Research

17 pages, 1369 KiB

Open AccessArticle

Optimal Integrated Single-Framework Algorithm for the Multi-Level School Bus Network Problem

by Amirreza Nickkar and Young-Jae Lee

Algorithms 2023, 16(11), 528; https://0-doi-org.brum.beds.ac.uk/10.3390/a16110528 - 16 Nov 2023

Viewed by 1095

In many states in the United States, school bus fleets are assigned to serve students sequentially at three levels—high school, middle school, and elementary school; however, in past studies, each of these stages in the problem was considered separately. This study introduces a [...] Read more.

In many states in the United States, school bus fleets are assigned to serve students sequentially at three levels—high school, middle school, and elementary school; however, in past studies, each of these stages in the problem was considered separately. This study introduces a novel integrated school bus problem that considers the sequential operation of fleets for all three levels in a unified framework. An example of a hypothetical network was developed and tested to demonstrate the developed algorithm. The algorithm successfully handled the integration of school buses’ optimal route generation while meeting all constraints. The results showed that the routings with the integrated single-framework algorithm can reduce the total costs by 4.5% to 12.4% compared to the routings with the separated level algorithm. Also, it showed that the total costs of the integrated routing framework for different morning and afternoon time windows are 8.28% less than the same routings (identically reversed) for the morning and afternoon time windows. Full article

(This article belongs to the Special Issue Intelligent Optimization for Transportation, Logistics and Vehicle Routing)

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17 pages, 6081 KiB

Open AccessArticle

Hybrid Approach to the Cost Estimation of External-Fleet Full Truckload Contracts

by Szymon Cyperski, Paweł D. Domański and Michał Okulewicz

Algorithms 2023, 16(8), 360; https://0-doi-org.brum.beds.ac.uk/10.3390/a16080360 - 27 Jul 2023

Cited by 2 | Viewed by 980

Abstract

Freight forwarding and transportation are the backbone of the modern economy. There are thousands of transportation companies on the market whose sole purpose is to deliver ordered goods from pickup to delivery. Transportation can be carried out by two types of fleets. A [...] Read more.

Freight forwarding and transportation are the backbone of the modern economy. There are thousands of transportation companies on the market whose sole purpose is to deliver ordered goods from pickup to delivery. Transportation can be carried out by two types of fleets. A company can have its own trucks, or it can use third-party companies. This transportation can be carried out in a variety of formulas, with full truckload being the most common for long routes. The shipper must be aware of the potential cost of such a service during the process of selecting a particular transport. The presented solution addresses this exact issue. There are many approaches, ranging from detailed cost calculators to machine learning solutions. The present study uses a dedicated hybrid algorithm that combines different techniques, spanning clustering algorithms, regression and kNN (k Nearest Neighbors) estimators. The resulting solution was tested on real shipping data covering multi-year contract data from several shipping companies operating in the European market. The obtained results proved so successful that they were implemented in a commercial solution used by freight forwarding companies on a daily basis. Full article

(This article belongs to the Special Issue Intelligent Optimization for Transportation, Logistics and Vehicle Routing)

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13 pages, 2663 KiB

Open AccessArticle

Autonomous Electric Vehicle Route Optimization Considering Regenerative Braking Dynamic Low-Speed Boundary

by Masoud Mohammadi, Poria Fajri, Reza Sabzehgar and Farshad Harirchi

Algorithms 2023, 16(6), 262; https://0-doi-org.brum.beds.ac.uk/10.3390/a16060262 - 24 May 2023

Cited by 1 | Viewed by 1119

Abstract

Finding the optimal speed profile of an autonomous electric vehicle (AEV) for a given route (eco-driving) can lead to a reduction in energy consumption. This energy reduction is even more noticeable when the regenerative braking (RB) capability of AEVs is carefully considered in [...] Read more.

