Route Planning with Dynamic Information from the EPLOS System

The paper presents the problem of distribution route planning with dynamic information about sudden customers\u27 needs. Particular attention was paid to dynamic vehicle route planning and its influence on the distance covered by a distribution vehicle. In the article, authors assume that the quick information about customers’ sudden needs is transferred from the EPLOS tool data base. Authors analyze the available literature on transport route optimization and propose a solution to the problem of distribution among customers with sudden needs. In order to present the impact of quick information influence on the distribution route minimization, a simulation model of the vehicle routing problem was generated in the FlexSim environment

O impacto do congestionamento no roteamento de veículos para a logística urbana

O tema desta dissertação consiste em perceber o impacto do congestionamento no roteamento de veículos para a logística urbana. Para que esse estudo seja realizado é necessário incorporar o congestionamento num modelo de VRP de forma a avaliar o seu impacto na definição das rotas dos veículos. De modo a se proceder a este estudo, foi adotado um modelo de cálculo de emissões com a finalidade de diminuir as mesmas e, por consequência, diminuir a utilização dos horários de maior trânsito. De seguida, foi usada uma formulação de VRP em que fosse possível incorporar o modelo de cálculo de emissões. O problema foi formulado para ser usado como um programa MIP (Mixed integer programming). Foi concluído que a implementação de um modelo de emissões em conjunto com uma formulação VRP leva a que seja possível estudar o congestionamento em zonas urbanas, tendo sido observado que é possível diminuir o uso de horários de congestionamento na logística urbana.The theme of this dissertation is to understand the impact of congestion on vehicle routing for urban logistics. For this study to be carried out it is necessary to incorporate congestion into a VRP model to assess its impact on the definition of vehicle routes. In order to carry out this study, a model for calculating emissions has been adopted with the aim of reducing emissions and thus reducing the use of peak traffic times. A VRP formulation was then used in which the emissions calculation model could be incorporated. The problem was formulated to be used as a MIP (Mixed integer programming) programme. It was concluded that the implementation of an emissions model in conjunction with a VRP formulation makes it possible to study congestion in urban areas, and it was observed that it is possible to reduce the use of congestion times in urban logistics

A Monarch Butterfly Optimization for the Dynamic Vehicle Routing Problem

The dynamic vehicle routing problem (DVRP) is a variant of the Vehicle Routing Problem (VRP) in which customers appear dynamically. The objective is to determine a set of routes that minimizes the total travel distance. In this paper, we propose a monarch butterfly optimization (MBO) algorithm to solve DVRPs, utilizing a greedy strategy. Both migration operation and the butterfly adjusting operator only accept the offspring of butterfly individuals that have better fitness than their parents. To improve performance, a later perturbation procedure is implemented, to maintain a balance between global diversification and local intensification. The computational results indicate that the proposed technique outperforms the existing approaches in the literature for average performance by at least 9.38%. In addition, 12 new best solutions were found. This shows that this proposed technique consistently produces high-quality solutions and outperforms other published heuristics for the DVRP

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications