SESSION A4:

[95] A large neighborhood search for the vehicle routing problem with two-dimensional loading constraints
Lei Wu, Mhand Hifi and Moudher Khalid Abdal-Hammed

In this paper we investigate the use of the large neighborhood search for solving the vehicle routing problem with two-dimensional loading constraints, an NP-hard combinatorial optimization problem. Such a problem may be viewed as the combination of two complementary well-known problems: the two-dimensional bin-packing problem and the capacitated vehicle routing problem. The proposed method is based on a framework of the large neighborhood search combined with several local search procedures and has been evaluated on Gendreau’s benchmark instances. The obtained results are compared to those reached by the best methods taken from the literature. Encouraging results have been obtained.

[126] A Robust Optimization Approach for the Vehicle Routing Problem with Uncertain Travel Cost
Elyn Lizeth Solano Charris, Christian Prins and Andréa Cynthia Santos

The Robust Vehicle Routing problem (RVRP) with discrete scenarios is studied here to handle uncertain travelling time, where a scenario represents a possible discretization of the possible travel time observed on each arc at a given traffic hour. The goal is to build a set of routes considering the minimization of the worst total cost over all scenarios. Heuristics and a Genetic Algorithm (GA) are proposed for the RVRP considering a bounded set of discrete scenarios and the asymmetric arc costs on the transportation network. Tests on small and medium size instances are presented to evaluate the performance of the proposed GA for the RVRP. On small-size instances, a maximum of 20 customers, 3 vehicles and 30 discrete scenarios are handled. For medium-size instances, 100 customers, 20 vehicles and 20 scenarios as maximum are tested. Computational results indicate the GA produces good solutions and retrive the majority of proven optima in a moderate computational time.

[235] Genetic algorithm to infer criteria weights for multicriteria Inventory Classification
Hadhami Kaabi, Khaled Jabeur and Talel Ladhari

Multi Criteria Inventory Classification (MCIC) is a well known method to classify inventory items into three predefined and ordered categories A, B and C such as category A contains the most valuable inventory items and category C contains the least valuable ones. The aim of this classification is to keep related inventory costs under control. The process of MCIC consists in ranking items according to their weighted scores. The score of each item is the result of an aggregation function that combines the item evaluations on the different criteria and the criteria weights. In the literature, several methods propose different methodologies to determine the criteria weights (e.g. Linear Programming (LP), Artificial Intelligence (AI) and Multi Criteria Decision Making (MCDM)). This paper presents a method based on genetic algorithm to learn these weights. To estimate these criteria weights and to validate the usefulness of our proposed method, a benchmark data set of 47 items consumed in a Hospital Respiratory Therapy Unit (HRTU) is used.

[136] An Exact Solution Search for the Max-Min Multiple Knapsack Problem
Ferhan Al-Maliky, Mhand Hifi and Hedi Mhalla

In this paper, we propose to solve the max-min multiple knapsack problem by using an exact solution search. An instance of the problem is defined by a set of n items to be packed into m knapsacks as to maximize the minimum of the knapsacks' profits. The proposed method uses a series of interval searches, where each interval is bounded with a target value (considered as a lower bound) and an estimated upper bound. The target lower bound is computed by applying some aggressive fixation of some items to optimality whereas the upper bound is computed by using a surrogate relaxation. The performance of the proposed method is evaluated on a set of instances containing a variety of sizes. Computational results showed the superiority of the proposed method when comparing its provided results to those obtained by the Cplex solver and one of the best exact method available in the literature.

SESSION B4: Efficiency and Productivity

[99] Solving the Bi-Objective Integer Programming: A DEA Methodology
Esmaiel Keshavarz and Mehdi Toloo

Finding and classifying all efficient solutions for a Bi-Objective Integer Linear Programming (BOILP) problem is one of the controversial issues in Multi-Criteria Decision Making problems. The main aim of this study is to utilize the well-known Data Envelopment Analysis (DEA) methodology to tackle this issue. Toward this end, we first state some propositions to clarify the relationships between the efficient solutions of a BOILP and efficient Decision Making Units (DMUs) in DEA and next design a new two-stage approach to find and classify a set of efficient solutions. Stage I formulates a two-phase Mixed Integer Linear Programming (MILP) model, based on the Free Disposal Hull (FDH) model in DEA, to gain a Minimal Complete Set of efficient solutions. Stage II uses a variable returns to scale DEA model to classify the obtained efficient solutions from Stage I as supported and non-supported. A BOILP model containing 6 integer variables and 4 constraints is solved as an example to illustrate the applicability of the proposed approach.

