Ouajdi Korbaa

Heuristic for Scheduling Intrees on m Machines with Non-availability Constraints

This paper considers the problem of scheduling n tasks subject to intree-precedence constraints on m identical machines under non-availability constraints. The objective is to minimize the makespan. This problem is known to be NP-hard. We propose and test several heuristic variants based on different selection and dispatching rules.

Heuristics for the Hybrid Flow Shop Scheduling Problem with Parallel Machines at the First Stage and Two Dedicated Machines at the Second Stage

In this article, we present four heuristics for solving the problem of the hybrid flow shop scheduling problem denoted HFS with parallel machines at the first stage and two dedicated machines at the second stage. We compare heuristics with lower bounds and upper bounds generated by a mathematical model.

A Genetic-Fuzzy Classification Approach to Improve High-Dimensional Intrusion Detection System

With the increasing number of attacks and growing scalability of connected networks over the past few years, researchers are brought to find other alternatives to judge the relevance, severity and correlation of network attacks. The high-dimensional intrusion detection system seems a promising dynamic protection component in security fields. In this work we propose an optimized classification scheme that coordinates several techniques for generating fuzzy association rules based on a large data set. Our main task is to ameliorate the detection rate of attacks in a real-time environment by using the one-versus-one decomposition to minimize as much as possible the false alarm rate. In addition, we aim to reduce the loss of knowledge through a suitable n-dimensional overlap function in order to model the conjunction in fuzzy rules to provide enough classification accuracy. We can also opt for the aggregation method to obtain the final decision. To evaluate the performance of our approach, an experimental study is performed so as to achieve relevant results. The final outcome shows that our approach outperforms other classifiers by providing the highest detection accuracy, a low false alarm rate and time consumption.

Towards a Risk-Aware Business Process Modelling Tool Using the ADOxx Platform.

Business Process modelling is a key element in the management of organizations. It allows to build an analytical representation of ‘as-is’ processes in an organization and compared it with ‘to-be’ processes for improving their efficiency. Besides, although, risk is an element that can affect business process negatively, it is still managed independently. A necessary link is missing between business process and risk models. To better manage risk related to business process, it should be integrated and evaluated dynamically within the business process models. Currently, there are different meta-models allowing business process modelling. Nevertheless, there are few meta-models allowing risk modelling and even fewer ones that integrate both concepts related to risks and business processes. Based on this need and these observations, we propose, in this work, a risk-aware business process modelling tool using the ADOxx meta-modelling platform.

A two-stage hybrid flow shop problem with dedicated machine and release date

Abstract This paper presents a mathematical model, three heuristics and two lower bounds in order to solve the hybrid flow shop scheduling problem with parallel machines at the first stage and two dedicated machines at the second stage. Each job is subject to a release date at stage one. The objective is to minimize the makespan.

Fast Spatial Spectral Schroedinger Eigenmaps algorithm for hyperspectral feature extraction

Abstract Based on the Laplacian Eigenmaps (LE) algorithm and a potential matrix, the Spatial Spectral Schroedinger Eigenmaps (SSSE) technique has proved a great yield during the hyperspectral dimensionality reduction process. Experimentally, SSSE is in deficiency of high computing time which may hinder its contribution in the remote sensing field. In this paper, a fast variant of the SSSE approach, called Fast SSSE, was proposed. The new suggested method substitutes the quadratic constraint employed during the optimization problem, by a linear constraint. This overhaul preserves the data properties in analogous way to the SSSE technique, but with a fast implementation. Two real hyperspectral data sets were adopted during the experimental process. Experiment analysis exhibited good classification accuracy with a reduced computational effort, compared with the original SSSE approach.

Neuro-fuzzy and genetic-fuzzy based approaches in intrusion detection: Comparative study

There is no standard solution we can use to completely protect against computer network intrusion. Every solution has its advantages and drawbacks. Soft computing is considered as a promising paradigm to cope with the dynamic evolution of networks. In previous works, we presented two soft computing approaches of intrusion detection. The first one is based on the neuro-fuzzy and the second one is based on the genetic fuzzy one. In this work, we elaborate an empirical comparative study to highlight the benefits of each method in intrusion detection and exploit their complementarities to enhance the detection rate of all types of attacks as well as decrease the false positives rate.

Dynamic delay risk assessing using cost-based FMEA for transportation systems

To be competitive, transportation systems must be able to analyze and to evaluate, in real-time, critical differences between the short-term planned actions and the actual performed actions generating states of undesirable or unacceptable risk. We propose a method for monitoring the dynamic evolution of risk in the operational flow of a transportation system. It consists in an approach assessing risk associated to delays affecting the transportation operations. We use the FMEA (Failure Modes and Effects Analysis) around failure scenarios rather than failure modes, and we evaluate risk using probability and cost. A scenario probability is estimated dynamically in discrete points based on events occurrences during the process execution. The proposed approach useful to transportation managers is based on consistent and meaningful risk evaluation criteria to facilitate cost-based decisions during execution. The implementation of this method is performed by monitoring a container delivery process facing delays risks.

A min-max ANT colony algorithm for machine loop layout problem

This paper handles a problem of loop machine layout using one load and unload machine. The goal of this problem is to minimize the total cost of transporting parts within each manufacturing cell. The novelty of this study is based on the reformulation of the problem taking into consideration new variables which are generally neglected by recent researches like proximity constraints and machine dimensions. Hence, we aim to place these machines on a grid that represents the surface of the cell, in order to construct a loop layout while respecting proximity constraints. This new formulation led us to propose a two-stage approach to solve this problem. The first step consists in positioning the machines on a grid while respecting the proximity constraints and machines dimensions. The second step aims to optimize the path between these machines already positioned in order to minimize number of the loops travelled by parts. In this paper, the second step will be the center of our interesting. To solve this problem, we use ant colony optimization algorithm. The effectiveness of our approaches is demonstrated through numerical examples.

A Multi-Agent System to Solve a New Formulation of Machine Layout Problem

This paper focuses on the machine arrangement problem on common loop network. The machines are arranged in a cycle and materials transported in only one direction around the cycle using a conveyor belt. The goal of this problem is to minimize the maximum number of loops required for the manufacture of any of the products. The problem is known to be NP-hard. Thus, the right way to proceed is through the use of heuristics techniques. However, the industrial managers complain about an issue which is the mishandling of large variables and constraints that led to a lack of realism. In this work, we introduce a new formulation for the machine layout problem by adding new constraints which are the machine dimensions and the proximity constraints between the machines. This new formulation led us to propose two-stage approach to solve this problem. The first step consists on positioning the machines on a grid while respecting the proximity constraints and machines dimensions. The second step aim to optimize the path between these machines already positioned in order to minimize number of the loops traveled by parts. In this paper, we are interested in the first step by using multi-agent system. This choice can be explained by the well-known cooperation of the multi-agent system. The effectiveness of our approaches is demonstrated through numerical examples.