage 2 : The shif and brea deerminaion The employee is assigned in daily basis from Sunday, Saurday, Friday o Monday. The employees opimal requiremen in each shif is allocaed firs hen he surplus number of employees is leveled o all shifs by considering prioriy as follows: shif 2, shif 1, and shif 3. The same concep as employee s allocaion, he brea ime is also allocaed firs in accordance o he opimal value of is number of employees. Then, he surplus number of employees is leveled along wih his brea period The Employee s reques 1. The maximum reques per person per wee is only 2 and i mus be submied before he schedule release. The head of deparmen will decide o approve or rejec he reques. 2. The opion for firs reques can vary no o be scheduled o shif 1, no o be scheduled o shif 2, no o be scheduled o shif 3, or no o be scheduled in a cerain day. Special Issue of he Inernaional Journal of he Compuer, he Inerne and Managemen, Vol. 13 No.SP2, Ocober,

3 B U S T L E C L A S IF IC A T IO N M IN E M P L O Y E E S N E E D E D F O R E V E R Y C L A S IF IC A T IO N B R E A K W IN D O W C U S T O M E R S A R R IV A L D E T E R M IN E M IN E M P L O Y E E S N E E D E D IN E V E R Y T IM E IN T E R V A L S N E E D O F E M P L O Y E E S E V E R Y 3 0 M IN D E T E R M IN E T H E O P T IM A L N U M B E R S O F E M P L O Y E E S O P T IM A L N U M B E R S O F E M P L O Y E E S / D A Y S / S H IF T /R E S T E M P L O Y E E S A V A IL A B L E S C H E D U L IN G R U L E S P S S L IN G O V IS U A L B A S IC S C H E D U L E F O R O N E E M P L O Y E E IN O N E W E E K E M P L O Y E E S S R E Q U E S T S C H E D U L E T H E E M P L O Y E E S M O N, T U E, W E D, T H U, F R I, S A T, S U N F O R i h W E E K S C H E D U L E F O R A L L E M P L O Y E E S IN O N E W E E K S C H E D U L IN G A L G O R IT H M Figure 2. The model and scheduling sysem 3. The second reques opion can vary form no o be scheduled o shif 1, no o be scheduled o shif 2, or no o be scheduled o shif 3 Leave reques by employees is reaed as heir opion o no o be assigned on ha day. The ouline of he model and scheduling sysem for he employee is shown in Figure The Analysis of Scheduling Resuls The Implemenaion of model and sofware o ge he surplus/defici. The surplus/defici of employees needed for all deparmens of he company is shown in Table 2 and he allocaion of surplus/defici for firs wee is shown in Table 3. Table 2. The recapiulaion of employee surplus/defici Wee-i Defici / Surplus (-) Supermare Dep Sore Bazaar Cashier Table 3. The allocaion of employee surplus /defici Day Deparmen Mon Tues Wed. ThursFri Sa Sun Supermare Dep. Sore Bazar Cashier I is shown in Table 2 ha he number of supermare s employees is enough o be scheduled in a normal woring day (negaive means employee's surplus). Bazaar has he bigges employees surplus afer Deparmen Sore. The number of cashiers is no enough he firs wee in every monh. Informaion abou employees defici or surplus can be considered in employee recruimen plan. The ineger programming model can be used o deermine he opimal numbers of employees required, bu in pracice, all of he available employees have o be assigned so ha he opimal soluions for ineger programming is almos impossible o be applied. In he proposed scheduling sysem, he defici or surplus will be allocaed equally as shown in Table 3. Five woring days employees surpluses for supermare are allocaed equally on Wednesday, Thursday, Friday, Saurday and Sunday. Whereas in Deparmen sore, 50 woring days employees surplus for his deparmen are leveled by allocaing 7 more people on he Monday o Saurday and 8 more people on Sunday. Only for cashiers, wo persons lac are allocaed on he Monday and Tuesday Implemenaion of model and Sofware o schedule he employees The number of employees assigned in he real schedule and he new schedule for deparmen sore is shown in Table 4. Table 4. The number of deparmen sore s employees, Monday Augus Real New Opimal Shif Shif Shif The schedule resuled by new scheduling sysem should be in he form of: Proceedings of he Inernaional Conference on Compuer and Indusrial Managemen, ICIM, Ocober 29-30, 2005, Bango, Thailand 13.3

4 1. The overall employee s schedule in one wee. 2. The employee's schedule of a deparmen in one day. 3. The employee's schedule of a deparmen in one shif in a day. 4. An employee s schedule in one wee. The real schedule did no arrange he employee s brea ime. I caused employee's empiness in brea ime. The gap analysis for he deparmen sore schedule on Monday, Augus is shown in Table 5 and Figure 4. From his gap, i is seen ha he new schedule is beer han he real schedule. The example of he real employee assignmen is shown in Figure 3. Table 5 The gap analysis Descripion Real Opimal Model Gap-Real Gap-Model The employees' surplus in his deparmen is very large, bu Figure 4 showed ha real schedule sill has ime inerval wih lac of employees ( ). The bigges employee surplus is in In he new schedule, he lac of employee did no happen in any inerval and he employee surplus in each inerval of ime is well allocaed. In real scheduling, he employee's res was no arranged, on he oher hand he new schedule considered he employees breaing ime. Therefore he new model and scheduling sysem is beer han real scheduling used by he company. Ja m Shif I Shif II Shif III G A P Figure 3. The shif and he number of employees assigned D E P A R T M E N T S T O R E G A P T IM E IN T E R V A L R e a l M o d e l Figure 4 The Deparmen sore gap analysis0 Monday, Augus Model compaibiliy wih he Real Condiion. Model compaibiliy wih he real condiion depends on he parameer used in modeling. If here are oo many idle in cerain ime and on he oher hand, oo many deficis in anoher inerval, his is an indicaion ha he parameer model mus be observed again. The condiion changes resuling in he change of he parameers model are able o be accommodaed by he sysem and Sofware. So ha his sysem and sofware is sill be applied by changing he defaul of he parameers model. 4. Conclusion The Conclusions from he whole research are as follows: 1. Scheduling wih he proposed model and sysem is beer han he real scheduling, ha is: a. Real schedule does no arrange he employee's breaing ime. b. In idle ime, , oo many employees scheduled in real schedule caused a very high cos o he company. 2. The new model and scheduling sysem is suiable for company using muliple shifs and having he paern 6 woring days for 7 service days a wee. In new sysem, he employee's brea is no carried ou simulaneously in brea window so as he employee's empiness in he breaing ime will no be available. Therefore his scheduling model can fulfill he opimal number of employees assigned in he busy/idle period and he employee's righ o mae use of his brea. 3. The new model and scheduling sysem suppored by Sofware faciliaes he human resources deparmen o schedule he available employees. The sofware made have several advanages: a. Faciliaes employees scheduling. b. Flexible in accommodaing he employee's reques for day-off in cerain day, or being no scheduled o cerain shif in cerain day. c. Flexible in accommodaing various model parameer change, including he change in he period of planning, he lengh of he res, woring hours in one day, shif, he parameer from ineger programming consrain. d. Knowing he opimal number of employees needed for a Deparmen could suppor a beer recruimen planning. e. The employee s defici/surplus is leveled each day, aferwards is leveled again during every shif so as o he dump/he lac of employee will no happened oo wors. Noaions used: K T C The se of all shif The se of planning periods ha he schedule covers The cos of assigning an employee in shif, K b The number of employees needed in every period. a is equal o one if period is woring period for shif Special Issue of he Inernaional Journal of he Compuer, he Inerne and Managemen, Vol. 13 No.SP2, Ocober,

