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Algorithms 2018,11, 35
As far as the tables TBLu, for all the nodes belonging to a certain arbitrary SC layer, says,
arederived,all the tablesaregatheredinto theCut_TableCTs for theentireSCcut. LetRs(u)denote
the totalnumberofobservedadverse (critical)events inanodeuofcutCsduringacertainplanning
period. If suchcutcontainsn(s)nodes, the totalnumberofcriticalevents in it isNs=∑u=1, . . . ,n(s)Rs(u).
Assumethat thereareF riskdrivers. Foreachriskdriver f (f =1, . . . ,F),wecancompute thenumber
Ns(u, f)of criticaleventscausedbydriver f in thenodeuandthe totalnumberNs(f)of criticalevents
inallnodesofcutCs, as registered in theriskprotocols.
Therelative frequencyps(f)of thatdriver f is thesourceofdifferent critical events innodesof
sandcanbetreatedas theestimationof thecorrespondingprobability. Thenwecompute the latter
probabilityas
ps(f)=Ns(f)/Ns (12)
Then∑f ps(f)=1.
Forthesakeofsimplicityof furtheranalysis,ourmodelapplies tothecasewhenthecriticalevents
are independentwithin thesametierandthe lossesareadditive (theseassumptionswillberelaxed in
our futureresearch). Foranynodeu from s,wecandefinecorrespondingprobabilitiesps(u, f)of the
event thatadriver f is thesourceofadverseevents innodeu
ps(u, f)=Ns(u, f)/Rs(u). (13)
Thispaper treats theps(u, f)valuesdefinedbyEquation(3)asprobabilitiesofeventsparticipating
incalculationof theentropyfunction inEquation(1).
Themain ideaof thesuggestedentropicapproach is that the informationentropyinthisstudy
estimatestheaverageamountof informationcontainedinastreamofcriticaleventsof theriskprotocol.
Thus theentropycharacterizesouruncertainty,or theabsenceofknowledge,about therisks. The idea
here is that the less theentropy is, themore informationandknowledgeabout risks isavailable for the
decisionmakers.
Theentropyvaluecanbecomputed iteratively foreachcutof theSC.Assumethat thenodesofa
cutCs−1, aredefinedatstep(iteration) s−1.DenotebyTsall supplier-nodes in thesupply layer sof
thegiventree. LetLs(Ts)denote the total lossesdefinedbytheriskprotocolandsummedupforall
nodesofcutCs in tiersTs:Ls(Ts)=∑u∈Ts cs(u). Further, letLTdenote the total losses forallnodesof
theentirechain. Thus,Ls(Ts) arecontributionsof thesuppliersofcutCs into the total lossesLT.
ThenLs(Ts)/LTdefinetherelative losses inthe s-truncatedsupplychain. Therelativecontribution
of lower tiers, that is,of thosewith larger svalues,are, respectively, (LT−Ls(Ts))/LT.Onecanobserve
that the larger is the share (LT−Ls(Ts))/LT in comparisonwithLs(Ts)/LT, the less is theavailable
informationabout the losses in thecutCs. Forexample, if theratioLs(Ts)/LT=0.2, thiscasegiveus
less informationabout the losses incutCs in comparisonwith theopposite caseofLs(Ts)/LT=0.8.
This argumentmotivatesus to take the ratios (LT−Ls(Ts))/LT as the coefficients (weights) of the
entropy(or,ofourunawareness)about theeconomic losses incurredbyadverseeventsaffectingthe
environmentalquality. Inotherwords, the lattercoefficientsweighthe lackofourknowledgeabout
the losses;as faras thenumber sgrows, thesecoefficientsbecomelessandlesssignificant.
Thenthe totalentropyofallnodesu includedinto thecut swillbedefinedas
H(s)=∑uH(u) (14)
where theweightedentropyineachnodeu is computedas
H(u)=−Us∑f ps(u, f) logps(u, f), (15)
Us=(LT−Ls(Ts))/LT, (16)
Letxu=1ifnodeu fromTs is includedinto s, andxu=0,otherwise.
170
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Buch Algorithms for Scheduling Problems"
Algorithms for Scheduling Problems
- Titel
- Algorithms for Scheduling Problems
- Autoren
- Frank Werner
- Larysa Burtseva
- Yuri Sotskov
- Herausgeber
- MDPI
- Ort
- Basel
- Datum
- 2018
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-03897-120-7
- Abmessungen
- 17.0 x 24.4 cm
- Seiten
- 212
- Schlagwörter
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Kategorien
- Informatik
- Technik