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Algorithms 2018,11, 35 Definethecomplete transitionmatricesas Mˆ(1)(s)= ( pˆ(1)ij (s) ) N×N , s=0,1,2, . . . , with pˆ(1)ij (s)= p (1) j (s)+p (2) ij (s), i, j=1,2, . . . ,N, s=0,1,2, . . . (6) From(5)and(6) it followsthat Mˆ(1)(s)=ML(s)+M(2)(s), s=0,1,2. . . (7) p(2)j ( A(2)j (s) ) = N ∑ i=1 pi(s+1) ·p(2)ij (s), j=1,2, . . . ,N, s=0,1, . . . or p(2)j ( A(2)j (s) ) = N ∑ i=1 pi(s+1) ·p(2)ij (s), j=1,2, . . . ,N, s=0,1, . . . (8) In thematrix form,Equation(8)canberewrittenas p(2)(s)=p(s+1) ·M(2)(s), s=0,1, . . . (9) Thefollowingclaimis true. Claim. The followingrelationholds: N ∑ i=1 ( pi(s+1) · ( N ∑ j=1 p(2)ij (s) )) =1− N ∑ j=1 p(1)j (s), s=0,1, . . . (10) Theproof is straightforwardandskippedhere. 3. InformationEntropyasaMeasureofSupplyChainComplexity InformationentropyisdefinedbyShannonas follows[20].GivenasetofeventsE={e1, . . . , en} with apriori probabilities of event occurrenceP= {p1, . . . , pn}, pi≥ 0, such that pi + .. . +pn =1, theentropyfunctionH isdefinedby H=−∑I pi logpi. (11) Inorder toyieldallnecessary informationontheSCcomplexity issues, thispaperuses thedata recordingofall adverseeventsoccurred. Theenterprise is tocollect andstore thedataaboutmain adverseevents thatoccurredandledtoeconomic losses in theenterprise, compensationcost, aswell as the statistical analysisof the recordeddata. Such requirementalsoapplies to the registrationof informationaboutcontrolofcomplianceof targetandactualenvironmental characteristics. Similar to [12], foreachnodeu, consideran informationdatabasecalleda ‘riskprotocol’. This isa registration listofmost importantevents thathaveoccurred in thenodeduringapre-specifiedtime period. Theprotocolprovidesus the informationwhetherornot theeventsareundesirableand, in the lattercase,whatare its riskdriversandpossible losses. Thisdata is recordedintablesTBLu, representing listsofevents ineachnodeuduringacertain timeperiodT (e.g.,month,oryear). Eachrowinthetablecorrespondstoanindividualeventoccurring inagivennodeatacertain timemoment (forexample, aday).Weusesymbol f asan indexof risk drivers,Fas the totalnumberof riskdrivers,and rasan indexof theevent (row). Thevaluezrf at the intersectionofcolumn f androw r is equal to1 if the risk factor f is a sourceof theadverseevent r, and0—otherwise. The last column,F+1, ineachrow(r) contains themagnitudeofeconomic loss causedbythecorrespondingevent r. 169
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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
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Algorithms for Scheduling Problems