Table Of ContentStudies in Computational Intelligence 966
Julian Andres Zapata-Cortes
Giner Alor-Hernández
Cuauhtémoc Sánchez-Ramírez
Jorge Luis García-Alcaraz Editors
New Perspectives
on Enterprise
Decision-Making
Applying Artificial
Intelligence
Techniques
Studies in Computational Intelligence
Volume 966
SeriesEditor
JanuszKacprzyk,PolishAcademyofSciences,Warsaw,Poland
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·
Julian Andres Zapata-Cortes
·
Giner Alor-Hernández
·
Cuauhtémoc Sánchez-Ramírez
Jorge Luis García-Alcaraz
Editors
New Perspectives
on Enterprise
Decision-Making Applying
Artificial Intelligence
Techniques
Editors
JulianAndresZapata-Cortes GinerAlor-Hernández
CEIPABusinessSchool DivisionofResearchandPostgraduate
FundaciónUniversitariaCEIPA Studies
Sabaneta,Colombia InstitutoTecnológicodeOrizaba
TecnológicoNacionaldeMéxico
CuauhtémocSánchez-Ramírez Orizaba,Mexico
DivisionofResearchandPostgraduate
Studies JorgeLuisGarcía-Alcaraz
InstitutoTecnológicodeOrizaba AutonomousUniversityofCiudadJuarez
TecnológicoNacionaldeMéxico CiudadJuárez,Chihuahua,Mexico
Orizaba,Mexico
DivisionofResearchandPostgraduate
Studies
TecnológicoNacionaldeMéxico/Instituto
TecnológicodeCiudadJuárez
CiudadJuárez,Chihuahua,Mexico
ISSN1860-949X ISSN1860-9503 (electronic)
StudiesinComputationalIntelligence
ISBN978-3-030-71114-6 ISBN978-3-030-71115-3 (eBook)
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Preface
With the increase in computing power produced in the last decade, the ability to
analyze large datasets and the development of new algorithms have produced the
possibilitytodevelopmechanismsthatallowdecision-makingprocessesthatinpast
yearswerethoughtimpossibletobecarriedout.Thesemechanismshavethecapacity
tosurpassthehumancapacityforthesetaskswiththeabilitytocarryouttheanal-
ysisautonomouslyandtolearnfromthemselves,allowingthemtobeconceivedas
artificialintelligence.
ArtificialIntelligence(AI)techniquescovertheautomationofcognitiveandphys-
icaltasks.Thesetechniqueshelppeopletoperformtasksfasterandmakebetterdeci-
sions. It enables the automation of decision-making process without human inter-
vention.AItechniquescanenhanceautomationbyreducingintensivehumanlabor
andtedioustasks.TherearemoreformsinwhichArtificialIntelligenceismaking
a difference for enterprises in decision-making marketing, customer relationship
management,recommendersystems,problem-solving,opinionmining,augmented
analytics,tomentionbutafew.
Inmarketing,itisnecessarytounderstandcustomerneedsanddesiresandaligning
productstothoseneedsanddesires.Ahandleonchangingcustomerbehaviorisvital
tomakethebestmarketingdecisions.AIsimulationandmodelingtechniquesprovide
reliable insight into the consumers’ persona. This will help predicting consumers’
behavior. Through real-time data gathering, trend analysis, and forecasting, an AI
systemcanhelpbusinessesmakeinsightfulmarketingdecisions.Furthermore,orga-
nizationscanidentifyaconsumer’slifetimevaluewiththehelpofAI’sbuyerpersona
modeling. It can help organizations manage multiple inputs. During a complex
decision-making process, AI can efficiently manage and control different factors
at the same point in time. It can source and process large amounts of data within
minutes while providing valuable business-based insights. While we humans face
decision fatigue, algorithms do not have such limitations, which make AI-based
decisions faster and better. Also, AI techniques have provided businesses invalu-
ableinsightaboutconsumers,whichhelpsthemenhancetheircommunicationwith
the consumers. It also helps retailers predict product demand and respond to it
quickly.Forthisend,opinionmininghelpsbusinessesunderstandwhypeoplefeel
v
vi Preface
thewaytheyfeel.Mostoftenasinglecustomer’sconcernsmightbecommonamong
others.Whensufficientopinionsaregatheredandanalyzedcorrectly,theinformation
gleanedwillhelporganizationsgaugeandpredicttheconcernsofthesilentmajority.