5 X is equal o zero if period is no woring period for shif An ineger decision variable for he number of employees in shif M The number of employees in shif and sar his lunch brea in period BM The se of planning periods where employee in shif TM can sar for lunch brea. The se of shif where period is saring period for lunch brea. 5. References Ayin,T. (1996). Opimal Shif Scheduling wih Muliple Brea Windows. Managemen Science, Insiue for Operaions research and The Managemen Science, 42, 4, Ayin, T (1998). A Composie Branch and Cu Algorihm for Opimal Shif Scheduling wih Muliple Breas and Brea Windows. Journal of he Operaional Research Sociey, 49, 6, Baer, K.R.(1976), Worforce Allocaion in Cyclical Scheduling Problems. Operaion Research Quarerly, 27, Danzig, G.B.(1954), A commen on Edie s Traffic Delays a Toll Boohs. Operaions Research, 2, 3, Proceedings of he Inernaional Conference on Compuer and Indusrial Managemen, ICIM, Ocober 29-30, 2005, Bango, Thailand 13.5

Single-machine Scheduling with Periodic Maintenance and both Preemptive and. Non-preemptive jobs in Remanufacturing System 1

Single-machine Scheduling with Periodic Maintenance and both Preemptive and. Non-preemptive jobs in Remanufacturing System 1 Absrac number: 05-0407 Single-machine Scheduling wih Periodic Mainenance and boh Preempive and Non-preempive jobs in Remanufacuring Sysem Liu Biyu hen Weida (School of Economics and Managemen Souheas Universiy

More information

Multiprocessor Systems-on-Chips

Multiprocessor Systems-on-Chips Par of: Muliprocessor Sysems-on-Chips Edied by: Ahmed Amine Jerraya and Wayne Wolf Morgan Kaufmann Publishers, 2005 2 Modeling Shared Resources Conex swiching implies overhead. On a processing elemen,

More information

Performance Center Overview. Performance Center Overview 1

Performance Center Overview. Performance Center Overview 1 Performance Cener Overview Performance Cener Overview 1 ODJFS Performance Cener ce Cener New Performance Cener Model Performance Cener Projec Meeings Performance Cener Execuive Meeings Performance Cener

More information

DETERMINISTIC INVENTORY MODEL FOR ITEMS WITH TIME VARYING DEMAND, WEIBULL DISTRIBUTION DETERIORATION AND SHORTAGES KUN-SHAN WU

DETERMINISTIC INVENTORY MODEL FOR ITEMS WITH TIME VARYING DEMAND, WEIBULL DISTRIBUTION DETERIORATION AND SHORTAGES KUN-SHAN WU Yugoslav Journal of Operaions Research 2 (22), Number, 6-7 DEERMINISIC INVENORY MODEL FOR IEMS WIH IME VARYING DEMAND, WEIBULL DISRIBUION DEERIORAION AND SHORAGES KUN-SHAN WU Deparmen of Bussines Adminisraion

More information

Constant Data Length Retrieval for Video Servers with Variable Bit Rate Streams

Constant Data Length Retrieval for Video Servers with Variable Bit Rate Streams IEEE Inernaional Conference on Mulimedia Compuing & Sysems, June 17-3, 1996, in Hiroshima, Japan, p. 151-155 Consan Lengh Rerieval for Video Servers wih Variable Bi Rae Sreams Erns Biersack, Frédéric Thiesse,

More information

TSG-RAN Working Group 1 (Radio Layer 1) meeting #3 Nynashamn, Sweden 22 nd 26 th March 1999

TSG-RAN Working Group 1 (Radio Layer 1) meeting #3 Nynashamn, Sweden 22 nd 26 th March 1999 TSG-RAN Working Group 1 (Radio Layer 1) meeing #3 Nynashamn, Sweden 22 nd 26 h March 1999 RAN TSGW1#3(99)196 Agenda Iem: 9.1 Source: Tile: Documen for: Moorola Macro-diversiy for he PRACH Discussion/Decision

More information

Strategic Optimization of a Transportation Distribution Network

Strategic Optimization of a Transportation Distribution Network Sraegic Opimizaion of a Transporaion Disribuion Nework K. John Sophabmixay, Sco J. Mason, Manuel D. Rossei Deparmen of Indusrial Engineering Universiy of Arkansas 4207 Bell Engineering Cener Fayeeville,

