Joint 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 Data (ISSN 2306-5729).
Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 857
Special Issue Editors
Interests: analytics; artificial intelligence; data science; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: computer simulation; logistics assistance systems; industrial IT systems; business processes; intralogistics; supply chains; urban last-mile delivery; data farming and data analysis
Special Issues, Collections and Topics in MDPI journals
Interests: simulation output analysis, statistical ranking and selection methods, and medical and humanitarian applications of operations research
Special Issues, Collections and Topics in MDPI journals
Interests: smart cities; transportation; vehicle routing problems; logistics; simheuristics
Special Issues, Collections and Topics 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 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. Data is an international peer-reviewed open access monthly journal published by MDPI.
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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
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