Finding the optimal speed profile of an autonomous electric vehicle (AEV) for a given route (eco-driving) can lead to a reduction in energy consumption. This energy reduction is even more noticeable when the regenerative braking (RB) capability of AEVs is carefully considered in obtaining the speed profile. In this paper, a new approach for calculating the optimum eco-driving profile of an AEV is formulated using mixed-integer linear programming (MILP) while carefully integrating the RB capability and its limitations in the process of obtaining a driving profile with minimum energy consumption. One of the most important limitations of RB which has been neglected in previous studies is operation below the low-speed boundary (LSB) of electric motors, which impairs the energy extraction capability of RB. The novelty of this work is finding the optimal speed profile given this limitation, leading to a much more realistic eco-driving profile. Python is used to code the MILP problem, and CPLEX is employed as the solver. To verify the results, the eco-driving problem is applied to two scenarios to show the significance of considering a dynamic LSB. It is shown that for the route under study, up to 27% more energy can be harvested by employing the proposed approach. Full article

(This article belongs to the Special Issue Intelligent Optimization for Transportation, Logistics and Vehicle Routing)

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40 pages, 10583 KiB

Open AccessArticle

Efficient Mathematical Lower Bounds for City Logistics Distribution Network with Intra-Echelon Connection of Facilities: Bridging the Gap from Theoretical Model Formulations to Practical Solutions

by Zhiqiang Niu, Shengnan Wu and Xuesong (Simon) Zhou

Algorithms 2023, 16(5), 252; https://0-doi-org.brum.beds.ac.uk/10.3390/a16050252 - 12 May 2023

Viewed by 1749

Abstract

Focusing on the dynamic improvement of the underlying service network configuration, this paper aims to address a specific challenge of redesigning a multi-echelon city logistics distribution network. By considering the intra-echelon connection of facilities within the same layer of echelon, we propose a [...] Read more.

Focusing on the dynamic improvement of the underlying service network configuration, this paper aims to address a specific challenge of redesigning a multi-echelon city logistics distribution network. By considering the intra-echelon connection of facilities within the same layer of echelon, we propose a new distribution network design model by reformulating the classical quadratic assignment problem (QAP). To minimize the overall transportation costs, the proposed model jointly optimizes two types of decisions to enable agile distribution with dynamic “shortcuts”: (i) the allocation of warehouses to supply the corresponding distribution centers (DCs), and (ii) the demand coverage decision from distribution centers to delivery stations. Furthermore, a customized branch-and-bound algorithm is developed, where the lower bound is obtained by adopting Gilmore and Lawler lower Bound (GLB) for QAP. We conduct extensive computational experiments, highlighting the significant contribution of GLB-oriented lower bound, to obtain practical solutions; this type of efficient mathematical lower bounds offers a powerful tool for balancing theoretical research ideas with practical and industrial applicability. Full article

(This article belongs to the Special Issue Intelligent Optimization for Transportation, Logistics and Vehicle Routing)

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24 pages, 450 KiB

Open AccessArticle

Computational Approaches for Grocery Home Delivery Services

by Christian Truden, Kerstin Maier, Anna Jellen and Philipp Hungerländer

Algorithms 2022, 15(4), 125; https://0-doi-org.brum.beds.ac.uk/10.3390/a15040125 - 09 Apr 2022

Cited by 4 | Viewed by 2479

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

(This article belongs to the Special Issue Intelligent Optimization for Transportation, Logistics and Vehicle Routing)

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19 pages, 3143 KiB

Open AccessArticle

Swarm Robots Cooperative and Persistent Distribution Modeling and Optimization Based on the Smart Community Logistics Service Framework

by Meng Zhang and Bin Yang

Algorithms 2022, 15(2), 39; https://0-doi-org.brum.beds.ac.uk/10.3390/a15020039 - 26 Jan 2022

Cited by 6 | Viewed by 3142

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

(This article belongs to the Special Issue Intelligent Optimization for Transportation, Logistics and Vehicle Routing)

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20 pages, 2764 KiB

Open AccessEditor’s ChoiceArticle

A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning

by Ahmed Abdelmoamen Ahmed and Gbenga Agunsoye

Algorithms 2021, 14(8), 250; https://0-doi-org.brum.beds.ac.uk/10.3390/a14080250 - 21 Aug 2021

Cited by 17 | Viewed by 5814

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

(This article belongs to the Special Issue Intelligent Optimization for Transportation, Logistics and Vehicle Routing)

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12 pages, 434 KiB

Open AccessArticle

Lifting the Performance of a Heuristic for the Time-Dependent Travelling Salesman Problem through Machine Learning

by Gianpaolo Ghiani, Tommaso Adamo, Pierpaolo Greco and Emanuela Guerriero

Algorithms 2020, 13(12), 340; https://0-doi-org.brum.beds.ac.uk/10.3390/a13120340 - 14 Dec 2020

Cited by 4 | Viewed by 2413

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

(This article belongs to the Special Issue Intelligent Optimization for Transportation, Logistics and Vehicle Routing)

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