[163] Simulation Based Optimization Approach to solve a Maintenance Process Problem
Nouha Lahiani, Yasmina Hani, Abderrahman El Mhamedi and Abdelfateh Triki

In this paper, a simulation based optimization method is introduced. A combined simulation and Non Dominated Sorting Genetic Algorithm II (NSGA-II) optimization engine is developed to solve a maintenance process problem for an industrial case. The coupling is used in order to optimize the performances of the simulation model representing a maintenance process by choosing the best queues’ scheduling policy. Our simulation model is performed using FLEXSIM software. NSGA-II model is implemented using C# language, because it ensures exchanges between FLEXSIM software and Ms Excel. The NSGA-II and simulation models operate in parallel over time with interactions. The aim of this paper is to propose an original approach for reorganizing the maintenance process using simulation based optimization approaches.

[179] An evaluation of a production performance across the selected EU regions
Jana Hanclova

The paper deals with production performance evaluation of the EU selected regions in the period 2000 – 2011. We estimate a translog stochastic production frontier using the true fixed-effects methods. We detect statistically significant savings with respect to the technical progress in capital input and consumption with respect to the technical progress in labor input. We evaluate the performance based on the Malmquist productivity index. We analyze these productivity patterns through technological progress and technical efficiency. According to the results, the researched region can be classified into four quadrants by causes of changes in productivity. Results showed that average productivity growth was 1.5% mainly due to the technological progress 1% and a slight influence of technical efficiency change 0.5%.

[166] A Comparison of Multiple Objective Evolutionary Algorithms for Solving the Multi-Objective Node Placement Problem
Hela Masri, Ons Abdelkhalek and Saoussen Krichen

The multi-objective node placement (MONP) problem involves the extention of an existing heterogeneous network while optimizing three conflicting objectives: maximizing the communication coverage, minimizing active nodes and communication devises costs, and maximizing of the total capacity bandwidth in the network. Multiple devices' types are to be deployed in order to ensure networks' heterogeneity. As the MONP problem is NP-Hard, heuristic approaches are necessary for large problem instances. In this paper, We compare the ability of three different sorting-based multiple objective genetic algorithms to find an optimal placement and connection between the potential placed nodes. The empirical validation is performed using a simulation environment called Inform Lab and based on real instances of maritime surveillance application. Results and discussion on the performance of the algorithms are provided.

SESSION C4: Fault Detection and Control

[111] Fault Tolerant Predictive Control for Multi-Agent Dynamical Systems: Formation Reconfiguration using Set-Theoretic Approach
Minh Tri Nguyen, Cristina Stoica Maniu, Sorin Olaru and Alexandra Grancharova

This paper deals with the task assignment problem, which represents a major issue in dynamical formation control. The formation is defined in terms of a group of homogeneous dynamical agents. The position of each agent in the formation is predetermined by pre-imposing the distance between each pair of agents. Recently by using set-theoretic methods, the task assignment has been formulated in terms of an optimization problem allowing to keep the agents in a tight formation in real-time. In this paper we revisit these results and propose a new algorithm for the task assignment formulation in view of real-time control by including fault detection and isolation capabilities. The proposed methods will be illustrated by means of a numerical example.

