Khaled Ghédira

New local diversification techniques for flexible job shop scheduling problem with a multi-agent approach

The Flexible Job Shop problem is among the hardest scheduling problems. It is a generalization of the classical Job Shop problem in that each operation can be processed by a set of resources and has a processing time depending on the resource used. The objective is to assign and to sequence the operations on the resources so that they are processed in the smallest time. In our previous work, we have proposed two Multi-Agent approaches based on the Tabu Search (TS) meta-heuristic. Depending on the location of the optimisation core in the system, we have distinguished between the global optimisation approach where the TS has a global view on the system and the local optimisation approach (FJS MATSLO) where the optimisation is distributed among a collection of agents, each of them has its own local view. In this paper, firstly, we propose new diversification techniques for the second approach in order to get better results and secondly, we propose a new promising approach combining the two latter ones. Experimental results are also presented in this paper in order to evaluate these new techniques.

PHC-NSGA-II: A Novel Multi-objective Memetic Algorithm for Continuous Optimization

We introduce in this paper a new multi-objective memetic algorithm. This algorithm is a result of hybridization of the NSGA-II algorithm with a new designed local search procedure that we named Pareto Hill Climbing. Verification of our novel algorithm is carried out by testing it on two sets of multi-objective test problems and comparing it to other multi-objective evolutionary algorithms (MOEAs) and other multi-criterion memetic algorithms (MMAs). Simulation results show the algorithm ability in tackling continuous multi-objective problems in terms of convergence and diversity. Our hybrid algorithm (1) outperforms pure MOEAs, (2) is competent with other gradient based MMAs, and (3) can solve non differentiable problems.

Classification of model refactoring approaches.

In this paper, we provide a detailed overview of existing researches in the field of software restructuring and model refactoring, from a formal as well as a practical point of view. We propose a possible taxonomy for the classification of several existing and proposed model refactoring approaches. The taxonomy is described with a feature model that makes the different design choices for model refactoring explicit.

R2-IBN: Argumentation Based Negotiation Framework for the Extended Enterprise

Nowadays, in order to face new challenges, extended enterprises must be up to date to the new strategic, economic and organizational structures. Consequently, intelligent software based on agent technology emerges to improve system design, and to increase enterprise competitive position as well. The competitiveness is based on the cooperation. Thus, within this cooperation, conflicts may arise. The automated negotiation plays a key role to look for a common agreement. Argumentation theory has become an important topic in the field of Multi-Agent Systems and especially in the negotiation problem. In this paper, first, an overview of an already proposed model MAIS-E2(Multi-Agent Information System for an Extended Enterprise) is presented. Then an argumentation based negotiation framework: Relationship-Role and Interest Based Negotiation (R2-IBN) framework is presented, and within this framework, we focused mainly on, argument generation module via inference rules and argument selection module via fuzzy logic.

MOF-EMF Alignment

This paper explains the relationship between the MOF and EMF meta-models and describes the ATL transformation tool. Based on this specific mapping, general principles for transformation between these modelling frameworks are discussed and used to reflect on our current standardization work on MOF query/view/transformations (QVT) within the OMGs model driven architecture initiative.

Resolution of Conlicts Via Argument Based Netotiation: Extended Enterprise Case

In the age of information proliferation and communication advances, extended enterprise must hold to new strategic, economic and organizational structures in order to face new challenges. Consequently, intelligent software based on agent technology emerges to improve system design, and to increase enterprise competitive position as well. The competitiveness is based on the cooperation. Thus, within this cooperation, conflicts may arise. The automated negotiation plays a key role as a means to look for a common agreement. Argument based negotiation is an effective means of resolving conflicts in multi-agent system. In this paper, first, a new multi-agent information system for an extended enterprise (MAIS-E2) model is proposed for the purpose of having an integrated logistic system. MAIS-E2 is a collection of intelligent agent-agencies with specialized expertises, interacting to carry out common objectives. Then a formal description for R2-IBN framework is presented in a specific theory of argumentation

A TWO-STEP TRANSSHIPMENT MODEL WITH FUZZY DEMANDS AND SERVICE LEVEL CONSTRAINTS

We consider a distribution network composed of one supplier and several non-identical locations characterized by fuzzy customer demands and service level constraints. These locations could cooperate together via product transfer known as transshipment. The transshipment problem consists in determining the replenishment quantities that minimize the total inventory cost where a specific transfer policy is practiced. Our objectives in this paper are to identify an inter-location transfer policy that participates to satisfy the service level constraints and to determine the approximate replenishment quantities. To achieve these objectives we propose: (1) a new transshipment model based on the chance constrained programming, (2) a two-step transshipment policy that differs from classic ones by product transfer from locations in need to others also in need and (3) a hybrid algorithm based on fuzzy simulation and genetic algorithms to approximate replenishment quantities. (Received in March 2009, accepted in September 2009. This paper was with the authors 2 months for 1 revision.)

Container Handling Using Multi-agent Architecture

Container terminals are essential intermodal interfaces in the global transportation network. Efficient container handling at terminals is important in reducing transportation costs and keeping shipping schedules. This paper discusses a multi-agent model to simulate, solve and optimize the amount of storage space for handling container departures within a fluvial or maritime port. The multi-agent model is developed to minimize the expected total number of rehandles while respecting spatio-temporal constraints. Experimental and numerical runs are given and illustrated. These runs carry out the proposed multi-agent model with comparison to the associated centralized version, basing on non-informed search algorithm and informed search algorithm. They show that the multi-agent model reduces rather the number of expected relocation movements especially when basing on informed search algorithm.

FORMALISING AN AGENT-BASED CONTAINER STACKING MODEL VIA PETRI NETS

Abstract Within the frame of combinatorial optimisation problem solving, particularly the one that concern scheduling in fluvial and maritime transport domain, we are interested in quay management of fluvial ports and precisely in the activity of loading of ISO containers within a barge. First, we present a multi-agent model dedicated to the stacking of containers while minimizing the number of done movements. Then, we propose an associated formalisation using Coloured Petri Nets. Such formalism is developed and is visualized via the environment software CPN-Tools. Finally, the use of such a formal method to express a multi - agent model for this class of problems will be briefly discussed.