2026 The International Workshop on Data Science, Industrial Engineering, Operations Research, and Management Science,DIORAMA 2026(The 2nd CMDS Advanced AI Seminar, FY2025)
- Dates
- 21 March 2026 (15:00–17:30)
22 March 2026 (10:00–11:40) - Format
- Hybrid (On-site at Kobe University & Online via Zoom)
- Venue
- 21 March
Takigawa Memorial Academic Exchange Hall (Main Conference Room)
https://www.kobe-u.ac.jp/ja/about/outline/facilities/takigawa/
22 March
Graduate School of Engineering, LR Building, Room LR202
https://www.kobe-u.ac.jp/ja/campus-life/general/access/rokko/rokkodai2/ - Registration
- https://forms.gle/785k6cWHJKGGCZDi8
• Participants attending the reception are kindly requested to register by 14 March.
• On-site participation (without reception) is also accepted on the day of the event. - Sponsored by
- Center for Mathematical and Data Sciences (CMDS), Kobe University
JSPS Grant Numbers: 23K26334, 24K07946 - Workshop Overview
- In the face of growing uncertainty, complexity, and market risks in socio-economic systems, effective decision-making has become more critical than ever. Firms, policymakers, and public institutions are increasingly required to design strategies that balance efficiency, sustainability, and resilience. To address these challenges effectively, they can draw on advanced theories and methodologies from data science, industrial engineering, operations research, and management science. This workshop explores the application of these analytical and quantitative approaches to pressing actual problems across key domains, including energy systems, environmental management, healthcare operations, and supply chain networks. By integrating modeling, optimization, data-driven analysis, and decision-support methodologies, we aim to deepen the understanding of how complex systems can be designed, managed, and regulated more effectively. Through rigorous analysis of contemporary issues and methodological innovations, the workshop highlights how research insights can contribute to sustainable development, risk mitigation, and resilient management practices. It also fosters interdisciplinary dialogue between academia and practice, encouraging solutions that are both theoretically grounded and practically implementable.
- Program
- 21 March 2026
- 15:00–16:00 Invited Lecture Session
Title:
Power Play: The Impact of Harnessing Data Centers Spatial Workload Flexibility on Electricity Market
Speaker:
Prof. Yihsu Chen
Department of Electrical and Computer Engineering Jack Baskin School of Engineering University of California, Santa Cruz - Abstract:
The rapid adoption of artificial intelligence (AI) has ushered in an era of computational demand, with large language models (LLMs) at the forefront since 2022. This unprecedented growth in loads, induced by the computational demand of data centers, poses daunting challenges to the electric power system. One common belief is that the flexibility provided by data center operations might attenuate these impacts by spatially migrating workloads to modular data centers (MDCs), which are typically located at the network edge. In this talk, I will present our ongoing work that considers the optimization problems faced by the hyperscaler and MDCs in addition to consumers, producers, and the electric grid operator, where the hyerscaler enters an agreement to lease MDCs while ensuring that the required service level objectives (SLOs) are met. At the same time, the hyperscaler minimizes operating costs while accounting for associated emissions. The overall market model is formulated as a complementarity problem where the proof is provided showing the existence and uniqueness of the solutions. When applying to a case study, we show that even with a provision that requires MDCs to disclose the CO2 emissions associated with their energy supply sources, renting less polluting MDCs is unlikely to yield meaningful emission reductions due to so-called contract-reshuffling. The situation can be mitigated when conventional loads are supplied by forward contracts through power purchase agreements. We also demonstrate that while direct load reduction via workload migration at the hyperscaler's host bus can effectively lower local power prices, this may inadvertently inflate prices at other locations, ultimately increasing total consumer load payments across the network. The increase in average load payments is positively correlated with the total grid load. Furthermore, it can also exacerbate price volatility, necessitating additional ancillary service procurement to ensure stable grid operations.
- 16:10–17:30 Session1: Decision-making under Uncertainty
1-1 - Optimal Fuel Procurement in the Renewable Transition with Price Volatility and Policy Targets
*Makoto Shimoshimizu1, Kazuya Ito1, Ryuta Takashima1
1Department of Industrial and Systems Engineering, Tokyo University of Science - 1-2
- Energy Transition Governance under Uncertainty: Comparative Insights from Climate and Energy Fieldwork in Switzerland and Australia
*Daneal Yuerae1, Junichiro Oda1
1Department of Resource Policy and Management, Faculty of International Resource Sciences, Akita University - 1-3
- Inventory Control Using Stochastic Processes: Explicit Analysis of Reorder Times and Total Costs via Lévy Processes
*Ryoya Koide1, Yurika Ono2, Aya Ishigaki2
1Department of Mathematics, Tokyo University of Science
2Department of Industrial and Systems Engineering, Tokyo University of Science - 1-4
- Long-Term Equilibrium Analysis of Feed-in Premiums and Carbon Taxes in Electricity Markets
*Hirotaka Hiraiwa1, Kazuya Ito1, Ryuta Takashima1
1Department of Industrial and Systems Engineering, Tokyo University of Science - 22 March 2026
10:00-11:40 Session2: Resource Allocation in IE OR
2-1 - When Will CO₂-to-Fuels Become Competitive? Mapping the Window of Opportunity via Scenario-Grid Optimization
*Jundai Koketsu1, Aya Ishigaki1, Ryuta Takashima1
1Department of Industrial and Systems Engineering, Tokyo University of Science - 2-2
- Contract Timing and Pricing of Power Purchase Agreements under Uncertainty: An Equilibrium Approach
*Yutaro Oga1, Kazuya Ito1, Ryuta Takashima1
1Department of Industrial and Systems Engineering, Tokyo University of Science - 2-3
- A Prediction Method for Life Satisfaction Using the Kokuho Database for Designing Preventive Care Interventions
*Karen Shinohara1, Aya Ishigaki1, Taku Harada1
1Department of Industrial and Systems Engineering, Tokyo University of Science - 2-4
- The Influence of Surgery Team Dynamics on Surgery Overtime
*Aurelius Aaron1, Mari Ito2, Tokito Koga3
1School of Accounting and Finance, The Hong Kong Polytechnic University
2Center for Mathematical and Data Sciences, Kobe University
3Department of Anesthesiology, Hyogo Prefectural Nishinomiya Hospital - 2-5
- Design of a Recommendation System for Demand Forecasting Methods to Support Ordering Decision-Making
*Yurika Ono1, Shota Kimoto1, Daichi Arimizu2, Takeru Moriyama2, Takayuki Itsui2, Aya Ishigaki1
1Department of Industrial and Systems Engineering, Tokyo University of Science
2Mitsubishi Electric Corporation
