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Algorithms 2018,11, 66 where thepermutationĻ€t∈ S isoptimal for the factual scenario pāˆ— ∈Tof the jobprocessing times. We call the scenario pāˆ— = (pāˆ—1,p āˆ— 2, . . . ,p āˆ— n) ∈ T factual, if pi is equal to the exact time needed for processing the job Ji ∈J . The factualprocessing time pāˆ—i becomesknownafter completing the job Ji∈J . If thepermutationĻ€k=(Ļ€k1,Ļ€k2, . . . ,Ļ€kn)∈Shasthenon-emptyoptimalityboxOB(Ļ€k,T) =āˆ…, thenonecancalculate the followingerror function: F(Ļ€k,T)= n āˆ‘ i=1 ( 1āˆ’ uāˆ—kiāˆ’ lāˆ—ki pUkiāˆ’pLki ) (nāˆ’ i+1) . (7) A value of the error function F(Ļ€k,T) characterizes how the objective function value of āˆ‘ni=1Ci(Ļ€k,p āˆ—) for thepermutationĻ€kmaybeclose to theobjective functionvalueofāˆ‘ni=1Ci(Ļ€t,pāˆ—) for thepermutationĻ€t∈S,which isoptimal for thefactualscenario pāˆ— ∈Tof the jobprocessingtimes. ThefunctionF(Ļ€k,T)characterizes the followingdifference n āˆ‘ i=1 Ci(Ļ€k,pāˆ—)āˆ’ n āˆ‘ i=1 Ci(Ļ€t,pāˆ—), (8) whichhas tobeminimizedforagoodapproximatesolutionĻ€k to theuncertainproblem1|pLi ≤ pi≤ pUi |āˆ‘Ci. ThebetterapproximatesolutionĻ€k to theuncertainproblem1|pLi ≤ pi≤ pUi |āˆ‘Ciwillhave asmallervalueof theerror functionF(Ļ€k,T). The formula (7) is basedon the cumulative property of the objective functionāˆ‘ni=1Ci(Ļ€k,T), namely,eachrelativeerror ( 1āˆ’ u āˆ— ki āˆ’lāˆ—ki pUki āˆ’pLki ) >0obtaineddueto thewrongpositionof the job Jki in the permutationĻ€k is repeated (nāˆ’ i) times forall jobs,whichsucceedthe job Jki in thepermutationĻ€k. Therefore,avalueof theerror functionF(Ļ€k,T)mustbeminimizedforagoodapproximationof the valueofāˆ‘ni=1Ci(Ļ€t,T) for thepermutationĻ€k. If the equalityPerOB(Ļ€k,T) = 0holds, the error function F(Ļ€k,T)has themaximalpossible valuedeterminedas follows: F(Ļ€k,T)=āˆ‘ni=1 ( 1āˆ’ u āˆ— ki āˆ’lāˆ—ki pUki āˆ’pLki ) (nāˆ’ i+1)=āˆ‘ni=1(nāˆ’ i+1)= n(n+1)2 . Thenecessaryandsufficientcondition for the largestvalueofF(Ļ€k,T)= n(n+1) 2 isgiven inTheorem3. If theequalityPerOB(Ļ€k,T)=nholds, theerror functionF(Ļ€k,T)has thesmallestvalueequal to 0: F(Ļ€k,T) = āˆ‘ni=1 ( 1āˆ’ u āˆ— ki āˆ’lāˆ—ki pUki āˆ’pLki ) (nāˆ’ i+1) = āˆ‘ni=1(1āˆ’1)(nāˆ’ i+1) = 0. The necessary and sufficientconditionforthesmallestvalueofF(Ļ€k,T)=0isgiveninTheorem4,wherethepermutation Ļ€k∈S isoptimal foranyfactual scenario pāˆ— ∈Tof the jobprocessingtimes. Due to evident modifications of the proofs given in Section 3.3, it is easy to prove that Theorems5and6remaincorrect in the followingforms. Theorem7. If the equality B1 = J holds, then it takesO(n) time to find the permutation Ļ€k ∈ S and optimalityboxOB(Ļ€k,T)with the smallestvalueof the error functionF(Ļ€k,T)amongall permutationsS. Theorem8. Let each block Br ∈ B contain atmost one non-fixed job. The permutation Ļ€k ∈ Swith the smallestvalueof the error functionF(Ļ€k,T)amongall permutationsS is constructed inO(n logn) time. Using Theorems 7 and 8, we developed the modifications of Procedures 3 and 4 for the minimizationof thevalues of F(Ļ€k,T) insteadof themaximizationof thevalues ofPerOB(Ļ€k,T). ThederivedmodificationsofProcedures3and4arecalledProcedures3+F(Ļ€k,T)and4+F(Ļ€k,T), respectively.WemodifyalsoProcedures2and5forminimizationof thevaluesofF(Ļ€k,T) instead of themaximizationof thevaluesofPerOB(Ļ€k,T). ThederivedmodificationsofProcedures2and5 arecalledProcedures2+F(Ļ€k,T)and 5+F(Ļ€k,T), respectively. Basedontheprovenpropertiesof the 34
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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
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