AIhasimprovedthisminingprocessthroughautomation,whichisquickerandmore
reliable,helpingorganizationsinmakingcriticalbusinessdecisions.
Ine-commerce,anAIsystemlearnsaconsumer’spreferencebasedon“explicit”
or “implicit” feedbacks. This kind of systems are called recommender systems. A
recommender system can provide information helping the organization to reduce
bouncerateandcraftbettercustomer-specifictargetedcontent.Wisebusinessdeci-
sions are made when business executives and decision-makers have reliable data
andrecommendations.AInotonlyimprovestheperformanceofboththeindividual
membersoftheteambutalsothecompetitiveedgeofthebusiness.
In medicine, the artificial intelligence allows the improvement of many tasks,
rangingfrombettermanagementofresourcessuchasroomsandpersonnel,aswell
asachievingmoreaccurateandfastermedicaldiagnoses,suchascomputer-assisted
radiologies or the use of robots to perform less invasive surgeries. In the service
industry,AIisusedinautomatedonlineassistantsthatallowfasterandlowercost
service,whichhastheabilitytolearnfrompastinteractions,thusimprovingcustomer
service.
Basedonthisunderstanding,therearemanyotherapplicationsofartificialintelli-
genceinmanyknowledgefields,andthisisthemainreasonandthecoreofthepresent
book, which is aimed to disseminate current trends among innovative and high-
quality research regarding the implementation of conceptual frameworks, strate-
gies, techniques, methodologies, informatics platforms, and models about Enter-
prise Decision-Making Applying Artificial Intelligence Techniques. The specific
objectivesofthebookcanbesummarizedasfollows:
(cid:129)
Generateadisseminationvenuefromboththeacademiaandtheindustryinthe
topics studied in the book, presenting cases of new approaches, applications,
methods,andtechniquesoftheapplicationofArtificialIntelligenceinenterprise
decision-making.
(cid:129)
Generate a collection of theoretical and practical research works in the field of
ArtificialIntelligenceTechniquesappliedinenterprisedecision-making.
(cid:129)
Establish the state of the art in the field of Artificial Intelligence Techniques
appliedinenterprisedecision-making.
Thisbookiscomposedofasetofchapters,eachofthekindofregularresearch
papers. These works have been edited according to the norms and guidelines of
Springer Verlag Editorial. Several calls for chapters were distributed among the
mainmailinglistsofthefieldforresearcherstosubmittheirworkstothisissue.25
expressions of interest in the form of abstracts were received in total, which were
subjecttoascreeningprocesstoensuretheirclarity,authenticity,andrelevancyto
thisbook.TheseproposalscamefromseveralcountriessuchasColombia,Mexico,
Spain,Perú,andUkraine.
After the abstract reviewing process, 25 proposals were accepted and asked to
submitfullversions.Theseversionswerereviewedbyatleasttwopairsinorderto
Preface vii
ensure the relevance and quality of the documents. After this process, 21 chapters
werefinallyacceptedfortheirpublicationoncethecorrectionsrequestedbythepairs
andtheeditorswerecompleted.
The book content is structured in three parts: (1) Industrial Applications, (2)
Decision-Making Systems for Industry, and (3) Artificial Intelligence Techniques.
Thechaptersineachofthesepartsareasfollows.
PartIIndustrialApplications:Thispartcontainssevenchapters.
Chapter 1,entitled“MergingEventLogsforInter-organizationalProcessMining,”
presentsamethodologytomergetheeventlogsofthedifferentpartnersofacollab-
orativebusinessprocess,inordertoserveasinputfortheprocessminingalgorithm.
Ontheonehand,themethodologyconsistsofamethodsetforsearchingthecorre-
lationbetweeneventsofthelogofdifferentpartnersinvolvedinthecollaboration.
Thesemethodsareimplementedatthetracelevelandtheactivitylevel.Ontheother
hand,themethodologyconsistsofasetofmethodsandrulesfordiscoveringprocess
choreography. From the knowledge gained by the above methods, message-type
tasks are identified and marked in each event log, and then using a formal set of
rules,themessagetasksub-type(sendorreceive)isdiscovered.Finally,linksusing
messagesequenceflowconnectorsbetweenmessagetasksidentifiedaspairactivi-
tiesineventlogsareautomaticallydefined.Theproposedapproachistestedusing
areal-lifeeventlogthatconfirmstheireffectivenessandefficiencyintheautomatic
specificationofmessageflowsoftheprocesschoreographydiscovered,allowingto
buildacollaborativebusinessprocessmodel.
