简介：Thispaperconsiderssingle-machineschedulingproblemsingrouptechnologywiththejobs'processingtimesbeingsimplelinearfunctionsoftheirstarttimes.Theobjectivefunctionsaretheminimizingofmakespanandtotalweightedcompletiontime.SomeoptimalconditionsandalgorithmsaregivenandthefactthattheproblemoftotalweightedcompletiontimesisNP-hardisproved.

简介：Thispaperstudiesonlineschedulingofjobswithkindreleasetimesonasinglemachine.Here“kindreleasetime”meansthatinonlinesetting,nojobscanbereleasedwhenthemachineisbusy.EachjobJhasakindreleasetimer(J)≥0,aprocessingtimep(J)>0andadeadlined(J)>0.Thegoalistodetermineaschedulewhichmaximizestotalprocessingtime(∑p(J)E(J))ortotalnumber(∑E(J))oftheacceptedjobs.Forthefirstobjectivefunction∑p(J)E(J),wefirstpresentalowerbound√2,andthenprovideanonlinealgorithmLEJwithacompetitiveratioof3.Thisisthefirstdeterministicalgorithmfortheproblemwithaconstantcompetitiveratio.Whenp(J)∈{1,k},k>1isarealnumber,wefirstpresentalowerboundminf(1+k)/k,2k/(1+k)g,andthenweshowthatLEJhasacompetitiveratioof1+┌k┐=k.Inparticular,whenalltheklengthjobshavetightdeadlines,wefirstpresentalowerboundmax{4=(2+k),1}(for∑p(J)E(J))and4/3(for∑E(J)).ThenweprovethatLEJis┌k┐/k-competitivefor∑p(J)E(J)andweprovideanonlinealgorithmHwithacompetitiveratioof2┌k┐/(┌k┐+1)forthesecondobjectivefunction∑E(J).

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简介：为无线特定的网络的大量应用程序是时间批评的并且在通讯潜伏上强加紧要求。关键通讯操作之一是从一个来源节点广播一条消息。这篇论文学习在没有碰撞的传播模型下面在无线特定的网络安排问题的最小的潜伏广播。为这个NP难的问题的以前最好的已知的算法生产其潜伏是至少648的一张广播时间表(r最大/rmin)2时间最佳的时间表，在哪儿r最大和rmin分别地是在一个网络的节点的最大、最小的传播范围。我们显著地由至多建议表演比率是其近似的一个新安排算法改进这结果(1+2r最大/rmin)2+32。而且，在建议安排下面，每个节点就需要至多提交一条消息一次。

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简介：Inthispaper,weconsiderParallelMachinesSchedulingwithnonsimultaneousmachineavailabletime.WegivetheexactworstcaseperformanceboundofMLPTproposedbyLee.Furthermore,twoothermodifiedLPTalgorithmsarediscussed.Thepaperisendedbynumericalex-perimentsofthesealgorithms.

简介：Thispaperconsidersaschedulingproblemintwo-stagehybridflowshop,wherethefirststageconsistsoftwomachinesformedanopenshopandtheotherstagehasonlyonemachine.Theobjectiveistominimizethemakespan,i.e.,themaximumcompletiontimeofalljobs.WefirstshowtheproblemisNP-hardinthestrongsense,thenwepresenttwoheuristicstosolvetheproblem.Computationalexperimentsshowthatthecombinedalgorithmofthetwoheuristicsperformswellonrandomlygeneratedprobleminstances.

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