Applied Sciences

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Special Issue Information

Dear Colleagues,

Artificial Intelligence has succeeded in revolutionizing various aspects related to our everyday life, both in normal and pandemic life conditions. Transportation represents one of the base structures of our modern life as it insures the proper movement of goods, people and animals from one location to another, either by air, sea, road, cable, pipeline or space. On the other hand, logistics offers the needed knowledge for planning, implementing and controlling procedures that can ensure an efficient and effective transportation and storage of goods. In this context, the Special Issue is dedicated to the practical applications of Artificial Intelligence in transportation and logistics and plans to give an overview of the most recent advances in this field. This Special Issue is aimed at providing selected contributions on advances in both transportation and logistics by means of Artificial Intelligence. Additionally, the authors are encouraged to submit papers addressing the state-of-the-art or case studies featuring practical applications of Artificial Intelligence in transportation and logistics under various conditions.

Potential topics include, but are not limited to:

Role of Artificial Intelligence in transportation and/or logistics
Conceptual frameworks for Artificial Intelligence in transportation and/or logistic
Artificial Intelligence use cases and benefits in transportation and/or logistic
Artificial Intelligence for supply chain in logistics
Artificial Intelligence for route optimization
Demand prediction using Machine Learning or Deep Learning
Advancements of Artificial Intelligence techniques in transportation and/or logistics
Hybrid approaches in transportation and/or logistics modeling
Agent-based modeling for transportation and/or logistics
Specific applications in transportation and/or logistics solvable using Artificial Intelligence techniques
Practical case studies

Dr. Camelia Delcea
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
logistics
transportation
complex systems
traffic and transportation management
decentralized systems
disruptive technologies in logistics
artificial intelligence use cases
artificial intelligence benefits
agent-based modeling
route optimization
machine learning
deep learning

Published Papers (2 papers)

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Research

18 pages, 4732 KiB

Open AccessArticle

Coordinated Control Method of Bus Signal Priority and Speed Adjustment Based on Stop-Skipping

by Xuemei Zhou, Jiaojiao Xi, Zhen Guan and Guohui Wei

Appl. Sci. 2023, 13(8), 4803; https://doi.org/10.3390/app13084803 - 11 Apr 2023

Cited by 1 | Viewed by 1237

Abstract

There is an inherent coupling relationship between the time when buses arrive at the station and the time when they arrive the intersection, and it is essential to study the relationship as a whole to maximize the benefits of company operations and passenger services. In this study, a coordinated control method of signal priority and speed regulation in the stop-skipping mode at peak hours is proposed. First, the decision result of stop-skipping is obtained based on the historical passenger flow data. On this basis, the signal-priority decision is made for each vehicle in combination with the signal period and the arrival time of the intersection, and coordinated control is carried out in combination with the speed adjustment. The result of the genetic algorithm shows that cooperative control and prevention can minimize the passenger delay time and enterprise operation cost. The conclusions obtained in this research lay a theoretical foundation for company operation and signal-priority triggering mechanism. Full article

(This article belongs to the Special Issue Artificial Intelligence in Transport and Logistics)

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24 pages, 3342 KiB

Open AccessArticle

Solving the Container Relocation Problem by Using a Metaheuristic Genetic Algorithm

by Marko Gulić, Livia Maglić, Tomislav Krljan and Lovro Maglić

Appl. Sci. 2022, 12(15), 7397; https://doi.org/10.3390/app12157397 - 23 Jul 2022

Cited by 5 | Viewed by 2094

Abstract

Maritime transport is the backbone of international trade of goods. Therefore, seaports are of great importance for maritime transport. Container transport plays an important role in maritime transport and is increasing year by year. Containers transported to a container terminal are stored in container yards side by side and on top of each other, forming blocks. If a container that is not on top of the block has to be retrieved, the containers that are above the required container must be relocated before the required container is retrieved. These additional container relocations, which block the retrieval of the required container, slow down the entire retrieval process. The container relocation problem, also known as the block relocation problem, is an optimization problem that involves finding an optimal sequence of operations for retrieving blocks (containers) from a container yard in a given order, minimizing additional relocations of blocking containers. In this paper, the focus is on the two-dimensional, static, offline and the restricted container relocation problem of real-size yard container bays. A new method for resolving the container relocation problem that uses a genetic algorithm is proposed to minimize the number of relocations within the bay. The method is evaluated on well-known test instances, and the obtained results are compared with the results of various relevant models for resolving the container relocation problem. The results show that the proposed method achieves the best or the second-best result for each test instance within the test set. Full article

(This article belongs to the Special Issue Artificial Intelligence in Transport and Logistics)

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