Page - 36 - in Algorithms for Scheduling Problems
Image of the Page - 36 -
Text of the Page - 36 -
Algorithms 2018,11, 66
classesofharderproblems,wherethe jobpermutationĎkâSconstructedbyAlgorithm3outperforms
themid-pointpermutationĎmidâpandthepermutationĎmax constructedbyAlgorithmMAX-OPTBOX
derived in [15].AlgorithmMAX-OPTBOXandAlgorithm3werecodedinC#andtestedonaPCwith
IntelCore (TM)2Quad,2.5GHz,4.00GBRAM.Forall tested instances, inequalities pLi < p U
i hold
forall jobs JiâJ . Table2presents computational results for randomlygenerated instancesof the
problem1|pLi ⤠pi⤠pUi |âCiwithnâ{100,500,1000,5000,10,000}. Thesegmentsof thepossible
processingtimeshavebeenrandomlygeneratedsimilar to that in [15].
An integer centerCof thesegment [pLi ,p U
i ]wasgeneratedusingauniformdistribution in the
range [1,100]. ThelowerboundpLi ofthepossibleprocessingtimepiwasdeterminedusingtheequality
pLi =C ¡(1â δ100).Hereafter,δdenotes themaximal relativeerrorof theprocessingtimes pidueto the
givensegments [pLi ,p U
i ]. Theupperbound p U
i wasdeterminedusingtheequality p U
i =C ¡(1+ δ100).
Then, foreach job JiâJ , thepoint piwasgeneratedusingauniformdistributionintherange [pLi ,pUi ].
In order to generate instances,where all jobsJ belong to a single block, the segments [pLi ,pUi ]of
thepossibleprocessingtimesweremodiďŹedas follows: [pËLi , pË U
i ]= [p L
i +pâpi, pUi +pâpi],where
p=maxni=1pi. Since the inclusion p â [pËLi , pËUi ]holds, each constructed instance contains a single
block, |B|=1. Themaximumabsoluteerrorof theuncertainprocessingtimes pi, JiâJ , isequal to
maxni=1(p U
i âpLi ), andthemaximumrelativeerrorof theuncertainprocessingtimes pi, JiâJ , isnot
greater than2δ%.Wesaythat these instancesbelongtoclass1.
Similarlyas in [15], threedistribution lawswereused inourexperiments todetermine the factual
processingtimesof the jobs. (Weremindthat if inequality pLi < p U
i holds, thenthe factualprocessing
timeof the job Jibecomesknownaftercompletingthe job Ji.)Wecall theuniformdistributionas the
distribution lawwithnumber1, thegammadistributionwith theparametersι=9andβ=2as the
distribution lawwithnumber2andthegammadistributionwith theparametersι=4andβ=2as
thedistribution lawwithnumber3. Ineach instanceofclass1, forgeneratingthe factualprocessing
times fordifferent jobsof thesetJ , thenumberof thedistribution lawwasrandomlychosenfromthe
set{1,2,3}.Wesolved15seriesof therandomlygenerated instances fromclass1. Eachseriescontains
10 instanceswith thesamecombinationofnandδ.
In theconductedexperiments,weansweredthequestionofhowlarge theobtainedrelativeerror
Î= ÎłkpââÎłtpâ
Îłtpâ ¡100%of thevalueÎłkpâ of theobjective functionÎł=âni=1Ciwasfor thepermutationĎk
withtheminimalvalueofF(Ďk,T)withrespect to theactuallyoptimalobjective functionvalueÎłtpâ
calculatedfor the factualprocessingtimes pâ=(pâ1,p â
2, . . . ,p â
n)âT.Wealsoansweredthequestion
ofhowsmall theobtainedrelativeerrorÎof thevalueÎłkpâ of theobjective functionâCiwasfor the
permutationĎkwiththeminimalvalueofF(Ďk,T).WecomparedtherelativeerrorÎwiththerelative
errorÎmidâpof thevalueÎłmpâ of theobjective functionâCi for thepermutationĎmidâpobtainedfor
determining the jobprocessing times using themid-points of the given segments. We compared
the relative errorÎwith the relative errorÎmax of the value of the objective functionâCi for the
permutationĎmax constructedbyAlgorithmMAX-OPTBOXderivedin [15].
Thenumbernof jobs in the instance isgiven incolumn1inTable2. Thehalfof themaximum
possible errors δ of the randomprocessing times (inpercentage) is given in column2. Column3
gives the average errorÎ for the permutation Ďk with theminimal value of F(Ďk,T). Column 4
presents theaverageerrorÎmidâpobtainedfor themid-pointpermutationsĎmidâp,whereall jobsare
orderedaccordingto increasingmid-pointsof their segments.Column5presents theaveragerelative
perimeterof theoptimalityboxOB(Ďk,T) for thepermutationĎkwith theminimalvalueofF(Ďk,T).
Column6presents therelationÎmidâp/Î. Column7presents therelationÎmax/Î. Column8presents
theaverageCPU-timeofAlgorithm3for thesolvedinstances inseconds.
Thecomputationalexperimentsshowedthat forall solvedexamplesofclass1, thepermutations
ĎkwiththeminimalvaluesofF(Ďk,T) for theiroptimalityboxesgeneratedgoodobjective function
valuesÎłkpâ,whicharesmallerthanthoseobtainedforthepermutationsĎmidâpandforthepermutations
36
back to the
book Algorithms for Scheduling Problems"
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