Research Context: The Brazilian Unified Health System faces overload due to high demand for emergency services. Primary Health Care (PHC) plays a central role in mitigating this issue but suffers from the uneven distribution of human resources. Scientific and/or Practical Problem: The lack of optimized strategies for professional allocation generates imbalances among PHC units, resulting in service deficits in some regions and underutilization in others. Proposed Solution and/or Analysis: This study proposes a decision support system that uses open health data and mathematical optimization models to reallocate professionals across primary healthcare units. The system is formulated as an Integer Linear Programming (ILP) problem, aiming to maximize the minimum Service Capacity Margin (MCA), which reflects the balance between supply and demand. Related IS Theory: The research draws on Information Systems theories focused on decision support, such as Decision Support Systems (DSS) Theory, and mathematical modeling approaches in organizational contexts. Research Method: An ILP model (MAX-MCA) was developed to allocate professionals by category in Primary Health Care units. Demand was estimated from sociodemographic data, distributed using Voronoi diagrams, and adjusted based on actual service production from public records. Supply was calculated from the weekly working hours of professionals and average productivity standards defined by the respective professional councils. Summary of Results: Experiments were conducted in the municipalities of Ribeirão Preto and Guarulhos. In both scenarios, the model eliminated deficits and increased MCA across all analyzed categories, demonstrating more balanced and efficient utilization of human resources. Contributions and Impact to IS area: This work illustrates how optimization models integrated with Information Systems can support decision-making, promoting greater equity and efficiency in resource allocation. The study contributes to advancing operational research applications in health management.

Cloud computing has emerged as a popular computing paradigm, providing scalable and pay-per-use services to execute a variety of tasks. However, selecting the most suitable cloud services for a client can be challenging, as it involves taking into account the service characteristics and a set of client restrictions. In this paper, we formalize the problem of Service Selection under Uncertainty (SSuU) as an optimization problem. Our goal is to allocate tasks to appropriate cloud services while considering the probability of Service Level Agreement (SLA) violations and a set of client restrictions. We introduce an efficient dynamic programming approach to calculate the probability of SLA violations incrementally over time. We prove that the SSuU problem is strongly NP-complete, and propose an Integer Linear Programming (ILP) formulation and an Iterated Local Search-based algorithm for tackling it. To facilitate evaluation, we also introduce a set of benchmark instances for the SSuU problem. We extensively evaluate our proposed solutions on 94 input instances and compare them to the exact method (i.e., which always produces the optimal solution). Our results demonstrate that the metaheuristic approach is significantly faster and leads to exact solutions in the great majority of the evaluation scenarios.

Given a collection L of n subsets of a finite set R, the Maximum Intersection of k-Subsets problem (kMIS) aims to select a subset L' ⊆ L with |L'| = k such that the intersection of the subsets in L' is maximized. This problem is 𝒩𝒫 -hard and has applications in data anonymization, bioinformatics, recommendation systems, and various other fields. In this work, we propose an Iterated Local Search (ILS) algorithm for solving the kMIS. The local search phase uses the swap(1,1) neighborhood structure. To enhance efficiency, we introduce an auxiliary data structure which precomputes partial intersections, reducing the complexity of evaluating a neighboring solution from O(k^2) to O(k). The proposed ILS was evaluated against two state-of-the-art metaheuristics from the literature on 238 benchmark instances. The experimental results demonstrate that our approach efficiently finds high-quality solutions, establishing its competitiveness for solving the kMIS.

Broadcasting is an essential operation in distributed systems, with a wide range of applications. This study is focused on solving the Weighted Minimum Broadcast Time (WMBT), a problem that extends the classical Minimum Broadcast Time problem (MBT) by incorporating costs associated with each communication operation. We propose five contributions to the WMBT: (i) an integer linear programming model, (ii) two greedy algorithms, (iii) two Biased Random-Key Genetic Algorithms (BRKGAs), (iv) a lower bound algorithm, (v) a reduction rule to decrease an instance size, and (vi) a method to create instances with known optimal solutions. Our novel approaches are compared with state-of-the-art methods using large-scale synthetic instances. The experimental results demonstrate the effectiveness of our proposals. The greedy algorithms attains the best known solutions in a significant number of instances, while the two BRKGAs further enhance this performance, surpassing the greedy algorithms in many of the tested instances.

