Hybrid flow shop scheduling problems have a special structure combining some elements of both the flow shop and the parallel machine scheduling problems. Multiprocessor task scheduling problem can be stated as finding...
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Hybrid flow shop scheduling problems have a special structure combining some elements of both the flow shop and the parallel machine scheduling problems. Multiprocessor task scheduling problem can be stated as finding a schedule for a general task graph to execute on a multiprocessor system so that the schedule length can be minimized. Hybrid Flow Shop Scheduling with Multiprocessor Task (HFSMT) problem is known to be NP-hard. In this study we present an effective parallel greedy algorithm to solve HFSMT problem. parallel greedy algorithm (PGA) is applied by two phases iteratively, called destruction and construction. Four constructive heuristic methods are proposed to solve HFSMT problems. A preliminary test is performed to set the best values of control parameters, namely population size, subgroups number, and iteration number. The best values of control parameters and operators are determined by a full factorial experimental design using our PGA program. Computational results are compared with the earlier works of Oguz et al. [1,3], and Oguz [2]. The results indicate that the proposed parallel greedy algorithm approach is very effective in terms of reduced total completion time or makespan (C-max) for the attempted problems. (C) 2010 Elsevier B. V. All rights reserved.
Pore networks can be simulated in silico by using the dual site-bond Model. In this approach, a set of cavities (sites) are interconnected to each other by means of a set of throats (bonds), while considering that eac...
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Pore networks can be simulated in silico by using the dual site-bond Model. In this approach, a set of cavities (sites) are interconnected to each other by means of a set of throats (bonds), while considering that each site should be always larger than any of its delimiting bonds. The NoMISS greedyalgorithm has been implemented recently in order to address this task;nevertheless, even if this procedure is relatively fast, there arises problems related to large memory consumption and long computing time, as pore networks become somewhat large. Here, three parallel methods are proposed to allow a proficient construction of large pore networks. The first method is a parallel Monte Carlo procedure, which applies a number of exchanges among pore sizes in order to obtain a valid pore network. The other two methods are parallel versions of the pioneering NoMISS greedyalgorithm. The first version uses a static data partitioning to speed up the running time, whilst the second applies a dynamic data distribution policy to improve the pore network quality. The obtained results show the behavior of each proposed version with respect to their performance and quality, by employing the resources of a 125-core Linux cluster.
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