Logistics, Supply Chain and Financial Predictive Analytics

250 Pages·2019·5.71 MB·English

by  Kusum Deep

Asset Analytics
Performance and Safety Management
Series Editors:
Ajit Kumar Verma · P. K. Kapur · Uday Kumar
Kusum Deep
Madhu Jain
Editors
Said Salhi
Logistics, Supply
Chain and Financial
Predictive Analytics
Theory and Practices
Asset Analytics
Performance and Safety Management
Series editors
Ajit Kumar Verma, Western Norway University of Applied Sciences, Haugesund,
Rogaland, Norway
P.K.Kapur,CentreforInterdisciplinaryResearch,AmityUniversity,Noida,India
Uday Kumar, Division of Operation and Maintenance Engineering, Luleå
University of Technology, Luleå, Sweden
The main aim of this book series is to provide a floor for researchers, industries,
assetmanagers,governmentpolicymakersandinfrastructureoperatorstocooperate
and collaborate among themselves to improve the performance and safety of the
assets with maximum return on assets and improved utilization for the benefit of
society and the environment.
Assets canbedefinedasanyresourcethatwill createvalue tothebusiness.Assets
include physical (railway, road, buildings, industrial etc.), human, and intangible
assets (software,data etc.).The scope ofthebookserieswillbebutnotlimitedto:
(cid:129) Optimization, modelling and analysis of assets
(cid:129) Application of RAMS to the system of systems
(cid:129) Interdisciplinaryandmultidisciplinaryresearchtodealwithsustainabilityissues
(cid:129) Application of advanced analytics for improvement of systems
(cid:129) Applicationofcomputationalintelligence,ITandsoftwaresystemsfordecisions
(cid:129) Interdisciplinary approach to performance management
(cid:129) Integrated approach to system efficiency and effectiveness
(cid:129) Life cycle management of the assets
(cid:129) Integrated risk, hazard, vulnerability analysis and assurance management
(cid:129) Integration of data-information-knowledge for decision support
(cid:129) Production rate enhancement with best practices
(cid:129) Optimization of renewable and non-renewable energy resources
More information about this series at http://www.springer.com/series/15776
Kusum Deep Madhu Jain
(cid:129)
Said Salhi
Editors
Logistics, Supply Chain
and Financial Predictive
Analytics
Theory and Practices
123
Editors
KusumDeep SaidSalhi
Department ofMathematics KentBusiness School,Centre for Logistics
Indian Institute of Technology Roorkee andHeuristic Optimization (CLHO)
Roorkee,Uttarakhand, India University of Kent
Canterbury, Kent, UK
MadhuJain
Department ofMathematics
Indian Institute of Technology Roorkee
Roorkee,Uttarakhand, India
ISSN 2522-5162 ISSN 2522-5170 (electronic)
AssetAnalytics
ISBN978-981-13-0871-0 ISBN978-981-13-0872-7 (eBook)
https://doi.org/10.1007/978-981-13-0872-7
LibraryofCongressControlNumber:2018945458
©SpringerNatureSingaporePteLtd.2019
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Contents
Cooperative/Non-cooperative Supply Chain Models for Imperfect
Quality Items with Trade Credit Financing . . . . . . . . . . . . . . . . . . . . . . 1
Rita Yadav, Sarla Pareek and Mandeep Mittal
Determination of Initial Basic Feasible Solution for Transportation
Problems by: “Supply–Demand Reparation Method”
and “Continuous Allocation Method” . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Balasubramani Prajwal, J. Manasa and Radha Gupta
Comparison Study on Exponential Smoothing and ARIMA Model
for the Fuel Price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Sheik Abdullah Abdul Azees and Ramraj Sasikumar
An Ordering Policy with Generalized Deterioration, Ramp-Type
Demand Under Complete Backlogging. . . . . . . . . . . . . . . . . . . . . . . . . . 43
Trailokyanath Singh, Nirakar Niranjan Sethy and Ameeya Kumar Nayak
Inventory Model with Shortages and Deterioration for Three
Different Demand Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Shalini Singh and G. C. Sharma
Outlier Labeling Methods for Medical Data. . . . . . . . . . . . . . . . . . . . . . 67
K. Senthamarai Kannan and S. Stephen Raj
Goal Programming Model to Budgetary Allocation in Garbage
Disposal Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
P. Jyothi, G. A. Vatsala and Radha Gupta
A Validated Model of a Fault-Tolerant System . . . . . . . . . . . . . . . . . . . 91
Veena B. Mendiratta and Kishor S. Trivedi
