Optimization, Modeling, Simulation, and Analytics

COMOSA 2025
Erscheinungsjahr 2026
ISBN: 978-3-032-15419-4
Verlag: Springer Nature Switzerland AG

Buch, Englisch, 415 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Springer Optimization and Its Applications

Emerging from the   (Hanoi, Vietnam, August 15–16, 2025), this volume brings together selected studies that explore various aspects of optimization, simulation, modeling, and analytics. The contributions cover a wide spectrum of domains, including industrial engineering, supply chain management, healthcare, finance, and other related fields.   The book provides a comprehensive analysis of contemporary operational challenges and state-of-the-art approaches developed to address them. The topics encompass techniques and applications in operations management, e-commerce, security, healthcare matching, and hospitality, among others. By integrating modern analytical tools—such as optimization models, simulation, data science, and artificial intelligence—the chapters illustrate how these methodologies can be effectively applied to enhance efficiency, adaptability, and sustainability in real-world systems.   With its integrated and interdisciplinary perspective, this volume offers a holistic understanding of how optimization and intelligent technologies can transform complex decision-making environments. It serves as a valuable reference for researchers, practitioners, and policymakers interested in advancing analytical and optimization-based solutions to contemporary operational and managerial problems.

Zielgruppe

Research

Autoren/Hrsg.

Hà, Minh Hoàng

Van Hop, Nguyen

Doanh, Nguyen Ngoc

Huynh Thi Thanh, Binh

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung

Inhaltsverzeichnis

Optimizing Production and Inventory Dynamics: An Advanced EPQ Model for Modern Supply Chains.- Time-Resolved Exergy Optimization of Hybrid Solar Dryers Using Finite Element Modeling and Multi-Dimensional Trade Off Analysis.- An adaptive algorithm utilizing the Fama-French three-factor model for improving portfolios.- Enhancing Learning to Cuts Approach for Solving NP-hard Problems.- Ruin probabilities of risk models with dependent claim sizes.- A Simulation-based Optimization Approach for Patient Admission Scheduling Problem Under Uncertainty.- Ultrasonic material characterization using physics-informed deep learning.- A Hybrid Genetic Search for Energy-Aware Multi-Trip Drone Routing With TIme Windows.- A Continuous-Time Framework for Adaptive Asset Allocation.- Complete Coverage Path Planning Framework for Oil Tank Inspection Robots in Unstructureed Environments.- Implementing Computer Vision for Pipeline Defect Detection.- Addressing data imbalance to enhance water level forecasting quality.- An efficient branch-and-bound algorithm for solving a new variant of the university course timetabling problem.- Multi-Goal Path Planning for Multi-Robot Systems in  Dynamic Environments Using Hybrid A* Algorithm and Dynamic Window Approach.- Vehicle to Grid Based Smart Charging Strategy for Electric Vehicles in Urban Microgrids: A Case Study in Ho Chi Minh City.- Cost Optimization in Choice-based Competitive Facility Location: An Exact Approach.- Learning from the Crowd: LLMs as Expert Filters for Multi-Agent Decision Making.- Facial Expression Recognition with Identity-Normalized Landmark Features.- A Dynamic Disruption Recovery Model for Multi-Layer Supply Networks.- Examining the Nexus between Green Growth Indicators and Economic Growth: Panel Data Evidence and Policy Insights.- Electric Traveling Salesperson Problem with Wireless Charging Lanes: A Mathematical Programming Approach.- Impact of Different Contents generated by Human and AI on Social Media Engagement.- Deep Temporal-Spatial Learning for Early Prediction of Human Blastocyst Formation from Time-Lapse Videos.- Comparative Analysis of Temporal Deep Learning Models  for Flood Level Forecasting: A Case Study in the Gianh River Basin.- Transparent AI for Disaster Management: Explainable Landslide Prediction Models to Support Decision-Making.- A Scenario-Based Optimization Model for Seasonal Procurement Under Demand Uncertainty.- An Integrated Fuzzy MCDM–Stochastic Optimization Approach for Sustainable and Resilient Supplier Selection and Order Allocation Under Pandemic Disruptions.

Über Autor(innen)

Minh Hoàng Hà is currently a faculty member at the Faculty of Data Science and Artificial Intelligence, College of Technology, National Economics University (NEU). His research interests lie in combinatorial optimization, with broad applications in logistics, healthcare, and finance. He has authored nearly 50 research publications, the majority of which appear in ISI Q1-ranked journals and A/A*-level conferences. As a member of the ORLab research group, he has contributed to the development of intelligent optimization solutions that enhance cost efficiency and operational performance for organizations and enterprises both in Vietnam and internationally.

Nguyen Van Hop currently works at the School of Industrial Engineering and Management, International University, VNU-HCMC. Dr. Hop does research in AIs for Optimization, Optimization for Manufacturing and Supply Chain Systems.

Doanh Nguyen Ngoc is an associate professor at CECS, VinUniversity. He is the leader of Comos lab, the Center of Environmental Intelligence (CEI), VinUniversity. His research is devoted to supporting interdisciplinary modeling-based approaches in large sustainability projects. In particular, he has developed a methodology to couple equation-based and agent-based models in the framework of digital twins to study complex socio-environmental systems (with topics as diverse as urban development, transportation, irrigation management, waste management, human epidemiology or fisheries management).

Huynh Thi Thanh Binh is Associate Professor and Vice Dean of the School of Information and Communication Technology, Hanoi University of Science and Technology.. Her current research interests: Artificial Intelligence, Algorithms and Optimization, Computational Intelligence, Memetic Computing, Evolutionary Multitasking. She has published more than 150 refereed academic papers. She is Associate Editor of the Swarm and Evolutionary Computation (2024–now), Engineering Applications of Artificial Intelligence Journal (2021–now), and IEEE Transactions on Emerging Topics in Computational Intelligence (2022–now). She has served as a regular reviewer and a program committee member of numerous prestigious academic journals and conferences, such as Transactions on Evolutionary Computation, Swarm and Evolutionary Computation, Applied Soft Computing, Information Sciences, Memetic Computing, Congress on Evolutionary Computation (CEC), The Genetic and Evolutionary Computation Conference (GECCO), NeurIPS... She is Chair of IEEE Vietnam section and a Executive member of IEEE Asia Pacific.