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10 results on '"Oumayma Bahri"'

1. Use of Electric Vehicles in Home-Health Care Routing Problems: Analysis of a Multi-objective Approach under Uncertainty

Author

Lionel Amodeo, Oumayma Bahri, El-Ghazali Talbi, Laboratoire d'Optimisation des Systèmes Industriels (LOSI), Laboratoire Informatique et Société Numérique (LIST3N), Université de Technologie de Troyes (UTT)-Université de Technologie de Troyes (UTT), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), and Université de Technologie de Troyes (UTT)

Subjects
Waiting time, 021103 operations research, Operations research, Computer science, 020209 energy, 0211 other engineering and technologies, Optimal planning, Travel cost, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], 02 engineering and technology, Fuzzy logic, Control and Systems Engineering, Home health, Objective approach, 0202 electrical engineering, electronic engineering, information engineering, Optimization methods, Routing (electronic design automation), ComputingMilieux_MISCELLANEOUS
Abstract

Our aim in this study is to propose a novel approach for optimizing a variant of the home health care routing problem, in which the health-care workers perform a requested number of visits to patients by using electric vehicles. Our approach on the one hand uses multi-objective optimization methods to find optimal planning routes of EVs and on the other hand deals with uncertainty in some major problem constraints. The goal is to help decision makers choosing appropriate routes; while minimising the cost of electric vehicular displacements in terms of: the total travel cost for all home-health workers and the waiting time for all patients. Moreover, we consider in our problem the fuzzy nature of some major constraints.

Published
2021
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2. Robustness-based approach for fuzzy multi-objective problems

Author

El-Ghazali Talbi, Oumayma Bahri, Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Optimisation de grande taille et calcul large échelle (BONUS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), and Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, 021103 operations research, Optimization problem, Robustness (computer science), Computer science, Theory of computation, 0211 other engineering and technologies, General Decision Sciences, 02 engineering and technology, Management Science and Operations Research, Fuzzy logic, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

In recent years, reasoning under uncertainty in multi-objective problems has become an active research area. However, solving this kind of problems reveals many performance and robustness issues that have been so far been neglected. Most of existing studies in this area are focused on dealing with robustness in the field of mono-objective optimization. The aim of the present paper is to address robustness of multi-objective optimization problems over uncertain inputs data. In particular, we focus on the specific case of fuzziness propagation to the multiple objectives in such problems. Then in order to avoid the loss of efficiency of fuzzy-valued objective values, we introduce new robustness techniques combining fuzziness and multi-objective context.

Published
2021
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3. Robust Routes for the Fuzzy Multi-objective Vehicle Routing Problem

Author

Oumayma Bahri, El-Ghazali Talbi, and Nahla Ben Amor

Subjects
Engineering, Mathematical optimization, 021103 operations research, business.industry, Tardiness, Monte Carlo method, 0211 other engineering and technologies, 02 engineering and technology, Fuzzy logic, Control and Systems Engineering, Robustness (computer science), Vehicle routing problem, 0202 electrical engineering, electronic engineering, information engineering, Fuzzy number, 020201 artificial intelligence & image processing, business
Abstract

This paper considers a multi-objective variant of vehicle routing problems, in which the customer demands are supposed to be triangular fuzzy numbers and the objective functions are also disrupted by fuzziness. However, the propagation of fuzzy demands to the objectives can affect the reliability of generated solutions. To this end, we propose a robustness approach to deal with fuzziness in the multi-objective context. The new approach tries to achieve robust routes that minimize two fuzzy-valued objectives, the total traveled distance and total tardiness time. Additionally, we suggest to refine our previously proposed algorithms for integrating robustness. An experimental study based on a Monte-Carlo simulation is finally carried out to evaluate the obtained results.

Published
2016
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4. Possibilistic Framework for Multi-objective Optimization Under Uncertainty

Author

Oumayma Bahri, Nahla Ben Amor, El-Ghazali Talbi, Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus (LARODEC), Université de Tunis-ISG de Tunis, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), and Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, 021103 operations research, Computer science, Probabilistic-based design optimization, Pareto principle, Evolutionary algorithm, Uncertainty, 0211 other engineering and technologies, Extension (predicate logic), [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], 02 engineering and technology, Evolutionary algorithms, Multi-objective optimization, Possibilty theory, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Ranking, Vehicle routing problem, Line (geometry), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing
Abstract

International audience; Optimization under uncertainty is an important line of research having today many successful real applications in different areas. Despite its importance, few works on multi-objective optimization under uncertainty exist today. In our study, we address combinatorial multi-objective problem under uncertainty using the possibilistic framework. To this end, we firstly propose new Pareto relations for ranking the generated uncertain solutions in both mono-objective and multi-objective cases. Secondly, we suggest an extension of two well-known Pareto-base evolutionary algorithms namely, SPEA2 and NSGAII. Finally, the extended algorithms are applied to solve a multi-objective Vehicle Routing Problem (VRP) with uncertain demands.

Published
2018
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5. Dealing with Epistemic Uncertainty in Multi-objective Optimization: A Survey

Author

El-Ghazali Talbi, Oumayma Bahri, Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus (LARODEC), Université de Tunis-ISG de Tunis, Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Optimisation de grande taille et calcul large échelle (BONUS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), and Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
Pareto optimality, 021103 operations research, Epistemic uncertainty, Management science, Computer science, 0211 other engineering and technologies, Pareto principle, Context (language use), 02 engineering and technology, Multi-objective optimization, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Uncertain-valued objectives, Uncertainty quantification
Abstract

International audience; Multi-objective optimization under epistemic uncertainty is today present as an active research area reflecting reality of many practical applications. In this paper, we try to present and discuss relevant state-of-the-art related to multi-objective optimisation with uncertain-valued objective. In fact, we give an overview of approaches that have already been proposed in this context and limitations of each one of them. We also present recent researches developed for taking into account uncertainty in the Pareto optimality aspect.