A natural constraint in real-world applications is avoiding conflicting elements in problem solutions. Let = (, ) be a graph where each edge E has a positive integer weight (), and let = (, ) be a conflict graph such that subset of and each edge = 12 E represents a conflict between two edges 1, 2 E . In the MINIMUM CONFLICT-FREE SPANNING TREE (MCFST) problem, we are asked to find a spanning tree avoiding pairs of conflicting edges (if such a tree exists) with minimum cost. In contrast to the polynomial-time solvability of MINIMUM SPANNING TREE, to determine whether an instance (, ) of MCFST admits a feasible solution is an NP- complete problem. In this paper, we present a multivariate complexity analysis of MCFST by considering particular classes of graphs and . We show that the problem of determining whether an instance (, ) of MCFST has a feasible solution is NP-complete even if is a bipartite planar subcubic graph, and is a disjoint union of paths with three vertices. Contrastingly, we show that when is complete and is bipartite, then a solution for (, ) can be found in linear time, while the problem of finding an optimal solution is NP-hard. Also, we present (in)approximability results for MCFST on complete graphs , and a parameterized algorithm regarding the distance from the conflict graph to a hereditary graph class T for which MCFST on E T is polynomial-time solvable.

Planning the public transportation system of a city is a complex process that depends on various factors, including transportation modes, origin–destination demands, service quality and reliability, and operational costs. The vehicle frequency setting (FS) problem is a particularly challenging aspect of this planning process. This work proposes a novel methodology, based on biased random-key genetic algorithms (BRKGA), for optimizing the FS of a bus-based public transport system. The proposed approach considers two optimization models that aim to address the following key metrics: (i) passengers’ waiting time, and (ii) the operational cost for the concessionaire company, specifically the distance covered by buses. We apply our BRKGA methodology to a real case study using bus transport data from the city of Maceió (AL, Brazil). Our results demonstrate that, for each metric, the proposed methodology improves the performance of the city’s public transport system by over 10%, compared to the current configuration.

This study aims to optimize vehicular user association to base stations in a mobile network. We propose an efficient heuristic solution that considers the base station average handover frequency, the channel quality indicator, and bandwidth capacity. We evaluate this solution using real-world base station locations from São Paulo, Brazil, and the SUMO mobility simulator. We compare our approach against a state of the art solution which uses route prediction, maintaining or surpassing the provided quality of service with the same number of handover operations. Additionally, the proposed solution reduces the execution time by more than 80% compared to an exact method, while achieving optimal solutions.

The Minimum Broadcast Time (MBT) is a well-known data dissemination problem whose goal is to find a broadcast scheme that minimizes the number of steps needed to execute the broadcast operation. The problem has many applications in distributed systems and, in particular, the Industry 4.0 domain. Because Industry 4.0 applications rely primarily on the use of large-scale machine to machine communications, they need data dissemination techniques that combine high reliability with low communication latency. This work proposes a Biased Random-Key Genetic Algorithm and a matheuristic for the MBT. We carry out experiments with our algorithms on instances commonly used in the literature (hypercube, shuffle exchange, cube-connected cycles, de Bruijn, Harary graphs), and also on massive synthetic instances (up to 1000 vertices), allowing to cover many possibilities of real industry topologies. Our proposal is also compared with state-of-the-art exact methods and heuristics. Experimental results show that our algorithm is able to outperform the best-known heuristics for the MBT, and also that it is a very good alternative for large instances that cannot be solved by current exact methods.

