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

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

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• 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