Formation and Designing of “Least-Cost Ration Formulation
Application of Cattle” Using Excel VBA . . . . . . . . . . . . . . . . . . . . . . . . 101
Vishal Patil, Radha Gupta, D. Rajendran and Ravinder Singh Kuntal
v
vi Contents
New Stable Numerical Inversion of Generalized Abel Integral
Equation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Shweta Pandey, Sandeep Dixit and S. R. Verma
Empirical Analysis of Probabilistic Bounds . . . . . . . . . . . . . . . . . . . . . . 125
R. Swarnalatha and V. Kumaran
A Graph-Theoretical Approach for Comparison Between
the Pair of Allied Ragas Bhupali and Deshkar of North Indian
Classical Music. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Nandini Sarma and Pranita Sarmah
A General Class of Tests for Testing Homogeneity of Location
Parameters Against Ordered Alternatives . . . . . . . . . . . . . . . . . . . . . . . 163
Manish Goyal and Narinder Kumar
Modelling of Male Age at Marriage: Evidences from Western Region
of Uttar Pradesh (India). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
C. B. Gupta, Sachin Kumar and Brijesh P. Singh
Copula Functions and Applications in Engineering . . . . . . . . . . . . . . . . 195
Pranesh Kumar
A Preservation Technology Model for Deteriorating Items
with Advertisement-Dependent Demand and Trade Credit . . . . . . . . . . 211
Himanshu Rathore
Time Series Model for Stock Price Forecasting in India . . . . . . . . . . . . 221
A. Mohamed Ashik and K. Senthamarai Kannan
Asset Pricing Through Capital Market Curve . . . . . . . . . . . . . . . . . . . . 233
Dipankar Mondal and N. Selvaraju
TQM Indicators Implemented by Teachers of the Primary School . . . . 243
Neha Shroff
About the Editors
Dr. Kusum Deep is Professor at the Department of Mathematics, Indian Institute
of Technology Roorkee, India. Her research interests include numerical optimiza-
tion, nature-inspired optimization, computational intelligence, genetic algorithms,
parallel genetic algorithms and parallel particle swarm optimization.
Dr. Madhu Jain is Associate Professor at the Department of Mathematics, Indian
Institute of Technology Roorkee, India. Her research interests include computer
communicationsnetworks,performancepredictionofwirelesssystems,mathematical
modelling and biomathematics.
Dr. Said Salhi is Director of the Centre for Logistics & Heuristic Optimization
(CLHO)atKentBusinessSchool,UniversityofKent,UK.Priortohisappointment
at Kent in 2005, he served at the University of Birmingham’s School of
Mathematics for 15 years, where, in the latter years, he acted as Head of the
Management Mathematics Group. He obtained his B.Sc. in Mathematics from
University of Algiers and his M.Sc. and Ph.D. in OR from Southampton (Institute
of Mathematics) and Lancaster (School of Management), respectively. He has
edited six special journal issues and chaired the European Working Group in
Location Analysis in 1996 and recently the International Symposium on
Combinatorial Optimisation (CO2016) in Kent, held on 1–3 September 2016. He
has published over 100 papers in academic journals.
vii
Cooperative/Non-cooperative Supply
Chain Models for Imperfect Quality
Items with Trade Credit Financing
RitaYadav,SarlaPareekandMandeepMittal
1 Introduction
Gametheoryisapowerfulmathematicaltool,whichstudiesthecapacityoftheplay-
ers.Thistheoryplaysasignificantroleintheareaofsupplychainconnectedproblems
whose purpose is to construct supply chain policies under numerous assumptions
with different perspectives. These policies demonstrate coordination between sev-
eralchannelsinthesupplychaintogeteffectualoutcome.Nowadays,themaximum
supplychainindustriesareusingcreditperiodpolicytoimprovetheprofitofboth
thepartnersofthesupplychain.Thetradecreditpolicyisgenerallyofferedtothe
buyerbythesellerwhichisauthorizedsettlementbetweenbuyerandsellerforthe
latepayment.Manyresearchersexploredtheirstudyinthisarea.HayleyandHiggins
[1]studiedthebuyer’slotsizeproblemhavingatradecreditcontractbyassuming
afixeddemandandshowedthatlotsizeisnotaffectedbythelengthoftradecredit
period.Kimetal.[2]formulatedamathematicalmodeltofindouttheoptimaltrade
creditperiodwiththeassumptionthattheseller’spriceisfixed.HwangandShinn
[3]showedinhisstudythattheorderquantityofthebuyerfluctuateswiththelength
ofthetradecreditperiodbyconsideringdemand,pricesensitive.
