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Yalaoui N.,Caillau Company | Mahdi H.,Caillau Company | Amodeo L.,University of Technology of Troyes | Yalaoui F.,University of Technology of Troyes
2011 International Conference on Communications, Computing and Control Applications, CCCA 2011 | Year: 2011

In the hybrid flow shop problems, two decisions are taken at once: determine the allocation of jobs to the parallel machines as well as the sequence of the jobs assigned to each machine. In the addressed paper, we consider a hybrid flow shops problem under particular conditions. Indeed, we assume that the assignment of the jobs on the machines is known at advance and some jobs can not pass by certain stages. The objective function is to minimize the total tardiness. We proposed in previous works an exact resolution based on the mixed integer linear programming method solved by Cplex solver. We propose in this paper two metaheuristic methods such as a genetic algorithm and a particle swarm algorithm. Those are under fuzzy logic-controller. The obtained results are very interesting. © 2011 IEEE. Source


Yalaoui N.,Caillau Company | Amodeo L.,University of Technology of Troyes | Yalaoui F.,University of Technology of Troyes | Mahdi H.,Caillau Company
Journal of Intelligent and Fuzzy Systems | Year: 2014

In order to apply a continuous improvement of their production systems, all managers are always trying to improve their production systems by optimizing their scheduling method. This involves the search for efficient methods for obtaining the best results according to the costs and the delay criteria for example. This paper aims to solve a specific hybrid reentrant flow shop scheduling problem. This one contains some stages with some identical parallel machines. The orders sequenced are splited in batches. Each batch is processed one or more time on the system. For the resolution, various methods have been developed. An exact method which lists all the solutions and selects the best one and approximated methods such as the genetic algorithm (GA), the genetic algorithm under fuzzy controller (FLCGA), the particle swarm optimization (PSO) and the particle swarm optimization under fuzzy controller (FLCPSO). The results discussed in the paper are very interesting. © 2014 - IOS Press and the authors. All rights reserved. Source


Yalaoui N.,University of Technology of Troyes | Yalaoui N.,Caillau Company | Mahdi H.,Caillau Company | Amodeo L.,University of Technology of Troyes | Yalaoui F.,University of Technology of Troyes
Journal of Intelligent Manufacturing | Year: 2011

In this paper we solve a combined group technology problem with a facility layout problem (FLP). This new approach is called T-FLP. We have developed a hybrid algorithm containing three main steps. The first one, called MPGV (Machine Part Grouping with Volume) is a decomposition method that can create families of product and machine groups based on a volume data matrix. The second one consists on assigning machines to fixed locations, using as a constraint, the solution of theMPGV. This problem is solved as a Quadratic Assignment Problem (QAP). In the third step, we make a global evaluation of all the solutions. A loop on cells is performed using a minimum and maximum number of cells. This loop can choose the appropriate number of cells based on the best solution of a global evaluation. The hybrid algorithm is implemented with two different rules for taking into account the constraint of the MPGV solution. This has generated twomethods calledYMAY1 and YMAY2. In the MPGV we use a data oriented genetic algorithm. The QAP is solved with an Ant Colony Optimization mixed with a Guided Local Search (ACOGLS). This method has been used to solve a real industrial case. For estimating the efficiency of our method, we have compared our results with an optimal solution obtained by complete enumeration (an exact method). © Springer Science+Business Media, LLC 2009. Source


Yalaoui N.,University of Technology of Troyes | Yalaoui N.,Caillau Company | Dugardin F.,University of Technology of Troyes | Yalaoui F.,University of Technology of Troyes | And 2 more authors.
Studies in Fuzziness and Soft Computing | Year: 2010

This book lights out the different improvement of the recent history of fuzzy logic. The present chapter deals with the connections that exist between fuzzy logic and production scheduling. Production scheduling is a part of operational research which relies on combinatorial optimization solved by discrete methods. This large area covers several well-known combinatorial problems: vehicle routing problem (in which several vehicle must visit customers at once), scheduling problem [18] (explained in section 2 of this chapter), bin-packing problem (where piece must be placed in a rectangle), assignment problem (where piece must be assign to machine while optimizing a criterion). These short number of both theoretical and practical problems are persistent in numerous technical areas: transportation (flights, trucks, ships, auto-guided-vehicles), shop scheduling, surgery operating theater, layout of warehouse, landing/takeoff runway scheduling, timetable. © 2010 Springer-Verlag Berlin Heidelberg. Source


Yalaoui N.,University of Technology of Troyes | Yalaoui N.,Caillau Company | Ouazene Y.,University of Technology of Troyes | Yalaoui F.,University of Technology of Troyes | And 2 more authors.
International Journal of Production Research | Year: 2013

This paper deals with a particular version of the hybrid flow shop scheduling problem inspired from a real application in the automotive industry. Specific constraints such as pre-assigned jobs, non-identical parallel machines and non-compatibility between certain jobs and machines are considered in order to minimise the total tardiness time. A mixed-integer programming model that incorporates these aspects is developed and solved using ILOG Cplex software. Thus, because of the computation time constraint, we propose approximate resolution methods based on genetic and particle swarm optimisation algorithms coupled or not with fuzzy logic control. The effectiveness of these methods is investigated via computational experiments based on theoretical and real case instances. The obtained results show that fuzzy logic control improves the performances of both genetic and particle swarm optimisation algorithms significantly. © 2013 Taylor and Francis. Source

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