The Data Lake Organization Problem consists of optimized data navigation structures generation to reduce the user’s time exploring all available data. The goal is to find a data organization that maximizes the expected probability of table discovery during user navigation. For this problem, we propose a simulated annealing metaheuristic and compare it with the Organize literature solution on benchmark instances. The instances are Socrata Open Data Lake samples with varying topics and open data from government entities worldwide. To validate our proposal, we performed a statistical analysis using a non-parametric test, which confirmed the dominance of our proposition over the state-of-the-art. Our proposal was more efficient and increased the expected probability of table discovery up to 15%. Thus, our strategy can find better solutions in the benchmarks evaluated even without exhaustively analyzing all of them and more effectively exploring the space of solutions.

This work address a variant of the knapsack problem, known as the knapsack problem with forfeits, which has numerous applications. In this variant, a set of items and a conflict graph are given, and the objective is to identify a collection of items that adhere to the knapsack’s capacity while maximizing the total value of the items minus the penalties for conflicting items. We propose a novel heuristic for this problem based on the concepts of iterated local search, variable neighborhood descent, and tabu search. Our heuristic takes into account four neighborhood structures, and we introduce efficient data structures to explore them. Experimental results demonstrate that our approach outperforms the state-of-the-art algorithms in the literature. In particular, it delivers superior solutions within significantly shorter computation times across all benchmark instances. Additionally, this study includes an analysis of how the proposed data structures have influenced both the quality of the solutions and the execution time of the method.

Avoiding conflicting elements is a natural constraint that appears in several graph problems making them more challenging and close to real applications. Minimum Conflict-Free Spanning Tree (MCFST) is a variant of the classic Minimum Spanning Tree (MST) problem, where we are asked to find (if any) the spanning tree avoiding pairs of conflicting edges (conflict-free) of minimum cost. Although it is well known that MST is polynomial-time solvable, the MCFST problem is N P -hard. In this paper, we present a GRASP with adaptive memory (GRASP-AM) for Minimum Conflict-Free Spanning Tree . Adaptive memory (AM) is used in the constructive phase to decide which set of edges generates good solutions. Furthermore, we show how to implement the local search adopted in the GRASP-AM efficiently. Experimental results on a well-known benchmark indicate that our proposal outperforms the best existing heuristic for the problem. In particular, our GRASP-AM was able to find all known optimal solutions and to improve best-known solutions.

As a result of a fast-growing population, an increasing number of vehicles on the road, and inadequate public policies, metropolitan areas in Latin America are dealing with significant traffic congestion problems. Most cities do not have real-time urban traffic control systems. Therefore, the use of simulation software is a cost-effective solution to evaluate and reduce congestion in metropolitan areas. This paper seeks to assess urban traffic performance using the Urban Mobility Simulator (SUMO) on Fernandes Lima Avenue, the most important thoroughfare in Maceio, Alagoas, Brazil, which features distinctive characteristics such as a dedicated lane for public transportation and three segments with pedestrian traffic lights. Comparing the real observations with the simulation results, it was confirmed that the model provided accurate estimates, with errors of less than 5% for vehicle traffic volume and 10% for total travel time. After conducting experimental studies on four different scenarios, including the current state (1), no blue lane (2), no pedestrian traffic lights (3), and no blue lane and no pedestrian traffic lights (4), it was found that significant improvements in efficiency indicators, such as travel time, waiting time, fuel consumption, and carbon dioxide emissions, could be achieved. Scenarios 2, 3, and 4 were particularly effective, resulting in volumetric increases of 9.95%, 7.88%, and 10.77%, respectively, in vehicle traffic.