More information

Research on Inventory Sharing and Pricing Strategy of Multichannel Retailer with Channel Preference in Internet Environment

Research on Inventory Sharing and Pricing Strategy of Multichannel Retailer with Channel Preference in Internet Environment Vol. 7, No. 6 (04), pp. 365-374 hp://dx.doi.org/0.457/ijhi.04.7.6.3 Research on Invenory Sharing and Pricing Sraegy of Mulichannel Reailer wih Channel Preference in Inerne Environmen Hanzong Li College

More information

USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES

USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES Mehme Nuri GÖMLEKSİZ Absrac Using educaion echnology in classes helps eachers realize a beer and more effecive learning. In his sudy 150 English eachers were

More information

Analysis of Pricing and Efficiency Control Strategy between Internet Retailer and Conventional Retailer

Analysis of Pricing and Efficiency Control Strategy between Internet Retailer and Conventional Retailer Recen Advances in Business Managemen and Markeing Analysis of Pricing and Efficiency Conrol Sraegy beween Inerne Reailer and Convenional Reailer HYUG RAE CHO 1, SUG MOO BAE and JOG HU PARK 3 Deparmen of

More information

PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE

PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE Profi Tes Modelling in Life Assurance Using Spreadshees PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE Erik Alm Peer Millingon 2004 Profi Tes Modelling in Life Assurance Using Spreadshees

More information

Chapter 1.6 Financial Management

Chapter 1.6 Financial Management Chaper 1.6 Financial Managemen Par I: Objecive ype quesions and answers 1. Simple pay back period is equal o: a) Raio of Firs cos/ne yearly savings b) Raio of Annual gross cash flow/capial cos n c) = (1

More information

Chapter 8: Regression with Lagged Explanatory Variables

Chapter 8: Regression with Lagged Explanatory Variables Chaper 8: Regression wih Lagged Explanaory Variables Time series daa: Y for =1,..,T End goal: Regression model relaing a dependen variable o explanaory variables. Wih ime series new issues arise: 1. One

More information

Information Systems for Business Integration: ERP Systems

Information Systems for Business Integration: ERP Systems Informaion Sysems for Business Inegraion: ERP Sysems (December 3, 2012) BUS3500 - Abdou Illia, Fall 2012 1 LEARNING GOALS Explain he difference beween horizonal and verical business inegraion. Describe

More information

OPERATION MANUAL. Indoor unit for air to water heat pump system and options EKHBRD011ABV1 EKHBRD014ABV1 EKHBRD016ABV1

OPERATION MANUAL. Indoor unit for air to water heat pump system and options EKHBRD011ABV1 EKHBRD014ABV1 EKHBRD016ABV1 OPERAION MANUAL Indoor uni for air o waer hea pump sysem and opions EKHBRD011ABV1 EKHBRD014ABV1 EKHBRD016ABV1 EKHBRD011ABY1 EKHBRD014ABY1 EKHBRD016ABY1 EKHBRD011ACV1 EKHBRD014ACV1 EKHBRD016ACV1 EKHBRD011ACY1

More information

Task is a schedulable entity, i.e., a thread

Task is a schedulable entity, i.e., a thread Real-Time Scheduling Sysem Model Task is a schedulable eniy, i.e., a hread Time consrains of periodic ask T: - s: saring poin - e: processing ime of T - d: deadline of T - p: period of T Periodic ask T

More information

Caring for trees and your service

Caring for trees and your service Caring for rees and your service Line clearing helps preven ouages FPL is commied o delivering safe, reliable elecric service o our cusomers. Trees, especially palm rees, can inerfere wih power lines and

More information

The Grantor Retained Annuity Trust (GRAT)

The Grantor Retained Annuity Trust (GRAT) WEALTH ADVISORY Esae Planning Sraegies for closely-held, family businesses The Granor Reained Annuiy Trus (GRAT) An efficien wealh ransfer sraegy, paricularly in a low ineres rae environmen Family business

More information

Analogue and Digital Signal Processing. First Term Third Year CS Engineering By Dr Mukhtiar Ali Unar

Analogue and Digital Signal Processing. First Term Third Year CS Engineering By Dr Mukhtiar Ali Unar Analogue and Digial Signal Processing Firs Term Third Year CS Engineering By Dr Mukhiar Ali Unar Recommended Books Haykin S. and Van Veen B.; Signals and Sysems, John Wiley& Sons Inc. ISBN: 0-7-380-7 Ifeachor

More information

Making a Faster Cryptanalytic Time-Memory Trade-Off

Making a Faster Cryptanalytic Time-Memory Trade-Off Making a Faser Crypanalyic Time-Memory Trade-Off Philippe Oechslin Laboraoire de Securié e de Crypographie (LASEC) Ecole Polyechnique Fédérale de Lausanne Faculé I&C, 1015 Lausanne, Swizerland philippe.oechslin@epfl.ch

More information

Modeling a distribution of mortgage credit losses Petr Gapko 1, Martin Šmíd 2

Modeling a distribution of mortgage credit losses Petr Gapko 1, Martin Šmíd 2 Modeling a disribuion of morgage credi losses Per Gapko 1, Marin Šmíd 2 1 Inroducion Absrac. One of he bigges risks arising from financial operaions is he risk of counerpary defaul, commonly known as a

More information

Nikkei Stock Average Volatility Index Real-time Version Index Guidebook

Nikkei Stock Average Volatility Index Real-time Version Index Guidebook Nikkei Sock Average Volailiy Index Real-ime Version Index Guidebook Nikkei Inc. Wih he modificaion of he mehodology of he Nikkei Sock Average Volailiy Index as Nikkei Inc. (Nikkei) sars calculaing and

More information

cooking trajectory boiling water B (t) microwave 0 2 4 6 8 101214161820 time t (mins)

cooking trajectory boiling water B (t) microwave 0 2 4 6 8 101214161820 time t (mins) Alligaor egg wih calculus We have a large alligaor egg jus ou of he fridge (1 ) which we need o hea o 9. Now here are wo accepable mehods for heaing alligaor eggs, one is o immerse hem in boiling waer