R.Yadav
DepartmentofAppliedScience&Humanities,DronacharyaCollegeofEngineering,Gurugram,
India
e-mail:[email protected]
S.Pareek
ApajiInstituteofMathematicsandAppliedComputerTechnology,BanasthaliUniversity,
Rajasthan,India
e-mail:[email protected]
B
M.Mittal( )
DepartmentofMathematics,AmitySchoolofEngineeringandTechnology,
NewDelhi110061,India
e-mail:[email protected]
©SpringerNatureSingaporePteLtd.2019 1
K.Deepetal.(eds.),Logistics,SupplyChainandFinancialPredictiveAnalytics,
AssetAnalytics,https://doi.org/10.1007/978-981-13-0872-7_1
2 R.Yadavetal.
Primarily,Schwaller[4]developedEOQmodelsondefectiveitemsbycounting
inspectioncost.SomeresearcherslikeJamaletal.[5]andAggarwalandJaggi[6]
consideredunderthefixeddemand,thedeterioratingitemproblemswithpermissible
delayinpayments,themarketdemanddependsonthesellingpriceofthebuyer;Jaggi
and Mittal [7] established policies for imperfect quality deteriorating items under
inflation and permissible delay in payments. Thangam and Uthaya [8] developed
anEPQmodelforperishableitemsandwithtradecreditfinancingpolicyinwhich
demanddependsonboththevariable’ssellingpriceandthetradecreditperiod.Under
permissibledelayinpayments,JaggiandMittal[9]developedaninventorymodel
forimperfectqualityitemsinwhichshortagesarepermittedandfullybacklogged.
Shortagesareeliminatedduringthescreeningprocessandwiththeassumptionthat
demand rate is less than the screening rate. Zhou et al. [10] developed a specific
conditionintheSupplier-Stackelberggameinwhichthetradecreditpolicynotonly
increasestheoverallsupplychainprofitbutalsogivesbenefitstoeachpartnerofthe
supplychain.
Zhouetal.[11]alsofoundasynergiceconomicorderquantitymodel,inwhich
theconceptofimperfectquality,shortageswithtradecredit,andinspectionerrorsare
considered.AbadandJaggi[12]deliberatedsupplychainmodel,inwhichtheend
market demand is price sensitive and with trade credit period offered by the seller
tothebuyer.Theydevelopednon-cooperative(Seller-Stackelberggame)andcoop-
erative (Pareto-efficient solution) relationship between buyer and seller. Esmaeili
etal.[13]alsoexplainedcooperativeandnon-cooperativegamesinwhichtheend
demand was price sensitive as well as marketing expenditure sensitive. This work
wasextendedbyEsmaeiliandZeephongscul[14]forasymmetricinformationgame.
Zhang and Zeephongsekul [15] investigated non-cooperative models with credit
optionbyassumingthesamedemandfunction.
None of the researchers considered the effects of imperfect production on the
supplychainmodelinthecooperativeandnon-cooperativeenvironmentwithtrade
creditfinancing.Inthispaper,wedevelopasupplychainmodelforimperfectitems
withcreditoptionwhichisofferedbytheseller.Sellerdeliversalottothebuyer.Ina
deliveredlot,someitemsmaynotbeofperfectquality.Thus,buyerappliesinspection
processtoseparatedefectiveitemsonthesuppliedlot.Theperfectqualityitemsare
soldatsellingprice,andimperfectitemsaresoldatadiscountedprice.Inthispaper
cooperative and non-cooperative relationship are derived between the two players
(buyer and seller)of the game. Effect of creditperiod is alsoconsidered when the
demandispricesensitive.
Thispaperconsistsofsevensections.Thefirstsectionconsistsofanintroduction
andliteraturereview.Thesecondsectionintroducesnotationandassumptionsusedin
thepaper.ThethirdsectionformulatesthenoncooperativeSeller-Stackelbergmath-
ematicalmodel,inwhichselleristheleaderandbuyeristhefollower.Inthefourth
section,wediscussthecooperative gamemodelwithPareto-efficient approach. In
thefifthandsixthsections,weprovidenumericalexampleswithsensitivityanaly-
sis.Thelastsectionconsistsofconclusionpartwithsuggestionsforfutureresearch
work.