Published
2018

6. A generic fuzzy approach for multi-objective optimization under uncertainty

Author

Nahla Ben Amor, El-Ghazali Talbi, Oumayma Bahri, Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus (LARODEC), Université de Tunis-ISG de Tunis, Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Optimisation de grande taille et calcul large échelle (BONUS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), and Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, Fuzzy sets, 021103 operations research, General Computer Science, Computer science, General Mathematics, Fuzzy set, 0211 other engineering and technologies, Pareto principle, Evolutionary algorithm, 02 engineering and technology, Evolutionary algorithms, Multi-objective optimization, Fuzzy logic, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Ranking, Vehicle routing problem, Pareto dominance, 0202 electrical engineering, electronic engineering, information engineering, Fuzzy number, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Triangular fuzzy numbers
Abstract

International audience; Multi-objective optimization under uncertainty has gained considerable attention in recent years due to its practical applications in real-life. Many studies have been conducted on this topic, but almost all of them transformed the problem into a mono-objective one or just neglected the effects of uncertainty on the outcomes. This paper addresses specific uncertain multi-objective problems in which uncertainty is expressed by means of triangular fuzzy numbers. To handle these problems, we introduced a new approach able to solve them without any transformation by considering fuzziness propagation to the objective functions. The proposed approach is composed of two main contributions: First, a fuzzy Pareto dominance is defined for ranking the generated fuzzy solutions. Second, a generic fuzzy extension of well-known evolutionary algorithms is suggested as resolution methods. An experimental study on multi-objective Vehicle Routing Problems (VRP) with uncertain demands is finally carried to evaluate our approach.

Published
2018
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7. β-Robustness approach for fuzzy multi-objective problems

Author

El-Ghazali Talbi, Oumayma Bahri, Nahla Ben Amor, Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus (LARODEC), Université de Tunis-ISG de Tunis, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), and Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, 021103 operations research, Fuzzy classification, Optimization problem, business.industry, 0211 other engineering and technologies, 02 engineering and technology, Multi-objective optimization, Fuzzy logic, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Robustness (computer science), Vehicle routing problem, 0202 electrical engineering, electronic engineering, information engineering, Fuzzy set operations, Fuzzy number, 020201 artificial intelligence & image processing, Artificial intelligence, business, Mathematics
Abstract

International audience; The paper addresses the robustness of multi-objective optimization problems with fuzzy data, expressed via triangular fuzzy numbers. To this end, we introduced a new robustness approach able to deal with fuzziness in the multi-objective context. The proposed approach is composed of two main contributions: First, new concepts of β-robustness are proposed to analyze fuzziness propagation to the multiple objectives. Second, an extension of our previously proposed evolutionary algorithms is suggested for integrating robustness. These proposals are illustrated on a multi-objective vehicle routing problem with fuzzy customer demands. The experimental results on different instances show the efficiency of the proposed approach.

Published
2016
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8. Optimization algorithms for multi-objective problems with fuzzy data

Author

Talbi El-Ghazali, Nahla Ben Amor, Oumayma Bahri, Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus (LARODEC), Université de Tunis-ISG de Tunis, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Mathematical optimization, 021103 operations research, Fuzzy classification, Neuro-fuzzy, business.industry, 0211 other engineering and technologies, Pareto principle, 02 engineering and technology, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Defuzzification, Fuzzy logic, Fuzzy transportation, 0202 electrical engineering, electronic engineering, information engineering, Fuzzy set operations, Fuzzy number, 020201 artificial intelligence & image processing, ComputingMethodologies_GENERAL, Artificial intelligence, business, Mathematics
Abstract

International audience; This paper addresses multi-objective problems withfuzzy data which are expressed by means of triangular fuzzynumbers. In our previous work, we have proposed a fuzzy Paretoapproach for ranking the generated triangular-valued functions.Then, since the classical multi-objective optimization methods canonly use crisp values, we have applied a defuzzification process.In this paper, we propose a fuzzy extension of two well-knownmulti-objective evolutionary algorithms: SPEA2 and NSGAII byintegrating the fuzzy Pareto approach and by adapting theirclassical techniques of diversity preservation to the triangularfuzzy context. An application on multi-objective Vehicle RoutingProblem (VRP) with uncertain demands is finally proposed andevaluated using some experimental tests.

Published
2014
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9. New Pareto approach for ranking triangular fuzzy numbers

Author

Talbi El-Ghazali, Oumayma Bahri, Nahla Ben Amor, Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus (LARODEC), Université de Tunis-ISG de Tunis, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique Fondamentale de Lille (LIFL), and Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, 021103 operations research, Fuzzy classification, 0211 other engineering and technologies, Pareto principle, 02 engineering and technology, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Multi-objective optimization, Fuzzy logic, Fuzzy transportation, Ranking, 0202 electrical engineering, electronic engineering, information engineering, Fuzzy number, Fuzzy set operations, 020201 artificial intelligence & image processing, ComputingMethodologies_GENERAL, Mathematics
Abstract

International audience; Ranking fuzzy numbers is an important aspect in dealing with fuzzy optimization problems in many areas. Although so far, many fuzzy ranking methods have been discussed. This paper proposes a new Pareto approach over triangular fuzzy numbers. The approach is composed of two dominance stages. In the first stage, mono-objective dominance relations are introduced and tested with some examples. In the second stage, a Pareto dominance is defined for multi-objective optimization and then applied to solve a vehicle routing problem (VRP).

Published
2014
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