Special Issue "Applications of Data Analytics, Simulation-Optimization, and Machine Learning in Services: From Sustainable Transportation and Supply Chains to Smart Cities, Health Care and Finance"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 30 October 2021.

Special Issue Editors

Prof. Dr. Angel A. Juan
E-Mail Website
Guest Editor
IN3 - Department of Computer Science, Multimedia & Telecommunication, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
Interests: heuristics; metaheuristics; simulation; simheuristics; biased randomization; agile optimization; learnheuristics
Special Issues and Collections in MDPI journals
Prof. Dr. David Goldsman
E-Mail Website
Guest Editor
Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, GA 30332, USA
Interests: simulation output analysis, statistical ranking and selection methods, and medical and humanitarian applications of operations research
Special Issues and Collections in MDPI journals
Prof. Dr. Javier Faulin
E-Mail Website
Guest Editor
Operations Research and Statistics, Public University of Navarre, 31006 Pamplona, Spain
Interests: transportation and logistics; vehicle routing problems and simulation modelling and analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Many services today require the use of data analytics tools, machine learning methods, and simulation-optimization models. This is the case, for instance, of real-world practices in logistics & transportation, smart cities, health care, finance & insurance, telecommunication networks, manufacturing & production, e-commerce & e-marketing, energy & water consumption, higher education, and many other fields. In particular, we are interested in combinations of several methodologies as an efficient way to support intelligent decision-making in different services, especially those that might have a noticeable impact on citizens' quality of life. Of course, this includes relevant aspects such as sustainability, resilience, and fast adoption of emergent data-driven and environment-friendly technologies in urban and metropolitan areas.

This Special Issue aims to present a collection of high-quality papers on the aforementioned topics. Both methodological as well as practical contributions are welcome. The Special Issue is open to well-known researchers in these topics. In particular, this Special Issue is strongly connected to the topics covered in several tracks of the Winter Simulation Conference (WSC). Extended versions of the best papers presented there (as well as at other conferences of similar quality) are also welcome. Still, this Special Issue is open to other submissions as well.   

Prof. Dr. Angel A. Juan
Prof. Dr. Markus Rabe
Prof. Dr. David Goldsman
Prof. Dr. Javier Faulin
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. 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

  • Service Science
  • Simulation
  • Heuristics and metaheuristics
  • Simheuristics
  • Sustainable Transportation & Logistics
  • Resilient Networks
  • Supply chain management
  • Smart cities
  • Intelligent transportation systems
  • Sustainable transportation and logistics
  • Simulation-based optimization
  • Machine learning
  • Learnheuristics
  • Biased-randomized algorithms
  • Industry 4.0

Published Papers (1 paper)

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Research

Article
Analysis and Prediction of Carsharing Demand Based on Data Mining Methods
Algorithms 2021, 14(6), 179; https://doi.org/10.3390/a14060179 - 05 Jun 2021
Cited by 1 | Viewed by 803
Abstract
With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, [...] Read more.
With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, carsharing operators are faced with problems, such as imbalance in vehicle demand at stations. Therefore, scientific prediction of users’ travel demand is important to ensure the efficient operation of carsharing. The main purpose of this study is to use gradient boosting decision tree to predict the travel demand of station-based carsharing users. The case study is conducted in Lanzhou City, Gansu Province, China. To improve the accuracy, gradient boosting decision tree is designed to predict the demands of users at different stations at various times based on the actual operating data of carsharing. The prediction results are compared with results of the autoregressive integrated moving average. The conclusion shows that gradient boosting decision tree has higher prediction accuracy. This study can provide a reference value for user demand prediction in practical application. Full article
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