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Algorithms 2018,11, 35
Therefore, foranyaccuracy levelÎľ,wecanselect thenumberofacut s1 forwhich
H(s1â1)âH(s1)
H(0)âH(s1) < Îľ
Thetruncatedpartof theSCcontainingonly layersof thecut (s1â1)possesses therequiredlevel
of theentropyvariation. Thetheoremisproved.
Thetheorempermits thedecisionmaker todeďŹnethedecreasedsizeof theSCmodel, suchthat
thedecreasednumberof the layers in theSCmodel is sufďŹcient forplanningandcoordinating the
knowledgeabout therisks in therelationsbetweentheSCcomponents,without the lossofessential
informationabout therisks.
4. Entropy-BasedAlgorithmforComplexityAssessment
Thissectionsummarizes theoreticalďŹndingsof theprevioussections forobtainingadecreased
SC model on which planning and coordination of supplies can be done without loss of
essential information.
Inputdataof thealgorithm:
⢠thegivennumberNofriskdrivers,
⢠weight functions c(s) selectedbythedecisionmaker,
⢠probabilities pfprime(s) = Pr (Af prime(s)) that the riskdriver f is thedirect source of the supply
failure/delay in layer s,whicharecausedbyriskdriver f,
⢠probabilities pfsecond(s) = Pr (Af second(s)) that the risk driver f is the source of the supply
failure/delay in layer s, which is a result of the indirect effect on the f by the risk drivers f â˛
ofsupplydelay in layer s+1; theseprobabilitiesare termedas secondary.
⢠transitionprobabilitymatricesM(2)(s)= (
p(2)ij (s) )
NĂN , s=0,1,2, . . . ,k.
Step1.UsingtheentropyFormulas (11)â(17), calculateentropyof the layer0:
Hâ(L0)=â N
â
j=1 pj(0) log (
pj(0) )
H(0)=Hâ(L0)
Step2.UsingFormulas (2)â(9), compute thematrix
MË(1)(0), andvectorp(1)=(p1(1),p2(1), . . .pN(1))
Step3.Compute thecorrectedvectorofprobabilities for the layerL0,usingFormula
pc(0)=p(1) ¡MË(1)(0)andcorrectedentropyfor layerL0
HCâ(L0)=â N
â
j=1 pcj(0) log (
pcj(0) )
Step 4. For s=2,3, . . . , usingmatrix MË(1)(s), vector p(s) = (p1(s),p2(s), . . .pN(s)) compute
sequentially thecorrectedvectorsofprobabilities for the layersLsâ1: pc(sâ1)=p(s) ¡MË(1)(sâ1)
Step5.ComputeHCâ(Lsâ1)=â N
â
j=1 pcj(sâ1) log (
pcj(sâ1) )
Step6.Compute
H(0)âH(1)=
= (
1â c(1)c(0) )
H(0)âc(1)(Hâ(L1)âHCâ(L0))+c(1) N
â
i=1 (
pi(1) ¡ N
â
j=1 pË(1)ij (0) ¡ log (
pË(1)ij (0) ))
173
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Algorithms for Scheduling Problems
- Title
- Algorithms for Scheduling Problems
- Authors
- Frank Werner
- Larysa Burtseva
- Yuri Sotskov
- Editor
- MDPI
- Location
- Basel
- Date
- 2018
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-03897-120-7
- Size
- 17.0 x 24.4 cm
- Pages
- 212
- Keywords
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Categories
- Informatik
- Technik