Special Issue "Intelligent Optimization for Transportation, Logistics and Vehicle Routing"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 5725

Special Issue Editors

Dr. Roberto Carballedo Morillo
E-Mail Website
Guest Editor
Faculty of Engineering . ICT Department, University of Deusto, Bizkaia, Spain
Interests: artificial intelligence; optimization; vehicle routing problems
Dr. Eneko Osaba
E-Mail Website
Guest Editor
Tecnalia Research & Innovation, 48160 Derio, Spain
Interests: bioinspired optimization; combinatorial optimization; artificial intelligence; metaheuristics; swarm intelligence
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Special Issue Information

Dear Colleagues,

Transport is a very relevant sector for contemporary society, both for companies and individuals. Public transport systems, for example, are used by almost the entire population, and their development affects our quality of life. In this sense, there are multiple types of transport systems, each with its own characteristics. They all share the same challenges: limited vehicle capacity, cost limits, service frequencies, and/or the geographical area covered. Furthermore, modeling and planning such transport systems is a very complex task. Regarding transportation in the business world, and because of the advance of technologies, logistics systems have become a cornerstone for companies. The fact that anyone can easily be well connected has led to advanced transport networks, which are very demanding, something that was less important in past times. These are the reasons an efficient logistic network can serve as a competitive advancement for companies and relevant business operations.

Thus, problems regarding the design and solution of issues of transportation, logistics, and routing networks have gained momentum in the scientific community today. The main reasons for the importance of these optimization problems are two-fold: the social interest they generate and their inherent scientific interest. On the one hand, routing problems are usually modeled to face real-world situations related to logistics and transport. For this reason, their efficient solution entails a profit, either of a social and/or a business nature. On the other hand, most of the problems in this field have a remarkable computational complexity. This is why their resolution poses a significant challenge for the related scientific community.

This Special Issue aims at disseminating the latest findings and research achievements in the areas of optimization and routing problems, with an intention to balance between theoretical research ideas and their practicability as well as industrial applicability. To this end, scholars and practitioners from academia and industrial fields are invited to submit high-quality original contributions to this Special Issue.

Dr. Roberto Carballedo Morillo
Dr. Eneko Osaba
Guest Editors

Manuscript Submission Information

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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. Algorithms is an international peer-reviewed open access monthly 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 1400 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

  • Optimization
  • Vehicle routing problem
  • Traveling salesman problem
  • Heuristics and metaheuristics
  • Swarm and evolutionary computation
  • Transportation and logistics

Published Papers (4 papers)