Chapter 2, entitled “Towards Association Rule-Based Item Selection Strategy
in Computerized Adaptive Testing,” proposes the integration of Association Rule
MiningasanitemselectioncriterioninaCATsystem.Specifically,wepresentthe
analysisofassociationruleminingalgorithmssuchasApriori,FPGrowth,Predic-
tiveApriori, and Tertius into three datasets obtained from the subject databases to
knowtheadvantagesanddisadvantagesofeachalgorithmandchoosethemostsuit-
ableonetoemployinanassociationrule-basedCATsystemthatisbeingdevelopedas
aPh.D.project.Wecomparethealgorithmsconsideringthenumberofrulesdiscov-
ered,averagesupportandconfidence,lift,andvelocity.Accordingtotheexperiments,
Apriorifoundruleswithgreaterconfidence,support,lift,andinlesstime.
Chapter 3,entitled“UncertaintyLinguisticSummarizertoEvaluatethePerfor-
mance of Investment Funds,” proposes a methodology to implement the uncertain
linguisticsummarizerposedinLiu’suncertainlogictomeasuretheperformanceof
investment funds in the Colombian capital market. The algorithm extracts a truth
value for a set of linguistic summaries, written as propositions in predicate logic,
wherethetermsforthequantifier,subject,andpredicateareunsharp.Thelinguistic
summarizer proves to be autonomous, successful, efficient, and close to human
language.Furthermore,theimplementationhasageneralscopeandcouldbecomea
dataminingtoolunderuncertainty.Thepropositionsfoundcharacterizewithplenty
ofsensetheinvestmentfundsdata.Finally,acorollarythatallowsacceleratingthe
obtentionofthesummariesispresented.
viii Preface
Chapter 4,entitled“Map-Bot:MappingModelofIndoorWorkEnvironmentsin
MobileRobotics,”presentsamappingmodelofindoorworkenvironmentsinmobile
robotics, called Map-Bot. The model integrates hardware and software modules
fornavigation,dataacquisitionandtransferandmapping.Additionally,themodel
incorporatesacomputerthatrunsthesoftwareresponsiblefortheconstructionoftwo-
dimensionalrepresentationsoftheenvironment(Vespuccimodule),amobilerobot
thatcollectssensoryinformationfromtheworkplaceandawirelesscommunication
modulefordatatransferbetweenthecomputerandtherobot.Theresultsobtained
allowtheimplementationofthereactivebehavior“followwalls”locatedonitsright
sideonpathsof560cm.Themodelallowedtoreachasafeandstablenavigationfor
indoorworkenvironmentsusingthisdistributedapproach.
Chapter 5, entitled “Production Analysis of the Beekeeping Chain in Vichada,
Colombia.ASystemDynamicsApproach,”presentsamodelofbeekeepingproduc-
tionintheregionofVichadainColombia.Thebeekeepingchainwaschosenbecause
it is a sector of great economic importance in the mentioned region which has
the highest indices of multidimensional poverty in Colombia, but also it is one of
the places with the greatest conservation of its biodiversity. A systems dynamics
approachisusedfromacausaldiagramtoexplaintheinteractionsamongbeerearing,
wax production, honey production, and transformation, and then simulations were
performedtodeterminethebehaviorofinventoriesconcerningtheproductionand
demand.Thismodelhighlightsthedynamicsofthesystemandthemanagementofthe
supplychainandispresentedasausefultooltopredictproduction-demandscenarios
in the beekeeping sector where similar studies are scarce. As future research, it is
recommendedtoincludetheeconomicnatureoftheproductsinthiskindofmodels
so that scenarios can be proposed to help beekeepers make production decisions
accordingtodemand,anddevelopinventorypolicies.
Chapter 6,entitled“EffectofTPMandOEEontheSocialPerformanceofCompa-
nies,”reportsamodelofstructuralequationsthatintegratesthreeindependentvari-
ables:Totalproductivemaintenance,JustintimeandOverallequipmentefficiency,
andtherelationshiptheyhavewithSocialsustainabilityasadependentvariable.The
four variables are related through six hypotheses that are validated with informa-
tiongatheredfrom239questionnairesansweredbyexecutiveslaboringatMexican
maquiladoraindustry.Thepartialleastsquarestechniqueisusedtostatisticallyvali-
datetherelationshipsamongvariables.FindingsindicatethatTotalpredictivemain-
tenancehasastrongimpactonOverallequipmentefficiency andJustintime,and
the variables that most influence Social sustainability are Total predictive mainte-
nance and Just in time. It is concluded that Social sustainability can be obtained
throughproperuseandmaintenanceofthemachinesandwithtimelyfulfillmentof
productionorders.
