5,519 research outputs found
Design and Analysis of an Estimation of Distribution Approximation Algorithm for Single Machine Scheduling in Uncertain Environments
In the current work we introduce a novel estimation of distribution algorithm
to tackle a hard combinatorial optimization problem, namely the single-machine
scheduling problem, with uncertain delivery times. The majority of the existing
research coping with optimization problems in uncertain environment aims at
finding a single sufficiently robust solution so that random noise and
unpredictable circumstances would have the least possible detrimental effect on
the quality of the solution. The measures of robustness are usually based on
various kinds of empirically designed averaging techniques. In contrast to the
previous work, our algorithm aims at finding a collection of robust schedules
that allow for a more informative decision making. The notion of robustness is
measured quantitatively in terms of the classical mathematical notion of a norm
on a vector space. We provide a theoretical insight into the relationship
between the properties of the probability distribution over the uncertain
delivery times and the robustness quality of the schedules produced by the
algorithm after a polynomial runtime in terms of approximation ratios
Using Differential Evolution for the Graph Coloring
Differential evolution was developed for reliable and versatile function
optimization. It has also become interesting for other domains because of its
ease to use. In this paper, we posed the question of whether differential
evolution can also be used by solving of the combinatorial optimization
problems, and in particular, for the graph coloring problem. Therefore, a
hybrid self-adaptive differential evolution algorithm for graph coloring was
proposed that is comparable with the best heuristics for graph coloring today,
i.e. Tabucol of Hertz and de Werra and the hybrid evolutionary algorithm of
Galinier and Hao. We have focused on the graph 3-coloring. Therefore, the
evolutionary algorithm with method SAW of Eiben et al., which achieved
excellent results for this kind of graphs, was also incorporated into this
study. The extensive experiments show that the differential evolution could
become a competitive tool for the solving of graph coloring problem in the
future
A bounded-search iterated greedy algorithm for the distributed permutation flowshop scheduling problem
As the interest of practitioners and researchers in scheduling in a multi-factory environment is growing, there is an increasing need to provide efficient algorithms for this type of decision problems, characterised by simultaneously addressing the assignment of jobs to different factories/workshops and their subsequent scheduling. Here we address the so-called distributed permutation flowshop scheduling problem, in which a set of jobs has to be scheduled over a number of identical factories, each one with its machines arranged as a flowshop. Several heuristics have been designed for this problem, although there is no direct comparison among them. In this paper, we propose a new heuristic which exploits the specific structure of the problem. The computational experience carried out on a well-known testbed shows that the proposed heuristic outperforms existing state-of-the-art heuristics, being able to obtain better upper bounds for more than one quarter of the problems in the testbed.Ministerio de Ciencia e Innovación DPI2010-15573/DP
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