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首页> 外文期刊>European Journal of Operational Research >The just-in-time scheduling problem in a flow-shop scheduling system
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The just-in-time scheduling problem in a flow-shop scheduling system

机译:流水车间调度系统中的即时调度问题

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摘要

We study the problem of maximizing the weighted number of just-in-time (JIT) jobs in a flow-shop scheduling system under four different scenarios. The first scenario is where the flow-shop includes only two machines and all the jobs have the same gain for being completed JIT. For this scenario, we provide an O(n~3) time optimization algorithm which is faster than the best known algorithm in the literature. The second scenario is where the job processing times are machine-independent. For this scenario, the scheduling system is commonly referred to as a proportionate flow-shop. We show that in this case, the problem of maximizing the weighted number of JIT jobs is NP-hard in the ordinary sense for any arbitrary number of machines. Moreover, we provide a fully polynomial time approximation scheme (FPTAS) for its solution and a polynomial time algorithm to solve the special case for which all the jobs have the same gain for being completed JIT. The third scenario is where a set of identical jobs is to be produced for different customers. For this scenario, we provide an O(n~3) time optimization algorithm which is independent of the number of machines. We also show that the time complexity can be reduced to O(n log n) if all the jobs have the same gain for being completed JIT. In the last scenario, we study the JIT scheduling problem on m machines with a no-wait restriction and provide an O(mn~2) time optimization algorithm.
机译:我们研究了在四种不同情况下,在流水车间调度系统中最大化即时(JIT)作业的加权数量的问题。第一种情况是流水车间仅包括两台机器,并且所有作业对于完成JIT都具有相同的收益。对于这种情况,我们提供了O(n〜3)时间优化算法,该算法比文献中最知名的算法要快。第二种情况是作业处理时间与机器无关。对于这种情况,调度系统通常称为比例流水车间。我们证明,在这种情况下,对于任何数量的机器,最大化JIT作业的加权数量的问题在通常意义上都是NP-难的。此外,我们为其解决方案提供了一个完全多项式时间逼近方案(FPTAS),并提供了一种多项式时间算法来解决特殊情况,在这种情况下,所有作业具有相同的完成JIT的收益。第三种情况是将为不同的客户生产一组相同的作业。对于这种情况,我们提供了O(n〜3)时间优化算法,该算法与机器数量无关。我们还表明,如果所有作业都具有完成JIT的相同收益,则时间复杂度可以降低为O(n log n)。在最后一个场景中,我们研究了具有无等待限制的m台机器上的JIT调度问题,并提供了O(mn〜2)时间优化算法。

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