Chapter 7,entitled“ENERMONGRID: IntelligentEnergy Monitoring,Visual-
ization and Fraud Detection for Smart Grids,” presents a tool used for intelligent
energy monitoring, data visualization, and fraud detection in electric networks to
generate rich information in near real time, which can be used to make decisions
fortheoptimalenergyproduction,generation,distribution,andconsumption.This
tool allows solving many problems that can arise when dealing with energy load
Preface ix
estimates,lossestimates,aswellasfrauddetectionandprevention,intheentitiesin
chargeofmanaginganelectricnetwork,withthefocusofdoingsoinsmartgrids.
PartIIDecision-MakingSystemsforIndustry:Thispartcontainssevenchapters.
Chapter 8, entitled “Measuring Violence Levels in Mexico Through Tweets,”
proposesanovelwaytoevaluatewhatpeoplesayandhowtheyfeelaboutviolence
byanalyzingTwitterdata.Todothis,wedescribeamethodologytocreateanindi-
catortodenotesocialperception.ThismethodologyusestechnologieslikeBigData,
Twitter analytics, Web mining, and Semantic Web by manipulating software like
ELK (Elasticsearch for data storage, Logstash for collecting data, and Kibana for
data visualization); SPSS and R for statistical data analysis; and Atlas.ti, Ghephi,
and Wordle for semantic analysis. At the end of the chapter, we show our results
withawordcloud,asocialgraph,andtheindicatorofsocialperceptionofviolence
inMexicoatthefederalentityandmetropolitanzonelevels.
Chapter 9, entitled “Technology Transfer from a Tacit Knowledge Conserva-
tion Model into Explicit Knowledge in the Field of Data Envelopment Analysis,”
presents a preservation model of production engineers’ tacit knowledge of Data
EnvelopmentAnalysis(DEA).Theirexpertisewasexplicitlycodedintoacomputer
system,andthemodelwasdevelopedbyapplyingtechniquesandproceduresfrom
thefieldsofengineeringandknowledgemanagement.Technologytransferenablesto
solvetheproblemofselectingcriteriaandinterpretingresultswithDEAtechniques,
whentheefficiencyofsimilarorganizationsiscomparedusinganefficientfrontier
derivedfromnon-parametricapproximationsofsuchtechniques.Misunderstanding
thetechniquesleadstomisinterpretationsofDEAresults.Thismodelwascreated
byapplyingKnowledgeEngineering,whichenablestopreserveandextendspecific
experiencesandexpertiseintimebymeansofcomputersolutions.Themodelhadan
efficientandpositiveimpactonstrategicself-learningprocessesforthecommunity
interestedinproductionengineering,knowledgetransfer,andmanagement.
Chapter 10, entitled “Performance Analysis of Decision Aid Mechanisms for
HardwareBotsBasedonELECTREIIIandCompensatoryFuzzyLogic,”proposes
twonovelcognitivepreferencemodelsviableforhardwarewithfewmemorycells
and small processing capacity. These are the Outranking Relations (or OR) and
CompensatoryFuzzyLogic(orCFL)whicharetwotechniquesthatlackastudyof
theirhardwareperformanceasintelligencemodelers,anddespitetheirsimpledefini-
tionandgeneralizationcapacity,hardwareagentsalmostneverusethem.Thechapter
analyzesthefeasibilityofimplementingOutrankingRelations(OR)andCompen-
satory Fuzzy Logic (CFL) in hardware platforms with low resources, highlighting
thatcompetitivenessoftheproposedtoolsandthearisingofnewresearchlines.
Chapter 11,entitled“ABriefReviewofPerformanceandInterpretabilityinFuzzy
Inference Systems,” presents a scoping review related to fuzzy logic emphasizing
in Compensatory Fuzzy Logic (CFL), Archimedean Compensatory Fuzzy Logic
(ACFL), and inference systems. It presents the literature analysis on surveys and
generalreviewstocontrastthescopingreview.Thechapteralsopresentstheresearch
analysisthroughacasestudy,acomparisonofcompensatoryfuzzylogicwithother
fuzzylogicstructuresandotherrelatedworks.
