Achraf Jabeur Telmoudi

Parameter estimation of nonlinear systems using a robust possibilistic c-regression model algorithm

This article studies the problem of inappropriate parameter estimation for nonlinear system when the dataset is contaminated by noise based on fuzzy c-regression models. In comparison to the existing algorithms in the literature, the proposed method uses a generalized objective function that reduces the errors of partitioning datasets contaminated by noise, and as a consequence an accurate model is obtained. Indeed, it combines a modified version of possibilistic c-means procedure with fuzzy c-regression models. The weighted least squares method is exploited to identify the parameters contained in the consequent (THEN part). The results of this study demonstrate the effectiveness of the proposed method compared with other extended versions of the fuzzy c-regression model algorithm such as modified fuzzy c-regression model algorithm, possibilistic c-regression model and interval type-2 fuzzy c-regression model algorithm as well as other techniques existing in the literature.

Editorial to the special section on control and decision in engineering

The control and decision topics with applications in engineering present an interesting scope of research. The goal of the special issue is to present recent applied research works in control engineering and decisionmaking with applications in services and industrial cases. This special issue is a selection of extended versions of best presented papers in the 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT’17) took place in Barcelona, Spain, from 3 April 2017 to 5 April 2017. CoDIT’17 Conference was organized by ‘‘Universitat Politècnica de Catalunya,’’ with technical sponsorship of different IEEE entities: IEEE Systems, Man, and Cybernetics Society (IEEE SMC), the IEEE Control Systems Society (IEEE CSS), the IEEE Spain Section. CoDIT’17 Program Committee received around 423 papers from 64 countries worldwide that yielded 204 valid papers. The acceptance rate for this conference was around 48%. The authors from all continents presented their original work, in all areas of Control, Optimization, Decision, Engineering, Computer Science, and Information Technologies. Based on recommendation of program co-chairs and sessions chairs, several authors of excellent conference papers were contacted by the guest editors to submit an extended version for this special issue. The proceedings of CoDIT’17 are published via IEEE Xplore on this link: https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?pu number=8091148 We appreciate the recommendations of the CoDIT’17 Program Committee members for the selection of suitable conference papers for the journal publication. All submitted papers for the special Issue CoDIT’17 had to undergo the strict peer-review process managed by the Editor-in-Chief of JSCE with the collaboration with the Guest Editors. After a thorough reviewing procedure, with the help of external experts, 12 papers were selected. We especially want to acknowledge all the authors for their efforts in updating their contributions and submitting high-quality articles. We are also very grateful to the reviewers for their helpful and constructive comments. Our great gratitude goes to the Editor-inChief of the Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, Professor M. Necip Sahinkaya, for supporting the publication of this special issue. Special thanks go to the editorial staff for the excellent handling of the entire publication process.

Stochastic cases of the dynamic job shop problem based on the genetic algorithm to minimize

Up to now, the majority of researches on scheduling assume the difficulty of scheduling the job shop manufacturing system, especially the Dynamic Job Shop Scheduling Problem (DJSSP) which is the main purpose of our contribution. Looking to minimize the makespan value (Cmax), setup times and precedence constraints are considered. During the did work, we have proposed an inspired Genetic Algorithm(GA) approach based on the genetic operators to solve the DJSSP. The main task of the proposed method is to show how efficiently the system will schedule dynamically the new jobs. Eventually, the DJSSP based GA approach is proven to be successfully solved through experimental results. Hence, this later make it possible to generate minimal makespan values compared to the well known priority dispatching rules.

Reconfigurable manufacturing system: Overview and proposition of new approach

To confront the rapid change in manufacturing environments, companies need a Reconfigurable Manufacturing System (RMS) which combines the high production volume of Dedicated Manufacturing Lines (DML) with the large variety of products of Flexible Manufacturing System (FMS). In this paper, we propose a state of art of recent research work on RMS. After highlighting the advantages of RMS compared to existing manufacturing systems, we outline different issues discussed in the context of RMSs and what it brings as solutions. Finally we present the main idea of a new approach for reconfiguration process following a predictive monitoring.

PSO for Job-Shop Scheduling with Multiple Operating Sequences Problem - JS

This paper focus on a complex problem of job shop scheduling where each jobs have a multiple possible operations sequences. The resolution of this type of problem has not been treated in the literature. To solve this, a new algorithm based on Particle Swarm Optimization Global Velocity (PSOVG) was proposed. The objectif is to minimize the makespan. The simulation results show the efficiency of our proposed approach.