agglomerate
发表于 2025-3-28 15:50:47
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抗体
发表于 2025-3-28 19:23:32
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共同给与
发表于 2025-3-29 00:44:49
Experimental and Efficient Algorithms978-3-540-24838-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
GROSS
发表于 2025-3-29 04:35:19
0302-9743 Overview: Includes supplementary material: 978-3-540-22067-1978-3-540-24838-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
踉跄
发表于 2025-3-29 10:38:34
Tomotoshi Marumoto,Hideyuki Sayanimize their makespan, i.e., the maximum processing time over all processors. We propose a new heuristic for solving the multiprocessor scheduling problem, based on a hybrid heuristic to the bin packing problem. Computational results illustrating the effectiveness of this approach are reported and c
BIDE
发表于 2025-3-29 12:20:12
Kirsten Hattermann,Rolf Mentleinees, we propose new heuristics (local search and metaheuristics) in which edge swaps are iteratively applied to a current spanning tree. Structural properties that make the heuristics efficient are established. We also present a mixed integer programming formulation of the problem whose linear relax
ADAGE
发表于 2025-3-29 15:41:02
Raymond Y. Huang,Patrick Y. Wen.Several approaches for solving stochastic problems are reported in the literature. Metaheuristics seem to be a powerful tool for computing good and robust solutions. However, the efficiency of algorithms based on Local Search, such as Tabu Search, suffers from the complexity of evaluating the objec
semble
发表于 2025-3-29 21:54:55
Recent Results in Cancer Researchhis application. The vast majority of clustering methods in literature operate by resorting to a priori assumptions about the data, such as the number of cluster or cluster radius. Clusters are forced to conform to these assumptions, which may not be valid for the considered population. The latter c
Fibrinogen
发表于 2025-3-30 01:06:44
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Discrete
发表于 2025-3-30 05:53:51
Astrid Weyerbrock,Josef Zentnerre efficient on several types of problems or instances. We can distinguish exact methods dedicated to solve small instances, from heuristics – and particularly metaheuristics – that approximate best solutions on large instances. In this article, we firstly present an efficient exact method, called t