Special Issue "Optimization, Processing, and Visualization of Data for Sustainability"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 November 2020).

Special Issue Editors

Dr. Patricia Ruiz
E-Mail Website
Guest Editor
University of Cadiz, Spain
Interests: intelligent transportation systems; optimization; wireless networks
Special Issues and Collections in MDPI journals
Dr. Juan Carlos de la Torre
E-Mail Website
Guest Editor
University of Cadiz, Spain
Interests: optimization; machine learning; software sustainability
Special Issues and Collections in MDPI journals
Dr. Bernabe Dorronsoro
E-Mail Website
Guest Editor
Computer Science Engineering, Department Engineering School, University of Cadiz, 11003 Cádiz, Spain
Interests: metaheuristics; optimization; multi-objective optimization; mobile ad hoc networks; cloud computing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

At present, data have immense value in the digital world. All computer-based systems gather any kind of data, and companies make use of them to get a huge value out of them, in many different senses. The amount of stored data is continuously growing, and there is a need for efficient techniques to allow getting the most out of it. We focus in this Special Issue on recent advances on data-based optimization, knowledge extraction, and visualization techniques, focused on the enhancement of the sustainability of a system, product, process, etc. Interesting applications of sustainability enhancement can be found in applications belonging to fields such as logistics, mobility, industry, networks, (mobile) computing, or smart cities, among many others.

This Special Issue aims at attracting outstanding research works proposing the application of tools based on novel techniques in fields such as:

- Evolutionary algorithms;
- Heuristics;
- Exact approaches;
- Single-/multiobjective optimization;
- Parallel computing;
- Machine learning;
- Deep learning;
- Big Data techniques;
- Data analytics;
- Visualization and computer graphics.

The topics of interest for the applications are, among others:

- Industry 4.0;
- Logistics;
- Dynamic/static networks;
- Smart cities;
- Intelligent transportation systems;
- Smart Grid;
- Scheduling;
- Computing, from small portable devices to large datacenters.

All high-quality submitted papers related to the listed topics will be considered for publication in this Special Issue, provided they are recommended for publication after the review process. All manuscript submissions and reviews will be handled by the MDPI submission system https://susy.mdpi.com/. All papers should be prepared according to the MDPI Guide for Authors.

Dr. Patricia Ruiz
Dr. Juan Carlos de la Torre
Dr. Bernabe Dorronsoro
Guest Editors

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 papers will be 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 2000 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.

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
On the Use of Composite Indicators for Mobile Communications Network Management in Smart Sustainable Cities
Appl. Sci. 2021, 11(1), 181; https://doi.org/10.3390/app11010181 - 27 Dec 2020
Viewed by 593
Abstract
Beyond 5G networks will be fundamental towards enabling sustainable mobile communication networks. One of the most challenging scenarios will be met in ultra-dense networks that are deployed in densely populated areas. In this particular case, mobile network operators should benefit from new assessment [...] Read more.
Beyond 5G networks will be fundamental towards enabling sustainable mobile communication networks. One of the most challenging scenarios will be met in ultra-dense networks that are deployed in densely populated areas. In this particular case, mobile network operators should benefit from new assessment metrics and data science tools to ensure an effective management of their networks. In fact, incorporating architectures allowing a cognitive network management framework could simplify processes and enhance the network’s performance. In this paper, we propose the use of composite indicators based on key performance indicators both as a tool for a cognitive management of mobile communications networks, as well as a metric which could successfully integrate more advanced user-centric measurements. Composite indicators can successfully synthesize and integrate large amounts of data, incorporating in a single index different metrics selected as triggers for autonomous decisions. The paper motivates and describes the use of this methodology, which is applied successfully in other areas with the aim of ranking metrics to simplify complex realities. A use case that is based on a universal mobile telecommunications system network is analyzed, due to technology simplicity and scalability, as well as the availability of key performance indicators. The use case focuses on analyzing the fairness of a network over different coverage areas as a fundamental metric in the operation and management of the networks. To this end, several ranking and visualization strategies are presented, providing examples of how to extract insights from the proposed composite indicator. Full article
Show Figures