More information

Forecasting, Ordering and Stock- Holding for Erratic Demand

Forecasting, Ordering and Stock- Holding for Erratic Demand ISF 2002 23 rd o 26 h June 2002 Forecasing, Ordering and Sock- Holding for Erraic Demand Andrew Eaves Lancaser Universiy / Andalus Soluions Limied Inroducion Erraic and slow-moving demand Demand classificaion

More information

Term Structure of Prices of Asian Options

Term Structure of Prices of Asian Options Term Srucure of Prices of Asian Opions Jirô Akahori, Tsuomu Mikami, Kenji Yasuomi and Teruo Yokoa Dep. of Mahemaical Sciences, Risumeikan Universiy 1-1-1 Nojihigashi, Kusasu, Shiga 525-8577, Japan E-mail:

More information

Activity-Based Scheduling of IT Changes

Activity-Based Scheduling of IT Changes Aciviy-Based Scheduling of IT Changes David Trasour, Maher Rahmoun Claudio Barolini Trused Sysems Laboraory HP Laboraories Brisol HPL-2007-03 July 3, 2007* ITIL, change managemen, scheduling Change managemen

More information

Hotel Room Demand Forecasting via Observed Reservation Information

Hotel Room Demand Forecasting via Observed Reservation Information Proceedings of he Asia Pacific Indusrial Engineering & Managemen Sysems Conference 0 V. Kachivichyanuul, H.T. Luong, and R. Piaaso Eds. Hoel Room Demand Forecasing via Observed Reservaion Informaion aragain

More information

CAREER MAP HOME HEALTH AIDE

CAREER MAP HOME HEALTH AIDE CAREER MAP HOME HEALTH AIDE CAREER MAP HOME HEALTH AIDE Home healh aides are one of he fases growing jobs in New York Ciy. Wih more educaion, home healh aides can move ino many oher ypes of jobs in healh

More information

Distributing Human Resources among Software Development Projects 1

Distributing Human Resources among Software Development Projects 1 Disribuing Human Resources among Sofware Developmen Proecs Macario Polo, María Dolores Maeos, Mario Piaini and rancisco Ruiz Summary This paper presens a mehod for esimaing he disribuion of human resources

More information

Statistical Analysis with Little s Law. Supplementary Material: More on the Call Center Data. by Song-Hee Kim and Ward Whitt

Statistical Analysis with Little s Law. Supplementary Material: More on the Call Center Data. by Song-Hee Kim and Ward Whitt Saisical Analysis wih Lile s Law Supplemenary Maerial: More on he Call Cener Daa by Song-Hee Kim and Ward Whi Deparmen of Indusrial Engineering and Operaions Research Columbia Universiy, New York, NY 17-99

More information

Information Theoretic Evaluation of Change Prediction Models for Large-Scale Software

Information Theoretic Evaluation of Change Prediction Models for Large-Scale Software Informaion Theoreic Evaluaion of Change Predicion Models for Large-Scale Sofware Mina Askari School of Compuer Science Universiy of Waerloo Waerloo, Canada maskari@uwaerloo.ca Ric Hol School of Compuer

More information

A Decision-Making Tool for Home Health Care Nurses' Planning

A Decision-Making Tool for Home Health Care Nurses' Planning A Decision-Maing Tool for Home Healh Care Nurses' Planning Rm Ben Bachouch Universié de Lon, LIESP, INSA-Lon URAII, INSAT, Tunis rm.ben-bachouch@insa-lon.fr Alain Guine Universié de Lon, LIESP, INSA-Lon

More information

policies are investigated through the entire product life cycle of a remanufacturable product. Benefiting from the MDP analysis, the optimal or

policies are investigated through the entire product life cycle of a remanufacturable product. Benefiting from the MDP analysis, the optimal or ABSTRACT AHISKA, SEMRA SEBNEM. Invenory Opimizaion in a One Produc Recoverable Manufacuring Sysem. (Under he direcion of Dr. Russell E. King and Dr. Thom J. Hodgson.) Environmenal regulaions or he necessiy

More information

GoRA. For more information on genetics and on Rheumatoid Arthritis: Genetics of Rheumatoid Arthritis. Published work referred to in the results:

GoRA. For more information on genetics and on Rheumatoid Arthritis: Genetics of Rheumatoid Arthritis. Published work referred to in the results: For more informaion on geneics and on Rheumaoid Arhriis: Published work referred o in he resuls: The geneics revoluion and he assaul on rheumaoid arhriis. A review by Michael Seldin, Crisopher Amos, Ryk

More information

Identify and ranking the factors that influence establishment of total quality management system in Payame Noor University of Lordegan

Identify and ranking the factors that influence establishment of total quality management system in Payame Noor University of Lordegan Idenify and ranking he facors ha influence esablishmen of oal qualiy sysem in Payame Noor Universiy of Lordegan Farhad Farhadi MA Suden, Deparmen of Managemen, Najafabad Branch, Islamic Azad Universiy,

More information

Smooth Priorities for Multi-Product Inventory Control

Smooth Priorities for Multi-Product Inventory Control Smooh rioriies for Muli-roduc Invenory Conrol Francisco José.A.V. Mendonça*. Carlos F. Bispo** *Insiuo Superior Técnico - Universidade Técnica de Lisboa (email:favm@mega.is.ul.p) ** Insiuo de Sisemas e

More information

The naive method discussed in Lecture 1 uses the most recent observations to forecast future values. That is, Y ˆ t + 1

The naive method discussed in Lecture 1 uses the most recent observations to forecast future values. That is, Y ˆ t + 1 Business Condiions & Forecasing Exponenial Smoohing LECTURE 2 MOVING AVERAGES AND EXPONENTIAL SMOOTHING OVERVIEW This lecure inroduces ime-series smoohing forecasing mehods. Various models are discussed,

More information

Academic Advising. Ultimately the college experience you build is your responsibility. Office: Phone: Web: Advising Hours:

Academic Advising. Ultimately the college experience you build is your responsibility. Office: Phone: Web: Advising Hours: C C M T a s h g i e H w e N o k o o b e id u G n io a r is g e R d n a g in is v d A, n io a n ie r Suden O ! C C M T o e m o c l We y. ne ur jo l na io a uc ed ur yo ou gh ou hr o be used ed nd e in is

More information

Acceleration Lab Teacher s Guide

Acceleration Lab Teacher s Guide Acceleraion Lab Teacher s Guide Objecives:. Use graphs of disance vs. ime and velociy vs. ime o find acceleraion of a oy car.. Observe he relaionship beween he angle of an inclined plane and he acceleraion

More information

Distributed Echo Cancellation in Multimedia Conferencing System

Distributed Echo Cancellation in Multimedia Conferencing System Disribued Echo Cancellaion in Mulimedia Conferencing Sysem Balan Sinniah 1, Sureswaran Ramadass 2 1 KDU College Sdn.Bhd, A Paramoun Corporaion Company, 32, Jalan Anson, 10400 Penang, Malaysia. sbalan@kdupg.edu.my

More information

Present Value Methodology

Present Value Methodology Presen Value Mehodology Econ 422 Invesmen, Capial & Finance Universiy of Washingon Eric Zivo Las updaed: April 11, 2010 Presen Value Concep Wealh in Fisher Model: W = Y 0 + Y 1 /(1+r) The consumer/producer

More information

TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS

TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS RICHARD J. POVINELLI AND XIN FENG Deparmen of Elecrical and Compuer Engineering Marquee Universiy, P.O.

More information

Task-Execution Scheduling Schemes for Network Measurement and Monitoring

Task-Execution Scheduling Schemes for Network Measurement and Monitoring Task-Execuion Scheduling Schemes for Nework Measuremen and Monioring Zhen Qin, Robero Rojas-Cessa, and Nirwan Ansari Deparmen of Elecrical and Compuer Engineering New Jersey Insiue of Technology Universiy

More information

Market Liquidity and the Impacts of the Computerized Trading System: Evidence from the Stock Exchange of Thailand

Market Liquidity and the Impacts of the Computerized Trading System: Evidence from the Stock Exchange of Thailand 36 Invesmen Managemen and Financial Innovaions, 4/4 Marke Liquidiy and he Impacs of he Compuerized Trading Sysem: Evidence from he Sock Exchange of Thailand Sorasar Sukcharoensin 1, Pariyada Srisopisawa,

More information

Model-Based Monitoring in Large-Scale Distributed Systems

Model-Based Monitoring in Large-Scale Distributed Systems Model-Based Monioring in Large-Scale Disribued Sysems Diploma Thesis Carsen Reimann Chemniz Universiy of Technology Faculy of Compuer Science Operaing Sysem Group Advisors: Prof. Dr. Winfried Kalfa Dr.

More information

Time Series Analysis Using SAS R Part I The Augmented Dickey-Fuller (ADF) Test

Time Series Analysis Using SAS R Part I The Augmented Dickey-Fuller (ADF) Test ABSTRACT Time Series Analysis Using SAS R Par I The Augmened Dickey-Fuller (ADF) Tes By Ismail E. Mohamed The purpose of his series of aricles is o discuss SAS programming echniques specifically designed

More information

Cloud Service Trust Model and Its Application Research Based on the Third Party Certification

Cloud Service Trust Model and Its Application Research Based on the Third Party Certification Inernaional Journal of u- and e- Service, Science and Technology, pp.259-268 hp://dx.doi.org/1.14257/ijuness.215.8.7.26 Cloud Service Trus Model and Is Applicaion Research Based on he Third Pary Cerificaion

More information

How To Optimize Time For A Service In 4G Nework

How To Optimize Time For A Service In 4G Nework Process Opimizaion Time for a Service in 4G Nework by SNMP Monioring and IAAS Cloud Compuing Yassine El Mahoi Laboraory of Compuer Science, Operaions Research and Applied Saisics. Téouan, Morocco Souad

More information

Morningstar Investor Return

Morningstar Investor Return Morningsar Invesor Reurn Morningsar Mehodology Paper Augus 31, 2010 2010 Morningsar, Inc. All righs reserved. The informaion in his documen is he propery of Morningsar, Inc. Reproducion or ranscripion

More information

Georgia State University CIS 8000 IT Project Management. Upon completion of the course, students should be able to:

Georgia State University CIS 8000 IT Project Management. Upon completion of the course, students should be able to: Georgia Sae Universiy CIS 8000 IT Projec Course Descripion This course examines he defining characerisics of IT projecs, especially involving he developmen of sofware inensive sysems, and inroduces he

More information

Permutations and Combinations

Permutations and Combinations Permuaions and Combinaions Combinaorics Copyrigh Sandards 006, Tes - ANSWERS Barry Mabillard. 0 www.mah0s.com 1. Deermine he middle erm in he expansion of ( a b) To ge he k-value for he middle erm, divide

More information

International Journal of Supply and Operations Management

International Journal of Supply and Operations Management Inernaional Journal of Supply and Operaions Managemen IJSOM May 05, Volume, Issue, pp 5-547 ISSN-Prin: 8-59 ISSN-Online: 8-55 wwwijsomcom An EPQ Model wih Increasing Demand and Demand Dependen Producion

More information

STRUCTURING EQUITY INVESTMENT IN PPP PROJECTS Deepak. K. Sharma 1 and Qingbin Cui 2

STRUCTURING EQUITY INVESTMENT IN PPP PROJECTS Deepak. K. Sharma 1 and Qingbin Cui 2 ABSTRACT STRUCTURING EQUITY INVESTMENT IN PPP PROJECTS Deepak. K. Sharma 1 and Qingbin Cui 2 Earlier sudies have esablished guidelines o opimize he capial srucure of a privaized projec. However, in he

More information

Owens Community College

Owens Community College Sudy in he Unied Saes a: Owens Communiy College experience owens The Owens experience can help you sar he firs wo years of a bachelor s degree or prepare for a career. Owens invies you o seek your unique

