Antonio Chaves – Optimization Online

Random-Key Optimization for 2D Irregular Packing with Reusable Area Evaluation

Published: 2026/07/19

Applications - OR and Management Sciences, Meta Heuristics, Parallel Algorithms, Production and Logistics cutting and packing, metaheuristic, random keys

The diverse constraints of industrial applications lead to variants of 2D irregular packing problems that require tailored solution methods. This paper addresses a real-world industrial challenge by proposing a new problem definition, the Maximum Reusable Contiguous Area Problem (MRCAP), and a novel metric, the Maximum Contiguous Area, developed to measure and maximize the contiguous unused … Read more

A parallel branch-and-cut and an adaptive metaheuristic to solve the Family Traveling Salesman Problem

Published: 2022/08/16, Updated: 2023/02/22

Antonio Chaves

Branch and Cut Algorithms, Combinatorial Optimization, Meta Heuristics biased random-key genetic algorithms, branch-and-cut, q-learning, traveling salesman problem

Abstract. CitationChaves et al., A parallel branch-and-cut and an adaptive metaheuristic to solve the Family Traveling Salesman Problem. Submitted to …

A support tool for planning classrooms considering social distancing between students

Published: 2021/04/16, Updated: 2021/12/29

Applications - OR and Management Sciences, Global Optimization, Optimization Software and Modeling Systems covid-19, location problems, mathematical model, maximal coverage, metaheuristic, overlap control

In this paper, we present the online tool salaplanejada.unifesp.br developed to assist the layout planning of classrooms considering the social distance in the context of the COVID-19 pandemic. We address both the allocation problem in rooms where seats are fixed as well as the problem in rooms where seats can be moved freely. For the … Read more

An Adaptive and Near Parameter-free BRKGA Using Q-Learning Method

Published: 2021/02/22, Updated: 2021/04/21

Meta Heuristics genetic algorithm, parameter control, q-learning, reinforcement learning

The Biased Random-Key Genetic Algorithm (BRKGA) is an efficient metaheuristic to solve combinatorial optimization problems but requires parameter tuning so the intensification and diversification of the algorithm work in a balanced way. There is, however, not only one optimal parameter configuration, and the best configuration may differ according to the stages of the evolutionary process. … Read more