[115] Unvariate Process Monitoing using Multiscale Shewhart Charts
M. Ziyan Sheriff, Fouzi Harrou and Mohamed Nounou

Monitoring charts play an important role in statistical quality control. Shewhart charts are among the most commonly used charts in process monitoring, and have seen many extensions for improved performance. Unfortunately, measured practical data are usually contaminated with noise, which degrade the detection abilities of the conventional Shewhart chart by increasing the rate of false alarms. Therefore, the effect of noise needs to be suppressed for enhanced process monitoring. Wavelet-based multiscale representation of data, which is a powerful feature extraction tool, has shown good abilities to efficiently separate deterministic and stochastic features. In this paper, the advantages of multiscale representation are exploited to enhance the fault detection performance of the conventional Shewhart chart by developing an integrated multiscale Shewhart algorithm. The performance of the developed algorithm is illustrated using two examples, one using synthetic data, and the other using simulated distillation column data. The simulation results clearly show the effectiveness of the proposed method over the conventional Shewhart chart and the conventional Shewhart chart applied on multiscale pre-filtered data.

[242] Frequency Adaptive Repetitive Control of Grid-connected Inverters
Rabia Nazir, Keliang Zhou, Neville R. Watson and Alan Wood

Grid-connected inverters (GCI) are widely used to feed power from renewable energy distributed generators into smarter grids. Repetitive control (RC) enables such inverters to inject high quality fundamental-frequency sinusoidal currents into the grid. However, digital RC which can achieve zero steady- state error tracking of any periodic signal with known integer period, cannot exactly track or reject periodic signal of frequency variations, and would lead to a significant power quality degradation of GCIs when grid frequency varies and causes periodic signal with fractional periods. In this paper a frequency adaptive RC (FARC) strategy at fixed sampling rate is proposed to deal with any periodic signal of variable frequency, where a Lagrange interpolation based fractional delay filter is used to approximate the fractional period terms in RC. The proposed FARC offers fast on-line tuning of the fractional delay and the fast update of the coefficients, and then provides GCIs with a simple but very accurate real time frequency adaptive control solution to the injection of high quality sinusoidal current under grid frequency variations. A case study a three-phase GCI is conducted to testify the validity of the proposed strategy

SESSION D4: Identification and Control

[64] Inverse of Multivariable Linear Time Varying Bond-Graph models
Chafik Andaloussi, Hicham Hihi and Zakaria Chalh

In this paper, we propose to study the inversion of multivariable linear time varying systems modelled by bondgraph approach. Thus, an analysis method based on noncommutative differential algebra concepts is presented. The inverse and the reduced inverse models are studied in function of the decouplability of multivariable LTV system. To show the interest of this new method, several applications have been realized, among which is the thyristor circuit model TCSC often used in power electronics. This method can be implemented in software such as 20-sim or Symbol, in order to control the LTV systems in real time.

[76] On the Modeling of Discrete Time Auto-Regressive Representations
Lazaros Moysis and Nicholas Karampetakis

It is well known (I. Gohberg, P. Lancaster, L. Rodman 1982), (Vardulakis, A. I. G.; Antoniou, E., 2003) that given the discrete time AutoRegressive representation A(σ)β(k) = 0; where σ denotes the shift forward operator and A(σ) is a polynomial matrix, we can always construct the forward-backward behavior of this system, by using the finite and infinite elementary divisor structure of A(σ). The main theme of this work is to study the inverse problem : given a specific forward-backward behavior, find a family of polynomial matrices A(σ), such that the system A(σ)β(k) = 0 has exactly the prescribed behavior. As we shall see, the problem can be reduced either to a linear system solving problem or to an interpolation problem.

[102] Consensus in multi-agent systems with non-periodic sampled-data exchange and uncertain network topology
Mehran Zareh, Dimos V. Dimarogonas, Mauro Franceschelli, Karl Henrik Johansson and Carla Seatzu

In this paper consensus in second-order multi-agent systems with a non-periodic sampled-data exchange among agents is investigated. The sampling is random with bounded inter-sampling intervals. It is assumed that each agent has exact knowledge of its own state at any time instant. The considered local interaction rule is PD-type. Sufficient conditions for stability of the consensus protocol to a time-invariant value are derived based on LMIs. Such conditions only require the knowledge of the connectivity of the graph modeling the network topology. Numerical simulations are presented to corroborate the theoretical results.

