Page - 53 - in Algorithms for Scheduling Problems
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Algorithms 2018,11, 80
Theorem2. Suppose, in the iterationof thebinarysearchprocedurewith trialδ, all thebinsare scheduledat
Phase1, i.e.,no IA(b2)atPhase1arises. Then, a complete schedule inwhich the latenessofno job isgreater than
L(δ) is constructed in timeO(n2 logn). This schedule isPareto sub-optimal, and it isParetooptimal ifno idle
time interval inanybin is created.
Proof. For every bin B, since no IA(b2) during its construction at Phase 1 arises, this phase
will output a partial schedule for that bin, in which the lateness of no job is greater than L(δ)
(Lemmas 2 and 3). At the same time, by our construction, no kernel jobmay result in a lateness
greater thanL(δ). Then, the maximumjoblateness in theresultantcompleteschedule isnogreater
thanL(δ). This schedule isParetosub-optimaldueto theDNFheuristics (withaknownsub-optimal
approximationratio). Furthermore, ifno idle time interval inanybin iscreated, theremayexistno
feasiblesolutionwith themaximumjob latenessL(δ) suchthat the latest scheduled job in it completes
earlier than in theabovegeneratedsolution;hence, it isParetooptimal.
Let us now estimate the cost of Phase 1. For the purpose of this estimation, suppose n1
(n2, respectively) is thenumberofy-jobs (x-jobs, respectively). In thefirstpass,y-jobsarescheduledby
theEDheuristics in timeO(n1 logn1) if all the scheduledbinsarey-complete. Otherwise, sinceno
IA(b2)arises, each timetheschedulingofabincannotbecompletedbyall they-jobs,anewkernel
is declared that includes the corresponding y-jobs (Lemma1). Since theremay arise less than n1
different kernels, thenumber of times anewkernelmayarise is less thann1. Thus, the same job
willberescheduledless thann1 times (asabinorakernel job),whichyields the timecomplexityof
O(n21 logn1) for thefirst pass. Since the time complexityof theDNFheuristics is the sameas that
ofEDheuristicsusedat thefirstpassandnewkernelsaresimilarlydeclared(Lemma3), thecostof
thesecondpass issimilarlyO(n22 logn2). Thus, theoverall cost for theconstructionofall thebins is
O(n2 logn),which is also the cost of theoverall schemesince thekernel jobs are scheduledby the
EDheuristics.
Notethatthesecondconditionintheabovetheoremissufficient,butnotnecessaryforminimizing
themakespan; i.e., somebins in a feasible schedule respecting threshold L(δ)mayhave idle time
intervals, but that schedulemayminimize themakespan. In general, we rely on the efficiency
of theheuristicsusedforpackingourbins for theminimizationof themakespancriterion.Wealso
straightforwardlyobservethatbytheDNFheuristicss,nounscheduledjobmayfitwithinanyavailable
gapinanybinwithout forcingsomeother jobtosurpass thecurrentallowable latenessL(δ)atPhase1,
whereas theDNF-heuristicss tends tokeepreserved“short”unscheduled jobs.
Continuingwith the issueof thepracticalbehavior,aswehavementionedintheIntroduction,
the EDheuristics turns out to be an efficient tool for a proper distribution of the jobswithin the
bins. In [9], computationalexperimentswereconductedfor200randomlygenerated instancesofour
problem. For themajorityof these instances, the forceddelayofkernelK(S) in the initialED-schedule
S (i.e., thedifferencebetweenthecompletiontimeof thecorrespondingdelayingemerging joband
r(K))wasneglectable. Therefore, the latenessof thecorrespondingoverflowjobwaseitheroptimal
orveryclose to theoptimalmaximumjob lateness (asno jobofkernelK canbestartedearlier than
at time r(K) inanyfeasiblesolution).At thesametime,since theEDheuristicscreatesnoavoidable
machine idle time interval, theminimummakespan isalsoachieved. Thus, for themajorityof the
testedprobleminstances,bothobjectivecriteriaweresimultaneouslyminimizedor themaximumjobs
latenesswasveryclose to theoptimum,whereas themakespanwasoptimal.
Thecomputationalexperiments from[9]alsoyield that, formostof the tested instances,already
forsmalltrialδ’s, thebinswouldhavebeenscheduledinPhase1(i.e.,noIA(b2)wouldarise).Moreover,
therewill benoavoidable gap leftwithin thebins for these δ’s. In otherwords, the conditions in
Theorem2wouldbesatisfied, andPhase1wouldsuccessfully completed. In theory,however,we
cannotguaranteesuchabehavior,andweproceedwithourconstruction inPhase2.
53
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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