4-5 October, 2024

autumn meeting of industrial mathematics

at Foz do Arelho, Portugal

https://www.spm.pt/PT-MATHS-IN/amim24

The goal of this event is the sharing of knowledge and experience in the field of Mathematics in Industry, focusing on the application of mathematics to solve complex real-world problems across various domains with economic, social, biological, and technological value. Encompassing a detailed account on two landmark topics and a tour on a wide variety of problems, amim'24 aims at furthering the establishment of links between mathematics and the real world, providing a great opportunity to researchers and practitioners alike.

A two-day summit of talks, short courses, and collaborations

Day 1

short courses, poster tour, contributed talks and the meeting dinner

Day 2

short courses and contributed talks

The registration fee of 100 euros includes the Friday and Saturday lunches and dinners.

Accommodation is not included.

Master's students are exempt from the registration fee, and there is the possibility of financial support for travel and accommodation expenses for a limited number of students.

CONTACT amim24.meeting@gmail.com

Short Courses

Emilio Carrizosa

Explainable Data Driven Decision Making

Data-driven decision-making processes, mostly based on Machine Learning tools, are frequently considered black boxes, and thus suspicious for decision-makers and for people affected by such decisions. In particular, discrimination against sensitive groups may exist (and not properly detected) due to biases in the data feeding the Machine Learning algorithms. Different mathematical models are being proposed to make the process more transparent, giving some hints on how decisions have been made, and what are the main features affecting such decisions. In this course, some recent work on the topic will be presented, including mathematical models for detecting relevant variables, finding counterfactual decisions, and explainable inverse optimization.

Jorge Orestes Cerdeira

Exploring mixed integer linear programming: applications to industry problems

Mixed integer linear programming (MILP) consists of optimizing a linear function subject to linear constraints, where certain variables must take integer values. MILP has applications in various fields, including logistics, manufacturing, finance, and telecommunications, among others. I will discuss the difficulties on problem-solving posed by the integrality constraints, depict optimization techniques tailored specifically for MILP, and give examples of using MILP to model various industry problems, including some challenges presented at different ESGI's editions.