Documents

Actions Registration Registration Fees Deadlines and Policies Submit Paper Guidelines Preparing your Presentation Templates Glossary AI Tools Author's Login Reviewer's Login Ethics of Review Information Conference Details Important Dates Technical Program Social Event Call for Papers Program Committee Event Chairs Keynote Lectures Best Paper Awards Satellite Events Workshops Special Sessions Tutorials Demos Panels Travel and Accommodation Conference Venue About the Region Reaching the City Visa Information Hotel Reservation Partners Academic Partners Industrial Partners Institutional Partners Media Partners Partner Events Publication Partners Previous Conferences Websites Abstracts Invited Speakers Awards Books Published

Keynote Lectures

Solving the Inventory Routing Problem with Logistic Ratio Through Iterated Local Search
Claudia Archetti, University of Brescia, Italy, Italy

Analytic Allies: Fostering Synergy between Artificial Intelligence and Operations Research
Amanda Gustave, Chief Data Officer for Supreme Allied Commander, NATO (SACEUR), Belgium, Belgium

Operations Research for the Common Good
María Teresa Ortuño, Complutense University of Madrid, Spain, Spain

Solving the Inventory Routing Problem with Logistic Ratio Through Iterated Local Search

Claudia Archetti
University of Brescia, Italy

Brief Bio
Claudia Archetti is Professor of Operations Research at University of Brescia. From September 2021 to September 2024, she was Full Professor in Operations Research at ESSEC Business School in Paris. The main areas of the scientific activity are: models and algorithms for vehicle routing problems; mixed integer mathematical programming models for the minimization of the sum of inventory and transportation costs in logistic networks; exact and heuristic algorithms for supply-chain management; reoptimization of combinatorial optimization problems. She is author of more than 100 papers in international journals. She is co-Editor in Chief of Networks. She was VIP3 of EURO, the Association of European Operational Research Societies, in charge of publications and communication.

Abstract
The Inventory routing problem (IRP) aims at determining the optimal delivery plan of a single commodity distributed from one supplier to a set of customers over a planning horizon. The goal is to minimize the total cost which is given by the sum of transportation cost and inventory cost, the last one being paid both at the supplier and customers for the quantity hold in inventory. Because of the inventory cost, what typically happens in the optimal solution of the IRP is that customers have no inventory at the end of the horizon, which might clearly generate issues at the beginning of the next planning cycle. In practice, companies often consider a different objective function, i.e., minimizing the logistic ratio, which corresponds to ratio of the total transportation cost to the total quantity distributed. Implicitly, minimizing the logistic ratio corresponds to minimizing the unitary transportation cost. While this fractional objective function makes a lot of sense in practical applications, it creates challenges in the design of efficient solution approaches.
We present a new heuristic algorithm to tackle the IRP with logistic ratio which is based on Iterated Local Search (ILS). The algorithm has the advantage of being easy to understand and implement, combining classical operators with “smart” moves tailored to the problem. Tests on benchmark instances where the algorithm is compared with existing approaches show that, despite its simplicity, the algorithm beats the competitors and is currently the state-of-the-art for the problem.

Analytic Allies: Fostering Synergy between Artificial Intelligence and Operations Research

Amanda Gustave
Chief Data Officer for Supreme Allied Commander, NATO (SACEUR), Belgium

Brief Bio
Lt Col Amanda Gustave is currently active duty Air Force in the Operations Research career field (15A). She is currently stationed at NATO’s Supreme Headquarters Allied Powers Europe (SHAPE), in Mons, Belgium, serving as the Chief Data Officer (CDO). As CDO, she is enabling the alliance to leverage the power of data as a warfighting capability and safeguard the security and freedom of all alliance members. Other notable roles include: CDO, Joint Staff (J35) Global Force Management; Chief Analyst, U.S. Air Forces Europe and Africa. She is also currently serving on the Executive Committee of the Military Operations Research Society (MORS).

Abstract
This lecture focuses on the practical application of AI and OR across the military domain. The integration of artificial intelligence (AI) within military operations research (OR) represents many transformative opportunities for analysts to significantly enhance their analytical capabilities and expand their professional impact in an increasingly data-driven world. By embracing AI technologies such as machine learning algorithms, natural language processing, and automated optimization techniques, operations research professionals can revolutionize their approach to complex problem-solving, enabling them to tackle larger datasets, uncover sophisticated patterns, and optimize dynamic systems with remarkable efficiency across diverse applications. This powerful integration elevates the military OR analyst's role from traditional computational tasks to high-value strategic impact. AI-enhanced methodologies provide faster time-to-insight, more comprehensive scenario analysis, and superior decision-support capabilities required for military decision making at the strategic, operational and tactical level. This lecture will focus on several practical applications of the integration of AI and OR and how it’s shaping complex military warfighting scenarios—including battlefield resource allocation, tactical decision-making under uncertainty, force deployment optimization, and multi-domain operational planning. With these tangible examples, it highlights the importance of AI adoption in a military context as no longer optional, but essential. Building an “alliance” between AI and OR creates a powerful synergy that addresses modern operational challenges with superior speed, accuracy, and strategic impact.

Operations Research for the Common Good

María Teresa Ortuño
Complutense University of Madrid, Spain

Brief Bio
M. Teresa Ortuño is Professor of Operations Research at University Complutense of Madrid, Spain. She has worked in Reliability and Simulation Systems, Integer and Stochastic Programming, Production Planning, Combinatorial Optimization and Logistics. Prof. Ortuño's current scientific work is centred on the field of Humanitarian Logistics, green technologies and wildfires. She has more than 50 research works, most of them published in international journals. She is part of the research group HUMLOG (Decision Aid Models in Logistics and Disaster Management) at the University Complutense and has been member of several university cooperation for development projects. She has also been part of different European programs, as the HURRRICANE project developing models for Crisis and Natural Emergency.

Abstract
Operations Research is a powerful tool for promoting the common good. By applying OR techniques, decision-making processes can be improved in many areas, such as healthcare, environmental sustainability or humanitarian operations, among others. In this talk several applications of OR for the common Good will be presented, focusing on aspects such as fairness, equity, safety, security and uncertainty. Special attention will be given to models developed in the humanitarian logistics context, in which the ponent has worked for the last 15 years.