Searcharxiv⌕ Search

arXiv subjects

Menouar Boulif

Publications and source records attributed to Menouar Boulif.

6 recordsLinked to original sources
arXiv2026

Node-Shift-Encoding Genetic Algorithm with fuzzy-enhanced reference tour to solve the bi-objective service-oriented TSP

The Travelling Salesman Problem (TSP) remains a key area of research in combinatorial optimization, with applications in logistics, manufacturing, and service delivery. This paper addresses a bi-objective service-oriented TSP in which the clients' ranks in the delivery path matter. Unlike conventional depot-based TSP formulations, the considered problem does not assume a distinguished depot or a fixed tour origin. To address this setting, we adapt the Miller--Tucker--Zemlin (MTZ)-based formulation and derive an original linearization of the resulting model, enabling its solution with off-the-shelf integer linear programming solvers. This adaptation avoids the rigid tour origin imposed by the conventional MTZ formulation, for which fixing the starting node does not affect the tour cost but can affect the objective in a customer-rank-sensitive TSP. To solve this problem, we present a Node-Shift-Encoding (NSE)-based Genetic Algorithm augmented with fuzzy reasoning to update the reference tour throughout the evolutionary process. Experimental evaluation on TSPLIB benchmarks demonstrates that the proposed method achieves improved performance compared with the classical NSE approach.

cs.CE↗
arXiv2025

Towards a better approach to the Vehicle Routing Problem

The Vehicle Routing Problem (VRP) is a fundamental challenge in logistics management research, given its substantial influence on transportation efficiency, cost minimization, and service quality. As a combinatorial optimization problem, VRP plays a crucial role in a wide range of real world applications, particularly in transportation, logistics, and delivery systems, due to its diverse formulations and numerous extensions. Over the years, researchers have introduced various VRP variants to address specific operational constraints, emerging industry requirements and optimize specific objectives, making it one of the most extensively studied problems in operations research. This article provides a comprehensive overview of VRP by exploring its theoretical foundations, discussing the limitations of its classical model, and introducing its key extensions. By systematically reviewing the diverse constraints, objectives, and variants examined in recent literature, this study aims to contribute to a deeper understanding of VRP while highlighting its ongoing evolution and relevance in modern optimization and decision making processes.

cs.CE↗
arXiv2023

Edge-set reduction to efficiently solve the graph partitioning problem with the genetic algorithm

The graph partitioning problem (GPP) is among the most challenging models in optimization. Because of its NP-hardness, the researchers directed their interest towards approximate methods such as the genetic algorithms (GA). The edge-based GA has shown promising results when solving GPP. However, for big dense instances, the size of the encoding representation becomes too huge and affects GA's efficiency. In this paper, we investigate the impact of modifying the size of the chromosomes on the edge based GA by reducing the GPP edge set. We study the GA performance with different levels of reductions, and we report the obtained results.

cs.NE↗
arXiv2023

A new node-shift encoding representation for the travelling salesman problem

This paper presents a new genetic algorithm encoding representation to solve the travelling salesman problem. To assess the performance of the proposed chromosome structure, we compare it with state-of-the-art encoding representations. For that purpose, we use 14 benchmarks of different sizes taken from TSPLIB. Finally, after conducting the experimental study, we report the obtained results and draw our conclusion.

cs.NE↗
arXiv2019

Heterogeneous Parallel Genetic Algorithm Paradigm

The encoding representation of the genetic algorithm can boost or hinder its performance albeit the care one can devote to operator design. Unfortunately, a representation-theory foundation that helps to find the suitable encoding for any problem has not yet become mature. Furthermore, we argue that such a best-performing encoding scheme can differ even for instances of the same problem. In this contribution, we present the basic principles of the heterogeneous parallel genetic algorithm that federates the efforts of many encoding representations in order to efficiently solve the problem in hand without prior knowledge of the best encoding.

cs.NE↗