Fouad Ben Abdelaziz

Cyclic Task Scheduling for Multifunction Radar

A framework and method is presented for developing a cyclic task schedule in a multifunction radar. Rather than assuming the task dwell time to be a fixed value when building the schedule, the task dwell time is modeled as a fuzzy set to allow for increased radar schedule flexibility. An optimization model is developed for the scheduling problem and a heuristic method for its solution is proposed. The heuristic method exploits the fuzzy set model in order to intelligently adjust the task dwell times. This adjustment allows for accommodation of more tasks on the radar timeline, thereby resulting in fewer dropped tasks. Computational results are presented to assess the behavior of the proposed scheduling method.

A Recourse Goal Programming Approach for the Portfolio Selection Problem

Abstract This paper presents a recourse goal programming approach to a multiple objective stochastic programming portfolio selection model. The main assumption of our approach is that the investor has a minimum acceptable expected rate of return to achieve under some predefined risk restrictions. The risk restrictions are expressed through constraints on the optimal portfolio beta value. The model and the solution strategy are illustrated with data from securities listed in the S&P100 index.

A multiple objective stochastic portfolio selection problem with random Beta

When selecting a portfolio, we need to consider, in general, the portfolio return and portfolio risk. Many risk measures have been used in portfolio selection problems as the Beta risk measure, introduced by the capital asset pricing model. Most of the existing research papers suppose that securitys Beta has a deterministic value. Recently, many researchers argued that in selecting the optimal portfolio, securities’ Beta should be considered as an uncertain parameter. In this paper, we set up fundamentals to model the portfolios Beta as a random variable and propose a multiple objective stochastic portfolio selection model with random Beta. To solve the proposed model, we apply a stochastic goal programming approach. A numerical example from the US stock exchange market is reported.

An Optimization Model and Tabu Search Heuristic for Scheduling of Tasks on a Radar Sensor

A radar task is an event on the radar timeline that is used to sense a new target or update the information about a previously detected target. A radar sensor performs a variety of different tasks that need to be completed before a certain time horizon. The efficient utilization of the radar timeline is thus a critical issue in the operation of modern radar systems. This paper develops a generalized framework for the radar task scheduling problem in order to allow for scheduling flexibility and the ability to handle multiple tasks using a single radar. An exact optimization model to schedule the tasks and a computationally efficient tabu search heuristic scheduling method are proposed. The full structure of the radar task is explicitly considered in a way that prevents collision of subtasks while naturally performing interleaving of subtasks in order to make more efficient use of the radar timeline. The optimization model and the heuristic method are constructed in a flexible manner in order to easily accommodate various system and scenario specific constraints. Computational results are presented to assess the behavior of the proposed method.

Portfolio selection problem: a review of deterministic and stochastic multiple objective programming models

The literature on portfolio selection mostly concentrates on computational analysis rather than on modelling efforts. In response, this paper provides a comprehensive literature review of multiple objective deterministic and stochastic programming models for the portfolio selection problem. First, we summarize different concepts related to portfolio selection theory, including pricing models and portfolio risk measures. Second, we report the mathematical models that are generally used to solve deterministic and stochastic multiple objective programming problems. Finally, we present how these models can be used to solve the portfolio selection problem.

Strategic investments in R&D and efficiency in the presence of free riders

We consider an industry composed of two types of firms, namely, innovators that invest in process research and development (R&D), and surfers that do not but benefit from knowledge spillover.We verify if the conclusions reached in the seminal paper by d’Aspremont and Jacquemin hold in this setting.We obtain that cooperation among innovators still lead to higher R&D and output levels than when they do not cooperate.Our main result is that the presence of surfers in an industry can be welfare improving under some conditions.

