Chair of Operations, Economics & Strategy

Professor Weber Wins the 2025 ACIIS Best Paper Award

Published:31.10.25 — At the 2025 IEEE 7th International Conference on Applied Computational Intelligence in Information Systems (ACIIS), held October 20–22 at Universiti Teknologi Brunei in Brunei Darussalam, Prof. Weber received the Best Paper Award for his work on a robust aggregate scoring system for evaluating digital services. The winning paper, “Relatively Robust QoS and QoE Score Aggregation,” is forthcoming on IEEE Xplore as part of the conference proceedings.

Predicting the Status of Project Portfolios Using Markov Chains

Published:24.09.25 — Firms often manage hundreds of concurrent projects with uncertain status trajectories. A new conference paper by Prof. Thomas A. Weber (Chair of Operations, Economics and Strategy, EPFL) introduces a finite-state Markov-chain framework that tracks and forecasts the evolving composition of project portfolios. The method yields forward-looking estimates of success rates, average durations, and resource balance across active and idle projects, supporting earlier risk detection and data-driven reprioritization at the portfolio level. Presented at the 2025 IEEE 5th International Conference on Smart Information Systems and Technologies (SIST), the work demonstrates that the forecasts remain robust even when the underlying identification is moderately noisy, making the approach practical for real organizations with imperfect data.

A New Paradigm for Multicriteria Decision Making

Published:02.09.25 — The question of how decision makers can transparently trade off among several objectives is the topic of Prof. Weber’s latest contribution to robust multicriteria decision making, which is forthcoming in Management Science, the top journal of the field. The research addresses a central unsolved problem in multicriteria decision making, namely how to transparently trade off among different criteria, with performance guarantees. It builds on the notion of relatively robust decisions: choices that are Pareto‑efficient and maximize a performance index that provides an explicit worst‑case performance guarantee across all plausible weights.