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Hamida Labidi

Informations générales

Hamida Labidi
Grade

Post Doc

Biographie courte

PhD in Computer Science from ISG Tunis and member of Smart Lab. Research specialization lies in combinatorial optimization and operations research, with emphasis on the modeling and resolution of vehicle routing problems and related variants. Current work involves the development of advanced algorithms to address complex optimization challenges, particularly in transportation and logistics systems.

Équipes

Axes de recherche

Combinatorial Optimization

Publications

  • 2025
    Hamida Labidi, Abir Chaabani, Nadia Ben Azzouna

    Hybrid Genetic Algorithm for Solving an Online Vehicle Routing Problem with Time Windows and Heterogeneous Fleet

    This paper proposes a hybrid genetic algorithm to address an online vehicle routing problem with time windows and a heterogeneous fleet, presented at Hybrid Intelligent Systems (HIS 2023)., 2025

    Résumé

    The Vehicle Routing Problem (VRP) is a well-known optimization problem in which we aim traditionally to minimize transportation costs while satisfying customer demands. In fact, most logistics companies use a heterogeneous fleet with varying capacities and costs, presenting a more complex variant known as Rich VRP (RVRP). In this paper, we present a mathematical formulation of the RVRP, considering both hard time windows and dynamically changing requests to be as close as possible to real-life logistics scenarios. To solve this challenging problem, we propose a Hybrid Genetic Algorithm (HGA). The experimental study highlights the out-performance of our proposal when evaluated alongside other algorithms on the same benchmark problems. Additionally, we conduct a sensitivity analysis to illustrate how resilient the algorithm is when problem parameters are altered.

  • Hamida Labidi, Nadia Ben Azzouna, Khaled Hassine, Mohamed Salah Gouider

    An improved genetic algorithm for solving the multi-objective vehicle routing problem with environmental considerations

    This paper presents an improved genetic algorithm for addressing the multi-objective vehicle routing problem with environmental considerations, published at KES 2023., 2023

    Résumé

    In recent years, the negative impacts of neglecting the environment, particularly global warming caused by greenhouse gases, have gained attention. Many countries and organizations are taking steps to reduce their greenhouse gas emissions and promote sustainable practices. In this paper, we aim to address the gap in the classical Vehicle Routing Problem (VRP) by taking into consideration the environmental effects of vehicles. To find a balance between cost-efficiency and environmental impact, we propose a Hybrid Genetic Algorithm (HGA) to address the Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) and a heterogeneous fleet, taking into account new orders that arrive dynamically during the routing process. This approach takes into consideration the environmental effects of the solutions by optimizing the number and type/size of vehicles used to fulfill both static and dynamic orders. The goal is to provide a solution that is both cost-effective and environmentally friendly, addressing the issue of over-exploitation of energy and atmospheric pollution that threaten our ecological environment. Computational results prove that the hybridization of a genetic algorithm with a greedy algorithm can find high-quality solutions in a reasonable run time.

  • Hamida Labidi, Khaled Hassine, Fethi Mguis

    Genetic Algorithm for Solving a Dynamic Vehicle Routing Problem with Time Windows

    This paper proposes a genetic algorithm to solve the dynamic vehicle routing problem with time windows, presented at HPCS 2018., 2018

    Résumé

    The Vehicle Routing Problem (VRP) introduced by Dantzing and Ranser (1959) is a prominent combinatorial optimization problem. Over the last several decades, many variants of the multi-constrained vehicle routing problem have been studied and a class of problems known as rich vehicle routing problem (RVRPs), has been formed. This work is about solving a variant of RVRP with dynamically changing orders and time windows constraints. In the real world application, during the working day, new orders often occur dynamically and need to be integrated into the routes planing. A Genetic Algorithm (GA) with a simple heuristic is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on Solomon's benchmark with different percentage of the orders revealed to the algorithm during operation time.