Multicriteria fuzzy clustering for brain image segmentation

One of the most challenging task in image analysis is to identify correctly tissues where boundaries are generally not clear. Fuzzy clustering is supposed to be the most appropriate to model this situation in applications such as tissue classification, tumor detection. While, image segmentation using fuzzy clustering technique classifies correctly pixels of an image with a great extent of accuracy [1], recent works have shown that fuzzy clustering techniques considers a single objective may not provide a good result since no single validity measure works well on different kinds of data sets. Moreover, a wrong choice of a validity measure leads to poor results [2]. In this paper, we introduce a multiobjective fuzzy clustering approach producing a set of Pareto solutions among which the best solution, based on I-index validation measure, is chosen to be the final clustering solution. First, a spatial information is considered to deal more effectively with the noise and intensity inhomogeneities introduced in imaging process. Second, we propose to use a variable string length encoding technique to automatically identify the number of clusters, given that it does not require a prior knowledge about number of clusters present in a data set. Therefore, an initializing method based on a center approximation approach is proposed to accelerate the clustering process and make results more robust. Applied to normal and multiple sclerosis lesion magnetic resonance image brain images, our method shows better performance than competing algorithms.

The motivations of earnings management and financial aggressiveness in American firms listed on the NASDAQ 100

Purpose - The purpose of this paper is to examine the motivations of earnings management and financial aggressiveness levels in the big 100 companies listed on the NASDAQ 100 after the 2007 financial crisis. Design/methodology/approach - This paper uses two samples. The first contains 471 observations of 100 companies listed on the NASDAQ 100 for the period 2008-2012 and is used to examine the motivations of earnings management. The second represents 282 observations of companies listed on the NASDAQ 100 that use financial aggressiveness. The authors use a panel data model to analyze the effects of four explanatory variables (corporate governance structure, CEO compensation, CEO characteristics and audit fees) on both earnings management and financial aggressiveness levels. Findings - The results of the investigation show the significant impact of corporate governance structure, CEO compensation, CEO characteristics and audit fees on reducing the earnings management and financial aggressiveness levels. Research limitations/implications - The findings can be valuable to both investors and researchers. For researchers, the present work may help in explaining the motivations of earnings management and financial aggressiveness practices used by large American firms after the 2007 US financial crisis. For investors, this study serves to highlight the critical importance of corporate governance, CEO compensation and CEO characteristics in limiting such behaviors. Thus, investors are recommended to account for such variables in order to make effective investment decisions. As an extension to this study, researchers might consider other CEO psychological variables. Other market indices could also be considered in order to generalize and validate the results of the research. Practical implications - Investors must take into consideration the corporate governance structure and ask for supplementary information about CEO characteristics to ensure better investment decisions. Originality/value - In this paper, and in contrast to previous research, the authors test the impact of corporate governance structure, CEO compensation, CEO characteristics and audit fees together on the level of both earnings management and financial aggressiveness behavior for large US non-financial firms after the 2007 financial crisis. The authors show that older CEOs use less earnings management and financial aggressiveness. The findings can be valuable to investors, managers and regulators because they have implications for their interactive decision-making process.

A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem

We propose a novel cooperative swarm intelligence algorithm to solve multi-objective discrete optimization problems (MODP). Our algorithm combines a firefly algorithm (FA) and a particle swarm optimization (PSO). Basically, we address three main points: the effect of FA and PSO cooperation on the exploration of the search space, the discretization of the two algorithms using a transfer function, and finally, the use of the epsilon dominance relation to manage the size of the external archive and to guarantee the convergence and the diversity of Pareto optimal solutions.

A cooperative swarm intelligence algorithm based on quantum-inspired and rough sets for feature selection

Abstract Feature selection is an important preprocessing step for classification as it improves the accuracy and overcomes the complexity of the classification process. However, in order to find a potentially optimal feature subset for the feature selection problem, it is necessary to design an efficient exploration approach that can explore an enormous number of possible feature subsets. It is also necessary to use a powerful evaluation approach to assess the relevance of these feature subsets. This paper presents a new cooperative swarm intelligence algorithm for feature selection based on quantum computation and a combination of Firefly Algorithm (FA) and Particle Swarm Optimization (PSO). Quantum computation ensures a good trade-off between the exploration and the exploitation of the search space while the combination of the FA and PSO enables an effective exploration of all the possible feature subsets. We use rough set theory to assess the relevance of the potential generated feature subsets. We tested the proposed algorithm on eleven UCI datasets and compared with a deterministic rough set reduction algorithms and other swarm intelligence algorithms. The experiment results show clearly that our algorithm provides a better rate of feature reduction and a high accuracy classification.