Urban Air Logistics with Unmanned Aerial Vehicles (UAVs): Double-Chromosome Genetic Task Scheduling with Safe Route Planning

Smart Cities, Journal Year: 2024, Volume and Issue: 7(5), P. 2842 - 2860

Published: Oct. 6, 2024

In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order select the most suitable assignment for each task. This paper presents development methodology of evolutionary-based optimization framework designed tackle specific formulation Drone Delivery Problem (DDP) with charging hubs. The proposed is based on double-chromosome encoding logic. goal algorithm find optimal (and feasible) UAV assignments such that (i) tasks’ due dates are met, (ii) energy consumption model minimized, (iii) re-charge tasks allocated ensure service persistency, (iv) risk-aware flyable paths included paradigm. Hard soft constraints defined optimizer can also very demanding instances DDP, as tens random temporal deadlines. Simulation results show how algorithm’s influences capability UAVs assigned different constraints. Monte Carlo simulations corroborate two realistic scenarios city Turin, Italy.

Language: Английский

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Short-term noise annoyance towards drones and other transportation noise sources: A laboratory study

The Journal of the Acoustical Society of America, Journal Year: 2024, Volume and Issue: 156(4), P. 2578 - 2595

Published: Oct. 1, 2024

Noise from unmanned aerial vehicles, commonly referred to as “drones,” will likely shape our acoustic environment in the near future. Yet, reactions of population this new noise source are still little explored. The objective study was investigate short-term annoyance drones a controlled laboratory experiment. Annoyance (i) two quadcopters different sizes relation common contemporary transportation sources (jet aircraft, propeller helicopters, single car passbys), and (ii) drone maneuvers (takeoff; landing; high, medium, low flybys) flown at speeds elevations systematically assessed. results revealed that, same sound exposure level, perceived substantially more annoying than other airborne vehicles passenger cars. Furthermore, for maneuvers, landings, takeoffs flybys, speed. Different loudness metrics (LAE, LDE, effective psychoacoustic level) accounted ratings an equal degree. An analysis parameters highlighted significant link between tonality, sharpness, level. suggest perception increased potential drones, which require specifically tailored legislation.

Language: Английский

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Last Mile Drone Delivery: Complexity and Research Challenges

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 404 - 417

Published: Jan. 1, 2025

Language: Английский

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How Criteria Weights Influence Performance in Evaluating Logistic Productivity: An Application in the Emerging Markets Logistics Index

Verimlilik dergisi, Journal Year: 2025, Volume and Issue: PRODUCTIVITY FOR LOGISTICS, P. 1 - 28

Published: Feb. 3, 2025

Purpose: The differences between the criteria affecting logistics performance of countries and their importance levels are meaningful in terms policy development processes. It has been determined that weighted equally emerging markets index. For this reason, study reweighted Emerging Markets Logistics Index investigated effects weighting on ranking. In respect, aims to make index more objective. Methodology: study, Multi-Criteria Decision Making methods were utilized. Within context, MEREC (Method Based Removal Effects Criteria) was used determine weights, while MABAC (Multi Attributive Border Approximation Area Comparison) MAIRCA Ideal Real Comparative Analysis) preferred rank alternatives. Findings: it concluded values consistent with literature. Additionally, new weights obtained have an effect ranking countries. Orginality: is important provide opportunity develop infrastructure increase productivity a platform for implementation technologies operations. Furthermore, these enable diversification services through expanding consumer demand. This differs from other studies literature because Agility (AEMLI) instead Logistic Performance (LPI) MEREC-based MABAC-MAIRCA methods.

Language: Английский

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Evaluation of Machine Learning and Ensemble Learning Models for Classification Using Delivery Data

Verimlilik dergisi, Journal Year: 2025, Volume and Issue: PRODUCTIVITY FOR LOGISTICS, P. 89 - 104

Published: Feb. 3, 2025

Purpose: This study aims to evaluate the performance of various machine learning and ensemble models in classifying delivery times using Amazon data. Fast deliveries' role providing a competitive advantage boosting customer loyalty highlights importance this study. Methodology: The research employs dataset 43,739 records with 15 features. Data preprocessing steps include handling missing values, encoding categorical variables, calculating geospatial distances, normalizing Advanced techniques (e.g., KNN, SVM, Logistic Regression) methods ExtraTrees, AdaBoost) were systematically compared based on accuracy, precision, recall, F-score. Findings: Ensemble models, particularly those NB, LDA as base ET meta model, achieved highest accuracy (99.89%) F-score (99.89%). These results underscore potential such optimize logistics operations, reduce delays, enhance satisfaction. Originality: demonstrates effectiveness complex data, contributing optimizing efficiency enhancing Additionally, application large-scale data structures is unique terms its contribution literature. proposed framework offers scalable solution for real-time predictive modeling optimization.

Language: Английский

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