Gilles Goncalves

A genetic approach to solving the problem of cyclic job shop scheduling with linear constraints

This study concerns the domain of cyclic scheduling. More precisely we consider the cyclic job shop scheduling problem with linear constraints. The main characteristic of this problem is that the tasks of each job are cyclic and are subjected to linear precedence constraints. First we review some approaches in the field of cyclic scheduling and present the cyclic job shop scheduling problem definition, which has an open complexity. Then we present a general approach for solving it, based on the coupling of a genetic algorithm and a scheduler. This scheduler utilises a Petri-net modelling the linear precedence constraints between cyclic tasks. The goal of this genetic algorithm is to propose an order of priority for jobs on the machines, to be used by the scheduler for solving resource conflicts. Finally a benchmark and some preliminary results of this approach are presented.

Cyclic scheduling for F.M.S : Modelling and evolutionary solving approach

This paper concerns the domain of flexible manufacturing systems (FMS) and focuses on the scheduling problems encountered in these systems. We have chosen the cyclic behaviour to study this problem, to reduce its complexity. This cyclic scheduling problem, whose complexity is NP-hard in the general case, aims to minimise the work in process (WIP) to satisfy economic constraints. We first recall and discuss the best known cyclic scheduling heuristics. Then, we present a two-step resolution approach. In the first step, a performance analysis is carried out; it is based on the Petri net modelling of the production process. This analysis resolves some indeterminism due to the systems flexibility and allows a lower bound of the WIP to be obtained. In the second step, after a formal model of the scheduling problem has been given, we describe a genetic algorithm approach to find a schedule which can reach the optimal production speed while minimizing the WIP. Finally, our genetic approach is validated and compared with known heuristics on a set of test problems.

Representation of extended RBAC model using UML language

This paper presents an extension of the standard role-based access control (RBAC) model together with its representation using the Unified Modeling Language (UML). The presented model is developed for the role engineering in the security of information system. The presented implementation of the RBAC model consists in role creation via defining appropriate permissions. The entire procedure is performed in two stages: defining the permissions assigned to a function and providing the definitions of functions assigned to a particular role.

Role engineering: From design to evolution of security schemes

This paper presents a methodology to design the RBAC (Role-Based Access Control) scheme during the design phase of an Information System. Two actors, the component developer and the security administrator, will cooperate to define and set up the minimal set of roles in agreement with the application constraints and the organization constraints that guarantee the global security policy of an enterprise. In order to maintain the global coherence of the existing access control scheme, an algorithm is proposed to detect the possible inconsistencies before the integration of a new component in the Information System.

A hybrid GA approach for solving the Dynamic Vehicle Routing Problem with Time Windows

The dynamic vehicle routing problem is an extension of conventional routing problems whose particularities are that information can change after initial routes have been constructed or that not all the information is known when the routing process takes place. The main interest of this type of problem is that it corresponds to many real word applications (repair services, courier mail services, taxi cab services). In this paper, we study the particular case of the dynamic vehicle routing problem with time windows (DVRPTW) in which occurrences of new customers appear over time. We propose an original resolution approach based on a genetic algorithm adapted to this dynamic optimisation context. Taguchis tables have been used in order to adjust the parameters of our genetic algorithm. Experimental results based on the modified Solomon benchmarks show the efficacy of our approach as compared to other meta-heuristic approaches

Vehicle routing problem with time windows and fuzzy demands: an approach based on the possibility theory

In this paper, the vehicle routing problem with time windows and fuzzy demands (VRPTWFD) is considered and a fuzzy recourse model based on the possibility theory is proposed. A stochastic simulation and a genetic algorithm (GA) are integrated to design a hybrid intelligent algorithm to solve the fuzzy version of a stochastic recourse model. Moreover, an adaptation of the Solomons benchmark is proposed in order to assess the quality of the proposed approach.

Recurrent neural network approach for cyclic job shop scheduling problem

Abstract While cyclic scheduling is involved in numerous real-world applications, solving the derived problem is still of exponential complexity. This paper focuses specifically on modelling the manufacturing application as a cyclic job shop problem and we have developed an efficient neural network approach to minimise the cycle time of a schedule. Our approach introduces an interesting model for a manufacturing production, and it is also very efficient, adaptive and flexible enough to work with other techniques. Experimental results validated the approach and confirmed our hypotheses about the system model and the efficiency of neural networks for such a class of problems.

A multi-objective green supply chain network design

In this paper, we study a green supply chain network design problem with environmental concerns. We are interested in the environmental investments decisions in the design phase and propose a multi-objective optimization model that captures the a compromise between the total cost and the environment influence. This work addresses the optimization of the supply chain design considering economical and environmental issues. We explicitly consider two objective functions. The first one, measures the total cost: fixed setup cost, environmental protection investment, transportation cost, logistics cost, waste treatment cost and energy consumption cost. The second one, measures the total CO2 emission in all the supply chain. The strategic decisions considered in the model are warehouses and distribution centers location, building technology selection and processing/distribution planning.

Adaptive particle swarm optimizer for feature selection

The combinatorial nature of the Feature Selection problem has made the use of heuristic methods indispensable even for moderate dataset dimensions. Recently, several optimization paradigms emerged as attractive alternatives to classic heuristic based approaches. In this paper, we propose a new an adapted Particle Swarm Optimization for the exploration of the feature selection problem search space. In spite of the combinatorial nature of the feature selection problem, the investigated approach is based on the original PSO formulation and integrates wrapper-filter methods within uniform framework. Empirical study compares and discusses the effectiveness of the devised methods on a set of featured benchmarks

A Multi-agent Based Self-adaptive Genetic Algorithm for the Long-term Car Pooling Problem

Rising vehicles number and increased use of private cars have caused significant traffic congestion, noise and energy waste. Public transport cannot always be set up in the non-urban areas. Car pooling, which is based on the idea that sets of car owners having the same travel destination share their vehicles has emerged to be a viable possibility to reduce private car usage around the world. In this paper, we present a multi-agent based self-adaptive genetic algorithm to solve long-term car pooling problem. The system is a combination of multi-agent system and genetic paradigm, and guided by a hyper-heuristic dynamically adapted by a collective learning process. The aim of our research is to solve the long-term car pooling problem efficiently with limited exploration of the search space. The proposed algorithm is tested using large scale instance data sets. The computational results show that the proposed method is competitive with other known approaches for solving long-term car pooling problem.