More information

The Complete VoIP Telecom Service Provider. Myth: SIP Trunks are Hard to Configure

The Complete VoIP Telecom Service Provider. Myth: SIP Trunks are Hard to Configure The Complee VoIP Telecom Service Provider Myh: SIP Trunks are Hard o Configure 1 Overview Wha are we rying o avoid? Inerne Access Choices InGae (SIParaor vs Firewall) Cusomer Nework PBX Seup Conclusion

More information

ANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS

ANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS ANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS R. Caballero, E. Cerdá, M. M. Muñoz and L. Rey () Deparmen of Applied Economics (Mahemaics), Universiy of Málaga,

More information

1 HALF-LIFE EQUATIONS

1 HALF-LIFE EQUATIONS R.L. Hanna Page HALF-LIFE EQUATIONS The basic equaion ; he saring poin ; : wrien for ime: x / where fracion of original maerial and / number of half-lives, and / log / o calculae he age (# ears): age (half-life)

More information

Double Entry System of Accounting

Double Entry System of Accounting CHAPTER 2 Double Enry Sysem of Accouning Sysem of Accouning \ The following are he main sysem of accouning for recording he business ransacions: (a) Cash Sysem of Accouning. (b) Mercanile or Accrual Sysem

More information

PolicyCore. Putting Innovation and Customer Service at the Core of Your Policy Administration and Underwriting

PolicyCore. Putting Innovation and Customer Service at the Core of Your Policy Administration and Underwriting PolicyCore Puing Innovaion and Cusomer Service a he Core of Your Policy Adminisraion and Underwriing As new echnologies emerge and cusomer expecaions escalae, P&C insurers are seeing opporuniies o grow

More information

GUIDE GOVERNING SMI RISK CONTROL INDICES

GUIDE GOVERNING SMI RISK CONTROL INDICES GUIDE GOVERNING SMI RISK CONTROL IND ICES SIX Swiss Exchange Ld 04/2012 i C O N T E N T S 1. Index srucure... 1 1.1 Concep... 1 1.2 General principles... 1 1.3 Index Commission... 1 1.4 Review of index

More information

Automatic measurement and detection of GSM interferences

Automatic measurement and detection of GSM interferences Auomaic measuremen and deecion of GSM inerferences Poor speech qualiy and dropped calls in GSM neworks may be caused by inerferences as a resul of high raffic load. The radio nework analyzers from Rohde

More information

Chapter Four: Methodology

Chapter Four: Methodology Chaper Four: Mehodology 1 Assessmen of isk Managemen Sraegy Comparing Is Cos of isks 1.1 Inroducion If we wan o choose a appropriae risk managemen sraegy, no only we should idenify he influence ha risks

More information

Optimal Investment and Consumption Decision of Family with Life Insurance

Optimal Investment and Consumption Decision of Family with Life Insurance Opimal Invesmen and Consumpion Decision of Family wih Life Insurance Minsuk Kwak 1 2 Yong Hyun Shin 3 U Jin Choi 4 6h World Congress of he Bachelier Finance Sociey Torono, Canada June 25, 2010 1 Speaker

More information

INTRODUCTION TO EMAIL MARKETING PERSONALIZATION. How to increase your sales with personalized triggered emails

INTRODUCTION TO EMAIL MARKETING PERSONALIZATION. How to increase your sales with personalized triggered emails INTRODUCTION TO EMAIL MARKETING PERSONALIZATION How o increase your sales wih personalized riggered emails ECOMMERCE TRIGGERED EMAILS BEST PRACTICES Triggered emails are generaed in real ime based on each

More information

MTH6121 Introduction to Mathematical Finance Lesson 5

MTH6121 Introduction to Mathematical Finance Lesson 5 26 MTH6121 Inroducion o Mahemaical Finance Lesson 5 Conens 2.3 Brownian moion wih drif........................... 27 2.4 Geomeric Brownian moion........................... 28 2.5 Convergence of random

More information

Course Outline. Course Coordinator: Dr. Tanu Sharma Assistant Professor Dept. of humanities and Social Sciences Email:tanu.sharma@juit.ac.

Course Outline. Course Coordinator: Dr. Tanu Sharma Assistant Professor Dept. of humanities and Social Sciences Email:tanu.sharma@juit.ac. Course Name : HUMAN RESOURCE MANAGEMENT Course Code: 10B1WPD75 Course Credi: (-0-0) Semeser: VII Course Type: Elecive (All B. Tech. sudens) Deparmen: Humaniies and Social Sciences Course Coordinaor: Dr.

More information

CLASSIFICATION OF REINSURANCE IN LIFE INSURANCE

CLASSIFICATION OF REINSURANCE IN LIFE INSURANCE CLASSIFICATION OF REINSURANCE IN LIFE INSURANCE Kaarína Sakálová 1. Classificaions of reinsurance There are many differen ways in which reinsurance may be classified or disinguished. We will discuss briefly

More information

DDoS Attacks Detection Model and its Application

DDoS Attacks Detection Model and its Application DDoS Aacks Deecion Model and is Applicaion 1, MUHAI LI, 1 MING LI, XIUYING JIANG 1 School of Informaion Science & Technology Eas China Normal Universiy No. 500, Dong-Chuan Road, Shanghai 0041, PR. China

More information

ACTUARIAL FUNCTIONS 1_05

ACTUARIAL FUNCTIONS 1_05 ACTUARIAL FUNCTIONS _05 User Guide for MS Office 2007 or laer CONTENT Inroducion... 3 2 Insallaion procedure... 3 3 Demo Version and Acivaion... 5 4 Using formulas and synax... 7 5 Using he help... 6 Noaion...