A natural constraint in real-world applications is to avoid conflicting elements in the solution of problems. Given an undirected graph G=(V, E) where each edge e∈ E has a positive integer weight ω (e) , and a conflict graph Ĝ=(V̂, Ê) such that V̂⊆ E and each edge ê=(e_1, e_2) ∈Ê represents a conflict between two edges e_1, e_2 ∈ E , in the Minimum Conflict-Free Spanning Tree (MCFST) problem we are asked to find (if any) a spanning tree avoiding pairs of conflicting edges (conflict-free) with minimum cost, i.e., a minimum solution among spanning trees T such that E(T) is an independent set of Ĝ . A spanning tree T of G is a feasible solution for an instance I=(G,Ĝ) of MCFST if E(T) is an independent set of Ĝ . In contrast to the polynomial-time solvability of Minimum Spanning Tree, to determine whether an instance I=(G,Ĝ) of MCFST admits a feasible solution is 𝒩𝒫 -complete. In this paper, we present a multivariate complexity analysis of MCFST by considering particular classes of graphs G and Ĝ . In particular, we show that the problem of determining whether an instance I=(G,Ĝ) of MCFST has a feasible solution is 𝒩𝒫 -complete even if G is a bipartite planar subcubic graph, and Ĝ is a disjoint union of paths of size three ( P_3 ). Moreover, we show that whether G is a complete graph and Ĝ is a disjoint union of stars, then a feasible solution for I=(G,Ĝ) can be found in polynomial time. In addition, we present (in)approximability results for MCFST on complete graphs G, and an FPT algorithm parameterized by the distance to ℱ of the conflict graph Ĝ , where ℱ is a hereditary graph class such that MCFST on conflict graphs Ĝ∈ℱ can be solved in polynomial time.

Para uma cidade ser classificada como "inteligente", ela precisa ter sensores espalhados por ela. A disseminação de dados em uma rede de sensores é um dos desafios que deve ser superado. Em particular, um problema de broadcasting é o WEIGHED MINIMUM BROADCAST TIME (WMBT). O WMBT é um problema de disseminação de dados cujo objetivo é encontrar um esquema de disseminação que minimize o número de passos necessários para executar a operação de disseminação. Será apresentado uma aplicação do WMBT para o processo de atualização de firmware de dispositivos em uma rede Bluetooth. Será apresentado um modelo matemático para o WMBT. Esta proposta comparou com adaptações de heurísticas do estado-da-arte. Os resultados experimentais mostram que o modelo pode ser aplicado para resolver o WMBT.

In this work, we conduct a comparative study of GPU accelerated metaheuristics for data clustering. Three population-based metaheuristics were implemented in GPU: Particle Swarm Optimization (PSO), Differential Evolution (DE), Scatter Search (SS). These metaheuristics were compared with the state-of-the-art methods for data clustering considering both runtime efficiency and solution quality. GPU-PSO and GPU-DE algorithms demonstrated competitive performance in the data sets proposed by the literature, as well as real-world problems. Moreover, experimental results show that our GPU proposal obtained an average speedup of 175x over the CPU-only implementation.

Given a bipartite graph G=(V1,V2,E), a biclique of G is a complete bipartite subgraph of G, and a biclique partitioning of G is a set A⊆E such that the bipartite graph G′=(V1,V2,A) is a vertex-disjoint union of bicliques. The biclique partitioning problem (BPP) consists of, given a complete bipartite graph G=(V1,V2,E) with edge weights we∈R for all e∈E (thus, negative weights are allowed), finding a biclique partitioning A⊆E of minimum weight. The bicluster editing problem (BEP) is a variant of the BPP and consists of editing a minimum number of edges of an input bipartite graph G in order to transform its edge set into a biclique partitioning. Editing an edge consists of either adding it to the graph or deleting it from the graph. In addition to the BPP and the BEP, other problems in the literature aim at finding biclique partitionings, and this motivates the study of the biclique partitioning polytope Pnm of the complete bipartite graph Knm (i.e., Pnm is the convex hull of the incidence vectors of the biclique partitionings of Knm). In this paper we develop such a polyhedral study and show that ladder, bellows, and grid inequalities induce facets of Pnm. Our computational results show that these inequalities are very effective in solving the BEP. In particular, they are able to improve the value of the relaxed solution by up to 20%.