[216] Causal Temporal Signature from Diagnoser model for online Diagnosis of Discrete Event Systems
Ramla Saddem and Alexandre Philippot

In DES, there are two basic approaches to diagnosis: the first approach is the diagnosers and the second approach is Causal Temporal Signature (CTS) and chronicles. The first approach has limitations including the issue of combinatorial explosion. On the other side, it offers tools to study the diagnosability of the models constructed. CTS are easier to write but pose the problem of the guarantee of the completeness and especially the consistency of a given base. This paper presents an approach to translate formalism and model of a diagnoser approach into CTS. From these CTS, a recognition algorithm based on the concept of “world” is used. A “world” is defined as a set of coherent hypotheses of assignment of the event received by the diagnostic task.

SESSION E4: Tools and Design of Embedded on chip communications and systems

[19] Implementation of Universal Digital Architecture using 3D-NoC for Mobile Terminal
Benhaoues Atef, Bourennane El Bay, Camel Tanougast, Toumi Salah, Mayache Hichem and Kamel Messaoudi

The need to integrate multiple wireless communication protocols into a single low-cost flexible hardware platform is prompted by the increasing number of emerging communication protocols and applications in modern embedded systems. So the current challenge is to design of new digital architectures, in addition to its ability to take over of many functions. In this paper we have identified similarities between the despreader units in Rake receiver and the processor element in FFT-SDF (Fast Fourier Transform- Single path Delay Feedback) to propose a generic architecture shared between the two algorithms widely used. This Smart architecture is interconnected with similar modules by a 3D Network-on-Chip for implementation of Rake receiver (used in WCDMA system) and FFT receiver (in OFDM system). We present in this paper a 3D-NoC with half layer-layer connection, this architecture uses a modified XYZ routing algorithm. The proposed architectures are coded using VHDL onto a Virtex 5 Field-Programmable Gate Array (FPGA) device and results are compared with similar works. The implementation demonstrates that the proposed architectures can deliver a high reduction of the FPGA logic requirements with high maximum frequency.

[39] Modeling and simulation of a patch antenna from it Bond Graph model
Sabri Jmal, Hichem Taghouti and Abdelkader Mami

In this paper, we propose a new technique for analyzing patch antennas by the Bond Graph approach. Our technique is to identify and simulate the scattering parameters of a multi-triangular patch antenna from the Bond Graph model. This methodology involves two important steps. The first step is the development of the Bond Graph model by physical interpretation; the second is the determination and the simulation of the scattering parameters by an analytical procedure. This step is followed by a result validation by using comparison to the simulation results of the same structure found by the HP-ADS software.

[172] Formal Specification and Verification of wireless networked self-organized Systems on Chip
Hayat Daoud, Camel Tanougast, Mostefa Belarbi and Mikael Heil

Communication in networked systems must be reliable during run time data exchanges. Therefore, their specifications and designs must be clear, understandable and follow specific rules. Indeed, these systems must guard against potential failures in preventing faults and associated errors, failures or allowing a degraded operating modes in order to protect against global system failures while maintaining a minimum of functionality in the case of irreversible failures, thanks to fault tolerant and security mechanisms. Given challenges of verification by simulation of these complex systems have made the huge number of possible states of carrying operation, and to ensure reliability requirements, we propose a formal proof verification as an alternative operation and validation blocks against defects through one proposed self-adaptive fault tolerance paradigm suitable for occurred errors of networked embedded systems. The formalization process based on an incremental and validated correct-by-construction development of the proposed reliability mechanisms are designed and proved in order to ensure accuracy operating required related dependability of communication wireless networked systems.

[226] Fault-tolerant self-organized mechanism for networked reconfigurable MPSoC
Mikael Heil and Camel Tanougast

In this paper, a self-organized reliability technique is presented to test the Network on Chip (NoC) parts of networked reconfigurable MPSoC. We propose a new mechanism based on delocalized offline tests to detect and locate permanent errors in the NoC through networked multi-nodes system. Concepts and mechanisms of the proposed approach are detailed in the case of a network system based on Zigbee wireless communication where error detections and timing performance are giving.