More information

INTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES

INTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES INTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES OPENGAMMA QUANTITATIVE RESEARCH Absrac. Exchange-raded ineres rae fuures and heir opions are described. The fuure opions include hose paying

More information

Optimal Growth for P&C Insurance Companies

Optimal Growth for P&C Insurance Companies Opimal Growh for P&C Insurance Companies by Luyang Fu AbSTRACT I is generally well esablished ha new business produces higher loss and expense raios and lower reenion raios han renewal business. Ironically,

More information

Inventory Management and Demand Prediction System for Reagents and Consumables

Inventory Management and Demand Prediction System for Reagents and Consumables Invenory Managemen and Demand Predicion Sysem for Reagens and Consumables Tzu-Chuen Lu, Shih-Chieh Lai, 3 Chun-Ya Tseng *, Firs Auhor, Corresponding Auhor Deparmen of Informaion Managemen, Chaoyang Universiy

More information

CHARGE AND DISCHARGE OF A CAPACITOR

CHARGE AND DISCHARGE OF A CAPACITOR REFERENCES RC Circuis: Elecrical Insrumens: Mos Inroducory Physics exs (e.g. A. Halliday and Resnick, Physics ; M. Sernheim and J. Kane, General Physics.) This Laboraory Manual: Commonly Used Insrumens:

More information

A Joint Optimization of Operational Cost and Performance Interference in Cloud Data Centers

A Joint Optimization of Operational Cost and Performance Interference in Cloud Data Centers A Join Opimizaion of Operaional Cos and Performance Inerference in Cloud Daa Ceners Xibo Jin, Fa Zhang, Lin Wang, Songlin Hu, Biyu Zhou and Zhiyong Liu Insiue of Compuing Technology, Chinese Academy of

More information

Journal Of Business & Economics Research September 2005 Volume 3, Number 9

Journal Of Business & Economics Research September 2005 Volume 3, Number 9 Opion Pricing And Mone Carlo Simulaions George M. Jabbour, (Email: jabbour@gwu.edu), George Washingon Universiy Yi-Kang Liu, (yikang@gwu.edu), George Washingon Universiy ABSTRACT The advanage of Mone Carlo

More information

Improvement of a TCP Incast Avoidance Method for Data Center Networks

Improvement of a TCP Incast Avoidance Method for Data Center Networks Improvemen of a Incas Avoidance Mehod for Daa Cener Neworks Kazuoshi Kajia, Shigeyuki Osada, Yukinobu Fukushima and Tokumi Yokohira The Graduae School of Naural Science and Technology, Okayama Universiy

More information

A New Type of Combination Forecasting Method Based on PLS

A New Type of Combination Forecasting Method Based on PLS American Journal of Operaions Research, 2012, 2, 408-416 hp://dx.doi.org/10.4236/ajor.2012.23049 Published Online Sepember 2012 (hp://www.scirp.org/journal/ajor) A New Type of Combinaion Forecasing Mehod

More information

The Greek financial crisis: growing imbalances and sovereign spreads. Heather D. Gibson, Stephan G. Hall and George S. Tavlas

The Greek financial crisis: growing imbalances and sovereign spreads. Heather D. Gibson, Stephan G. Hall and George S. Tavlas The Greek financial crisis: growing imbalances and sovereign spreads Heaher D. Gibson, Sephan G. Hall and George S. Tavlas The enry The enry of Greece ino he Eurozone in 2001 produced a dividend in he

More information

Energy and Performance Management of Green Data Centers: A Profit Maximization Approach

Energy and Performance Management of Green Data Centers: A Profit Maximization Approach Energy and Performance Managemen of Green Daa Ceners: A Profi Maximizaion Approach Mahdi Ghamkhari, Suden Member, IEEE, and Hamed Mohsenian-Rad, Member, IEEE Absrac While a large body of work has recenly

More information

Experimental exploration of decision making in production-inventory system

Experimental exploration of decision making in production-inventory system Experimenal exploraion of decision making in producion-invenory sysem Felicjan Rydzak 1, Agaa Sawicka 2 1 Cenre for Advanced Manufacuring Technologies, Wroclaw Universiy of Technology, ul. Lukasiewicza

More information

Measuring macroeconomic volatility Applications to export revenue data, 1970-2005

Measuring macroeconomic volatility Applications to export revenue data, 1970-2005 FONDATION POUR LES ETUDES ET RERS LE DEVELOPPEMENT INTERNATIONAL Measuring macroeconomic volailiy Applicaions o expor revenue daa, 1970-005 by Joël Cariolle Policy brief no. 47 March 01 The FERDI is a

More information

Individual Health Insurance April 30, 2008 Pages 167-170

Individual Health Insurance April 30, 2008 Pages 167-170 Individual Healh Insurance April 30, 2008 Pages 167-170 We have received feedback ha his secion of he e is confusing because some of he defined noaion is inconsisen wih comparable life insurance reserve

More information

THE FIRM'S INVESTMENT DECISION UNDER CERTAINTY: CAPITAL BUDGETING AND RANKING OF NEW INVESTMENT PROJECTS

THE FIRM'S INVESTMENT DECISION UNDER CERTAINTY: CAPITAL BUDGETING AND RANKING OF NEW INVESTMENT PROJECTS VII. THE FIRM'S INVESTMENT DECISION UNDER CERTAINTY: CAPITAL BUDGETING AND RANKING OF NEW INVESTMENT PROJECTS The mos imporan decisions for a firm's managemen are is invesmen decisions. While i is surely

More information

Optimal Stock Selling/Buying Strategy with reference to the Ultimate Average

Optimal Stock Selling/Buying Strategy with reference to the Ultimate Average Opimal Sock Selling/Buying Sraegy wih reference o he Ulimae Average Min Dai Dep of Mah, Naional Universiy of Singapore, Singapore Yifei Zhong Dep of Mah, Naional Universiy of Singapore, Singapore July

More information

Software Project Management tools: A Comparative Analysis

Software Project Management tools: A Comparative Analysis Sofware Projec Managemen ools: A Comparaive Analysis Mrs. Sonali Nemade Pad.Dr.D.Y.Pail A.C.S. College, Pimpri (India) sonali_namade@yahoo.co.in Mrs. Madhuri.A. Darekar Pad.Dr.D.Y.Pail A.C.S. College,

