Preface

Over the last decades, it has become a strong need for exchange on common computational and algorithmic tools between researchers working in different application backgrounds. Under this situation, the first CESA conference (CESA96) was successfully held in Lille, France in July 1996. The Multiconference on “Computational Engineering in Systems Applications” (CESA2006), was co-sponsored by IMACS (the International Association for Mathematics and Computers in Simulation) and IEEE Systems Man and Cybernetics (IEEE/SMC) Society, and has been held on 4-6 October 2006 in Beijing, China. It aim was to bring together scholars and practitioners from academia and industries to exchange the latest development in theories, and applications of computational techniques. This Conference was co-chaired by Professor Pierre Borne (Ecole Centrale de Lille, France) and Professor Bo Zhang (Tsinghua University, China). In addition to the plenary lectures presented by Professor James M. Tien (Rensselaer Polytechnic Institute, USA), Professor Tianyou Chai (Northeastern University, China), Professor Florin G. Filip (Vice President of the Romanian Academy), Professor Jianwei Zhang (University of Hamburg, Germany) and Professor Toshio Fukuda (Nagoya University, Japan), 388 communications have been selected and accepted for presentation. The papers presented in this special issue correspond to enlarged and improved papers which have been selected among the best communications presented during the conferenc

Multi-objective Optimization For The Dynamic Multi-Pickup and Delivery Problem with Time Windows

The PDPTW is an optimization vehicles routing problem which must meet requests for transport between suppliers and customers satisfying precedence, capacity and time constraints. We present, in this paper, a genetic algorithm for multi-objective optimization of a dynamic multi pickup and delivery problem with time windows (Dynamic m-PDPTW). We propose a brief literature review of the PDPTW, present our approach based on Pareto dominance method and lower bounds, to give a satisfying solution to the Dynamic m-PDPTW minimizing the compromise between total travel cost and total tardiness time. Computational results indicate that the proposed algorithm gives good results with a total tardiness equal to zero with a tolerable cost.Comment: arXiv admin note: text overlap with arXiv:1101.339

A Proposed Genetic Algorithm Coding for Flow-Shop Scheduling Problems

A new genetic algorithm coding is proposed in this paper to solve flowshop scheduling problems. To show the efficiency of the considered approach, two examples, in pharmaceutical and agro-food industries are considered with minimization of different costs related to each problem as a scope. Multi-objective optimization is thus, used and its performances proved

A Taboo Search Optimization of the Control Law of Nonlinear Systems with Bounded Uncertainties

The aim of this paper is to propose a method to determine among the eligible controls of a nonlinear system, with bounded perturbations, the one which minimizes the final error. The approach is based on the implementation of aggregation techniques using vector norms in order to determine a comparison system used to calculate an attractor in view of its minimization by implementation of metaheuristics

A Neural Approach of Multimodel Representation of Complex Processes

The multimodel approach was recently developed to deal with the issues of complex processes modeling and control. Despite its success in different fields, it still faced with some design problems, and in particular the determination of the models and of the adequate method of validities computation. In this paper, we propose a neural approach to derive different models describing the process in different operating conditions. The implementation of this approach requires two main steps. The first step consists in exciting the system with a rich (e.g. pseudo random) signal and collecting measurements. These measurements are classified by using an adequate Kohonen self-organizing neural network. The second step is a parametric identification of the base-models by using the classification results for order and parameters estimation. The suggested approach is implemented and tested with two processes and compared to the classical modeling approach. The obtained results turn out to be satisfactory and show a good precision. These also allow to draw some interpretations about the adequate validities’ calculation method based on classification results