Abstract
Uncertainties in deviations of physical properties lead to a probabilistic failure analysis of the composite materials. The proposed optimization model for laminate composites is based on reliability analysis considering the ultimate failure state. To avoid difficulties associated with the complete analysis of the failure modes, bounds are established for the failure probability of the structural system. These bounds are related with the intact and degraded configurations of the structure. Using the first ply failure and the last ply failure theories and a degradation model for the mechanical properties with load sharing rules we obtain the failure probabilities corresponding to the two above configurations. The failure probability of each configuration is obtained using level 2 reliability analysis and the Lind-Hasofer method. The optimization algorithm is developed based on the problem decomposition into three subproblems having as objectives the maximization of the structural efficiency at intact and degraded configurations of the structure and weight minimization subjected to allowable values for the structural reliability. Additionally, the search for the initial design is performed introducing a weight minimization level. It is expected to explore the remaining load capacity of the structures after first ply failure as a function of the anisotropic properties of the composites. The design variables are the ply angles and the thicknesses of the laminates. The structural analysis for the model developed is performed through the finite element method mainly using the isoparametric degenerated shell finite element. The sensitivities are obtained using the discrete approach through the adjoint variable method. In order to show the performance of the analysis two examples are presented.

Abstract
In this paper, we consider the single machine scheduling problem with linear earliness and quadratic tardiness costs. and no machine idle time. We propose a genetic approach based on a random key alphabet. Several genetic algorithms based on this approach are presented. These versions differ on the generation of the initial population, as well as on the use of local search. The proposed procedures are compared with existing heuristics, as well as with optimal solutions for the smaller instance sizes. The computational results show that the performance of the proposed genetic approach is improved by the addition of a local search procedure, as well as by the insertion of simple heuristic solutions in the initial population. Indeed, the genetic versions that include either or both of these features not only provide significantly better results, but are also much faster. The genetic versions that use local search are clearly superior to the existing heuristics, and the improvement in performance over the best existing procedure increases with both the size and difficulty of the instances. These genetic procedures are also quite close to the optimum, and provided an optimal solution for most of the test instances.

Abstract
In this paper, we present a hybrid genetic algorithm for a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys. The genetic algorithm is used to establish the order in which the jobs are initially scheduled, and a local search procedure is subsequently applied to detect possible improvements. The approach is tested on a set of randomly generated problems and compared with existing efficient heuristic procedures based on dispatch rules and local search. The computational results show that this new approach, although requiring slightly longer computational times, is better than the previous algorithms in terms of solution quality.

Abstract
We address the single-source uncapacitated minimum cost network flow problem with general concave cost functions. Exact methods to solve this class of problems in their full generality are only able to address small to medium size instances, since this class of problems is known to be NP-Hard. Therefore, approximate methods are more suitable. In this work, we present a hybrid approach combining a genetic algorithm with a local search. Randomly generated test problems have been used to test the computational performance of the algorithm. The results obtained for these test problems are compared to optimal solutions obtained by a dynamic programming method for the smaller problem instances and to upper bounds obtained by a local search method for the larger problem instances. From the results reported it can be shown that the hybrid methodology improves upon previous approaches in terms of efficiency and also on the pure genetic algorithm, i.e., without using the local search procedure. (C) 2007 Wiley Periodicals, Inc.

Abstract
In this work we propose a multi-population genetic algorithm for tree-shaped network design problems using random keys. Recent literature on finding optimal spanning trees suggests the use of genetic algorithms. Furthermore, random keys encoding has been proved efficient at dealing with problems where the relative order of tasks is important. Here we propose to use random keys for encoding trees. The topology of these trees is restricted, since no path from the root vertex to any other vertex may have more than a pre-defined number of arcs. In addition, the problems under consideration also exhibit the characteristic of flows. Therefore, we want to find a minimum cost tree satisfying all demand vertices and the pre-defined number of arcs. The contributions of this paper are twofold: on one hand we address a new problem, which is an extension of the well known NP-hard hop-constrained MST problem since we also consider determining arc flows such that vertices requirements are met at minimum cost and the cost functions considered include a fixed cost component and a nonlinear flow routing component; on the other hand, we propose a new genetic algorithm to efficiently find solutions to this problem.