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博文

目前显示的是标签为“Scheduling”的博文

Multiband Scheduler for Future Communication Systems

Read  full  paper  at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=1#.VNMpoizQrzE Author(s)   Klaus DOPPLER , Carl WIJTING , Tero HENTTONEN , Kimmo VALKEALAHTI Affiliation(s) Radio Communications CTC, Nokia Research Center, Helsinki, Finland . Radio Communications CTC, Nokia Research Center, Helsinki, Finland . Radio Communications CTC, Nokia Research Center, Helsinki, Finland . Klaus DOPPLER, Carl WIJTING, Tero HENTTONEN, Kimmo VALKEALAHTI . ABSTRACT Operation in multiple frequency bands simultaneously is an important enabler for future wireless communication systems. This article presents a new concept for scheduling transmissions in a wireless radio system operating in multiple frequency bands: the Multiband Scheduler (MBS). The MBS ensures that the operation in multiple bands is transparent to higher network layers. Special attention is paid to achieving low delay and latency when operating the system in the multiba...

Single Machine Scheduling with Time-Dependent Learning Effect and Non-Linear Past-Sequence-Dependent Setup Times

Read  full  paper  at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53571#.VMnXpSzQrzE Author(s)    Yuling Yeh 1 , Chinyao Low 2 , Wen-Yi Lin 2 Affiliation(s) 1 Department of Marketing and Logistics, Nan Kai University of Technology, Nantou, Chinese Taipei . 2 Institute of Industrial Engineering and Management, National Yunlin University of Science and Technology, Douliou, Chinese Taipei . ABSTRACT This paper studies a single machine scheduling problem with time-dependent learning and setup times. Time-dependent learning means that the actual processing time of a job is a function of the sum of the normal processing times of the jobs already scheduled. The setup time of a job is proportional to the length of the already processed jobs, that is, past-sequence-dependent (psd) setup time. We show that the addressed problem remains polynomially solvable for the objectives, i.e., minimization of the total completion time and minimiza...

No-Wait Flowshops to Minimize Total Tardiness with Setup Times

Read  full  paper  at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=53285#.VLx3ecnQrzE Author(s)    Tariq Aldowaisan , Ali Allahverdi Affiliation(s) Department of Industrial and Management Systems Engineering, Kuwait University, Kuwait City, Kuwait . ABSTRACT The m -machine no-wait flowshop scheduling problem is addressed where setup times are treated as separate from processing times. The objective is to minimize total tardiness. Different dispatching rules have been investigated and three were found to be superior. Two heuristics, a simulated annealing (SA) and a genetic algorithm (GA), have been proposed by using the best performing dispatching rule as the initial solution for SA, and the three superior dispatching rules as part of the initial population for GA. Moreover, improved versions of SA and GA are proposed using an insertion algorithm. Extensive computational experiments reveal that the improved versions of ...

JIT Mixed-Model Sequencing Rules: Is There a Best One?

Read  full  paper  at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=52930#.VKyjFcnQrzE Author(s)     Patrick R. McMullen Affiliation(s) School of Business, Wake Forest University, Winston-Salem, North Carolina, USA . ABSTRACT This research effort compares four sequencing rules intended to smooth production scheduling for mixed-model production systems in a Just-in-Time/Lean manufacturing environment (“JIT” hereafter). Each rule intends to schedule mixed-model production in such a way that manufacturing flexibility is optimized in terms of system utilization, units completed, average in-process inventory, average queue length, and average waiting time. A simulation experiment, where the various sequencing rules are tested against each other in terms of the above production measures, shows that three of the sequencing rules essentially offer the same performance, whereas one of them shows more variation.   ...