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Research

Article
Computational Approaches for Grocery Home Delivery Services
Algorithms 2022, 15(4), 125; https://doi.org/10.3390/a15040125 - 09 Apr 2022
Viewed by 883
Abstract
The steadily growing popularity of grocery home-delivery services is most likely based on the convenience experienced by its customers. However, the perishable nature of the products imposes certain requirements during the delivery process. The customer must be present when the delivery arrives so [...] Read more.
The steadily growing popularity of grocery home-delivery services is most likely based on the convenience experienced by its customers. However, the perishable nature of the products imposes certain requirements during the delivery process. The customer must be present when the delivery arrives so that the delivery process can be completed without interrupting the cold chain. Therefore, the grocery retailer and the customer must mutually agree on a time window during which the delivery can be guaranteed. This concept is referred to as the attended home delivery (AHD) problem in the scientific literature. The phase during which customers place orders, usually through a web service, constitutes the computationally most challenging part of the logistical processes behind such services. The system must determine potential delivery time windows that can be offered to incoming customers and incrementally build the delivery schedule as new orders are placed. Typically, the underlying optimization problem is a vehicle routing problem with a time windows. This work is concerned with a case given by an international grocery retailer’s online shopping service. We present an analysis of several efficient solution methods that can be employed to AHD services. A framework for the operational planning tools required to tackle the order placement process is provided. However, the basic framework can easily be adapted to be used for many similar vehicle routing applications. We provide a comprehensive computational study comparing several algorithmic strategies, combining heuristics utilizing local search operations and mixed-integer linear programs, tackling the booking process. Finally, we analyze the scalability and suitability of the approaches. Full article
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Article
Swarm Robots Cooperative and Persistent Distribution Modeling and Optimization Based on the Smart Community Logistics Service Framework
Algorithms 2022, 15(2), 39; https://doi.org/10.3390/a15020039 - 26 Jan 2022
Cited by 1 | Viewed by 896
Abstract
The high efficiency, flexibility, and low cost of robots provide huge opportunities for the application and development of intelligent logistics. Especially during the COVID-19 pandemic, the non-contact nature of robots effectively helped with preventing the spread of the epidemic. Task allocation and path [...] Read more.
The high efficiency, flexibility, and low cost of robots provide huge opportunities for the application and development of intelligent logistics. Especially during the COVID-19 pandemic, the non-contact nature of robots effectively helped with preventing the spread of the epidemic. Task allocation and path planning according to actual problems is one of the most important problems faced by robots in intelligent logistics. In the distribution, the robots have the fundamental characteristics of battery capacity limitation, limited load capacity, and load affecting transportation capacity. So, a smart community logistics service framework is proposed based on control system, automatic replenishment platform, network communication method, and coordinated distribution optimization technology, and a Mixed Integer Linear Programming (MILP) model is developed for the collaborative and persistent delivery of a multiple-depot vehicle routing problem with time window (MDVRPTW) of swarm robots. In order to solve this problem, a hybrid algorithm of genetically improved set-based particle swarm optimization (S-GAIPSO) is designed and tested with numerical cases. Experimental results show that, Compared to CPLEX, S-GAIPSO has achieved gaps of 0.157%, 1.097%, and 2.077% on average, respectively, when there are 5, 10, and 20 tasks. S-GAIPSO can obtain the optimal or near-optimal solution in less than 0.35 s, and the required CPU time slowly increases as the scale increases. Thus, it provides utility for real-time use by handling a large-scale problem in a short time. Full article
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Article
A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning
Algorithms 2021, 14(8), 250; https://doi.org/10.3390/a14080250 - 21 Aug 2021
Cited by 4 | Viewed by 1461
Abstract
The increasing ubiquity of network traffic and the new online applications’ deployment has increased traffic analysis complexity. Traditionally, network administrators rely on recognizing well-known static ports for classifying the traffic flowing their networks. However, modern network traffic uses dynamic ports and is transported [...] Read more.
The increasing ubiquity of network traffic and the new online applications’ deployment has increased traffic analysis complexity. Traditionally, network administrators rely on recognizing well-known static ports for classifying the traffic flowing their networks. However, modern network traffic uses dynamic ports and is transported over secure application-layer protocols (e.g., HTTPS, SSL, and SSH). This makes it a challenging task for network administrators to identify online applications using traditional port-based approaches. One way for classifying the modern network traffic is to use machine learning (ML) to distinguish between the different traffic attributes such as packet count and size, packet inter-arrival time, packet send–receive ratio, etc. This paper presents the design and implementation of NetScrapper, a flow-based network traffic classifier for online applications. NetScrapper uses three ML models, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Network (ANN), for classifying the most popular 53 online applications, including Amazon, Youtube, Google, Twitter, and many others. We collected a network traffic dataset containing 3,577,296 packet flows with different 87 features for training, validating, and testing the ML models. A web-based user-friendly interface is developed to enable users to either upload a snapshot of their network traffic to NetScrapper or sniff the network traffic directly from the network interface card in real time. Additionally, we created a middleware pipeline for interfacing the three models with the Flask GUI. Finally, we evaluated NetScrapper using various performance metrics such as classification accuracy and prediction time. Most notably, we found that our ANN model achieves an overall classification accuracy of 99.86% in recognizing the online applications in our dataset. Full article
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Article
Lifting the Performance of a Heuristic for the Time-Dependent Travelling Salesman Problem through Machine Learning
Algorithms 2020, 13(12), 340; https://doi.org/10.3390/a13120340 - 14 Dec 2020
Cited by 1 | Viewed by 1226
Abstract
In recent years, there have been several attempts to use machine learning techniques to improve the performance of exact and approximate optimization algorithms. Along this line of research, the present paper shows how supervised and unsupervised techniques can be used to improve the [...] Read more.
In recent years, there have been several attempts to use machine learning techniques to improve the performance of exact and approximate optimization algorithms. Along this line of research, the present paper shows how supervised and unsupervised techniques can be used to improve the quality of the solutions generated by a heuristic for the Time-Dependent Travelling Salesman Problem with no increased computing time. This can be useful in a real-time setting where a speed update (or the arrival of a new customer request) may lead to the reoptimization of the planned route. The main contribution of this work is to show how to reuse the information gained in those settings in which instances with similar features have to be solved over and over again, as it is customary in distribution management. We use a method based on the nearest neighbor procedure (supervised learning) and the K-means algorithm with the Euclidean distance (unsupervised learning). In order to show the effectiveness of this approach, the computational experiments have been carried out for the dataset generated based on the real travel time functions of two European cities: Paris and London. The overall average improvement of our heuristic over the classical nearest neighbor procedure is about 5% for London, and about 4% for Paris. Full article
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