Figure 1

Open AccessArticle
Facilitating Vulnerable Supplier Network Management Using Bicriterion Network Resilience Management Approach
Appl. Sci. 2020, 10(23), 8502; https://doi.org/10.3390/app10238502 - 28 Nov 2020
Viewed by 302
Abstract
This study aims to enable a high level of coordination to cope with increasing levels of uncertainty by computing supplier- and network-based resilience values. Our case study is based on a real-world highly connected global manufacturing firm based in Korea as a test [...] Read more.
This study aims to enable a high level of coordination to cope with increasing levels of uncertainty by computing supplier- and network-based resilience values. Our case study is based on a real-world highly connected global manufacturing firm based in Korea as a test environment to evaluate a proposed bicriterion network resilience model using resilience and network values, together with an ordering approach. An outranking methodology is used to determine the improvement priorities of suppliers to achieve a high level of overall network resilience. The results show that the effectiveness of a firm’s performance with respect to the entire supply chain may increase or decrease based on its embeddedness and connectivity within the supply network. This study is one of the first to provide an integrative (resilience capabilities and network attributes) approach to the supplier improvement model, future studies are encouraged to expand the model to different network settings. Full article
Show Figures

Figure 1

Open AccessArticle
A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities
Appl. Sci. 2020, 10(23), 8326; https://doi.org/10.3390/app10238326 - 24 Nov 2020
Viewed by 344
Abstract
An accurate prediction of freight volume at the sanitary facilities of seaports is a key factor to improve planning operations and resource allocation. This study proposes a hybrid approach to forecast container volume at the sanitary facilities of a seaport. The methodology consists [...] Read more.
An accurate prediction of freight volume at the sanitary facilities of seaports is a key factor to improve planning operations and resource allocation. This study proposes a hybrid approach to forecast container volume at the sanitary facilities of a seaport. The methodology consists of a three-step procedure, combining the strengths of linear and non-linear models and the capability of a clustering technique. First, a self-organizing map (SOM) is used to decompose the time series into smaller clusters easier to predict. Second, a seasonal autoregressive integrated moving averages (SARIMA) model is applied in each cluster in order to obtain predicted values and residuals of each cluster. These values are finally used as inputs of a support vector regression (SVR) model together with the historical data of the cluster. The final prediction result integrates the prediction results of each cluster. The experimental results showed that the proposed model provided accurate prediction results and outperforms the rest of the models tested. The proposed model can be used as an automatic decision-making tool by seaport management due to its capacity to plan resources in advance, avoiding congestion and time delays. Full article
Show Figures

Figure 1

Open AccessArticle
Construction of Analytical Models for Driving Energy Consumption of Electric Buses through Machine Learning
Appl. Sci. 2020, 10(17), 6088; https://doi.org/10.3390/app10176088 - 02 Sep 2020
Cited by 1 | Viewed by 460
Abstract
In recent years, the Taiwan government has been calling for the use of public transportation and has been popularizing pollution-reducing green vehicles. Passenger transport operators are being encouraged to replace traditional buses with electric buses, to increase their use in urban transportation. Reduced [...] Read more.
In recent years, the Taiwan government has been calling for the use of public transportation and has been popularizing pollution-reducing green vehicles. Passenger transport operators are being encouraged to replace traditional buses with electric buses, to increase their use in urban transportation. Reduced energy consumption and operating costs are important operational benefits for passenger transport operators, and driving behavior has a significant impact on fuel consumption. Although many literatures or real-world systems have addressed the issues related to reducing energy consumption with electric buses, these works do not involve the records collected from an on-vehicle battery management system (BMS). Accordingly, the results of analyses of existing works lack in-depth discussions, and therefore the applicability of existing works is insignificant. Therefore, in this study, driving data were collected using a battery management system (BMS), and vehicular power consumption was classified according to energy efficiency. Then, decision trees and random forest were applied to construct energy consumption analytical models. Finally, the driving behaviors that influence energy consumption were investigated. A case study was conducted in which a Taichung passenger transport operator’s electric bus driving data on urban routes were collected to construct energy consumption analytical models. The data consisted of two parts, i.e., vehicle records and route records. On the basis of these records, we considered the practicability and applicability of the analytical models by transforming the unstructured records into raw data. Passenger transport operators and drivers can leverage the obtained eco-driving indicators for different bus routes for energy savings and carbon reduction. Full article
Show Figures

Figure 1

Back to TopTop