More information

MEDIA KIT NEW YORK CITY BAR

MEDIA KIT NEW YORK CITY BAR MEDIA KIT NEW YORK CITY BAR The New York Ciy Bar is he premier professional membership associaion for lawyers in he greaer New York Meropolian area. Wih over 24,000 aorney and law suden members, we represen

More information

The Complete VoIP Telecom Service Provider

The Complete VoIP Telecom Service Provider The Complee VoIP Telecom Service Provider 1 Overview Company Overview SIP Trunking Produc Overview Technical Specificaions Pricing Why SIP Trunking? Benefis over radiional elecom Ideal cusomer 2 Company

More information

Time-Series Forecasting Model for Automobile Sales in Thailand

Time-Series Forecasting Model for Automobile Sales in Thailand การประช มว ชาการด านการว จ ยด าเน นงานแห งชาต ประจ าป 255 ว นท 24 25 กรกฎาคม พ.ศ. 255 Time-Series Forecasing Model for Auomobile Sales in Thailand Taweesin Apiwaanachai and Jua Pichilamken 2 Absrac Invenory

More information

Prostate Cancer. Options for Localised Cancer

Prostate Cancer. Options for Localised Cancer Prosae Cancer Opions for Localised Cancer You or someone you know is considering reamen opions for localised prosae cancer. his leafle is designed o give you a shor overview of he opions available. For

More information

Mathematics in Pharmacokinetics What and Why (A second attempt to make it clearer)

Mathematics in Pharmacokinetics What and Why (A second attempt to make it clearer) Mahemaics in Pharmacokineics Wha and Why (A second aemp o make i clearer) We have used equaions for concenraion () as a funcion of ime (). We will coninue o use hese equaions since he plasma concenraions

More information

Markit Excess Return Credit Indices Guide for price based indices

Markit Excess Return Credit Indices Guide for price based indices Marki Excess Reurn Credi Indices Guide for price based indices Sepember 2011 Marki Excess Reurn Credi Indices Guide for price based indices Conens Inroducion...3 Index Calculaion Mehodology...4 Semi-annual

More information

Chapter 2 Problems. 3600s = 25m / s d = s t = 25m / s 0.5s = 12.5m. Δx = x(4) x(0) =12m 0m =12m

Chapter 2 Problems. 3600s = 25m / s d = s t = 25m / s 0.5s = 12.5m. Δx = x(4) x(0) =12m 0m =12m Chaper 2 Problems 2.1 During a hard sneeze, your eyes migh shu for 0.5s. If you are driving a car a 90km/h during such a sneeze, how far does he car move during ha ime s = 90km 1000m h 1km 1h 3600s = 25m

More information

LINKING STRATEGIC OBJECTIVES TO OPERATIONS: TOWARDS A MORE EFFECTIVE SUPPLY CHAIN DECISION MAKING. Changrui Ren Jin Dong Hongwei Ding Wei Wang

LINKING STRATEGIC OBJECTIVES TO OPERATIONS: TOWARDS A MORE EFFECTIVE SUPPLY CHAIN DECISION MAKING. Changrui Ren Jin Dong Hongwei Ding Wei Wang Proceedings of he 2006 Winer Simulaion Conference L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoo, eds. LINKING STRATEGIC OBJECTIVES TO OPERATIONS: TOWARDS A MORE EFFECTIVE

More information

Ecological Scheduling Decision Support System Based on RIA and Cloud Computing on the YaLong River Cascade Project

Ecological Scheduling Decision Support System Based on RIA and Cloud Computing on the YaLong River Cascade Project 2012 4h Inernaional Conference on Signal Processing Sysems (ICSPS 2012) IPCSIT vol. 58 (2012) (2012) IACSIT Press, Singapore DOI: 10.7763/IPCSIT.2012.V58.31 Ecological Scheduling Decision Suppor Sysem

More information

Towards Incentive-Compatible Reputation Management

Towards Incentive-Compatible Reputation Management Towards Incenive-Compaible Repuaion Managemen Radu Jurca, Boi Falings Arificial Inelligence Laboraory Swiss Federal Insiue of Technology (EPFL) IN-Ecublens, 115 Lausanne, Swizerland radu.jurca@epfl.ch,

More information

NASDAQ-100 Futures Index SM Methodology

NASDAQ-100 Futures Index SM Methodology NASDAQ-100 Fuures Index SM Mehodology Index Descripion The NASDAQ-100 Fuures Index (The Fuures Index ) is designed o rack he performance of a hypoheical porfolio holding he CME NASDAQ-100 E-mini Index

More information

Towards Optimal Capacity Segmentation with Hybrid Cloud Pricing

Towards Optimal Capacity Segmentation with Hybrid Cloud Pricing Towards Opimal Capaciy Segmenaion wih Hybrid Cloud Pricing Wei Wang, Baochun Li, and Ben Liang Deparmen of Elecrical and Compuer Engineering Universiy of Torono Absrac Cloud resources are usually priced

More information

Carrier assignment models in transportation procurement

Carrier assignment models in transportation procurement Journal of he Operaional Research Sociey (26) 57, 1472 1481 r 26 Operaional Research Sociey Ld. All righs reserved. 16-5682/6 $3. www.palgrave-journals.com/jors arrier assignmen models in ransporaion procuremen

More information

Automated Allocation of ESA Ground Station Network Services

Automated Allocation of ESA Ground Station Network Services Auomaed Allocaion of ESA Ground Saion Nework Services Sylvain Damiani (), Holger Dreihahn (), Jörg Noll (), Marc Niézee (), and Gian Paolo Calzolari () () VEGA, Aerospace Division Rober Bosch Sraße 7,

More information

Private Cloud Computing for Enterprises: Meet the Demands of High Utilization and Rapid Change

Private Cloud Computing for Enterprises: Meet the Demands of High Utilization and Rapid Change Privae Cloud Compuing for Enerprises: Mee he Demands of High Uilizaion and Rapid Change Wha You Will Learn Enerprise daa ceners are faced wih a criical challenge: The number of applicaions and amoun of

More information