This paper addresses the Uncapacitated Single Allocation p-hub Maximal Covering Problem (USApHMCP), which aims to determine the best allocation for the p-hubs within a node network in order to maximize the network coverage. We proposed a search strategy-based heuristic Basic Variable Neighborhood Search (VNS) to solve the problem. Two different sets of test instances from the literature, Civil Aeronautics Board (CAB) and Australian Post (AP), were used to evaluate the performance of VNS and to compare it with the Tabu Search (TS) metaheuristic. In most instances, the bounds obtained by VNS and TS were the same but, on the other hand, for some of them, VNS presented a slight advantage and vice versa. That is, both algorithms are convenient to solve the proposed problem.

This paper studies two-level uncapacitated facility location problems, a class of discrete location problems that consider different hierarchies of facilities and their interactions. Benders reformulations for both single and multiple assignment variants and while several separation procedures for three classes of Benders cuts are presented: standard optimality cuts, lifted optimality cuts, and non-dominated optimality cuts. Extensive computational experiments are performed on difficult and large-scale benchmark instances to assess the performance of the considered separation routines.

Surgical scheduling consists of selecting surgeries to be performed within a day, while jointly assigning operating rooms, starting times and the required resources. Patients can be elective or emergency/urgent. The scheduling of surgeries in an operating theatre with common resources to emergency or urgent and elective cases is highly subject to uncertainties not only on the duration of an intervention but mainly on the arrival of emergency or urgent cases. At the beginning of the day we are given a candidate set of elective surgeries with and an expected duration and a time window the surgery must start, but the expected duration and the time window of an emergency or urgent case become known when the surgery arrives. The day is divided into decision stages. Due to the dynamic nature of the problem, at the beginning of each stage the planner can make decisions taking into account the new information available. Decisions can be to schedule arriving surgeries, and to reschedule or cancel surgeries not started yet. The objective is to minimize the total expected cost composed of terms related to refusing arriving surgeries, to canceling scheduled surgeries, and to starting surgeries out of their time window. We address the problem with an approximate dynamic programming approach embedding an integer programming formulation to support decision making. We propose a dynamic model and an approximate policy iteration algorithm making use of basis functions to capture the impact of decisions to the future stages. Computational experiments have shown with statistical significance that the proposed algorithm outperforms a lookahead reoptimization approach.

Green machine scheduling consists in the allocation of jobs in order to maximize production, in view of the sustainable use of energy. This work addresses the unrelated parallel machine scheduling problem with setup times, with the minimization of the makespan and the total energy consumption. The latter takes into account the power consumption of each machine in different operation modes. We propose multi-objective extensions of the Adaptive Large Neighborhood Search (ALNS) metaheuristic with Learning Automata (LA) to improve the search process and to solve the large scale instances efficiently. ALNS combines ad-hoc destroy and repair (also named removal and insertion) operators and a local search procedure. The LA is used to adapt the selection of insertion and removal operators within the framework of ALNS. Two new algorithms are developed: the MO-ALNS and the MO-ALNS/D. The first algorithm is a direct extension of single objective ALNS by using multi-objective local search. As this method does not offer much control of the diversification of the Pareto front approximation, a second strategy employs the decomposition. approach similar to MOEA/D algorithm. The results show that the MO-ALNS/D algorithm has better performance than MO-ALNS and MOEA/D in all indicators. These findings show that the decomposition strategy is beneficial not only for evolutionary algorithms, but it is indeed an efficient way to extend ALNS to multi-objective problems.

A natureza delicada das atividades hospitalares e a necessidade de manter altos níveis de serviço exigem uma eficiente gestão dos estoques de medicamentos, uma vez que eles possuem demanda derivada do tratamento de pacientes. Neste contexto, o presente trabalho apresenta uma metodologia para encontrar limites inferiores de um modelo matemático de dimensionamento de lote com seleção de fornecedores em uma farmácia hospitalar. É proposta a implementação da decomposição de Dantzig-Wolfe com um método de geração de colunas para o modelo. Analisando os resultados obtidos pela metodologia empregada, verifica-se a obtenção de limites inferiores melhores do que a relaxação linear.

The majority of urban centers are experiencing structural problems due to the contrast between the intensive vehicle flow and the lack of appropriated infrastructure to handle such a traffic demand. Reorienting the streets is a possible strategy to try to address these problems. The Strong Network Orientation Problem (SNOP) stands as an alternative to handle this situation in such a way the total travel distance in a network configuration is minimized. Since the problem involves large scale systems and is considered by the literature as NP-Hard, efficient heuristics algorithms are suitable to achieve appropriate solutions. This paper presents a General Variable Neighborhood Search (GVNS) algorithm for the SNOP composed by a systematic use of four neighborhoods structures and its appliance on instances found in the literature and real cases of a city. Its building steps including local search procedures provided by a Randomized Variable Neighborhood Search (RVND) algorithm and results are also presented and discussed. Results indicate that GVNS is an efficient metaheuristic to solve SNOP, both in running time and quality solution when compared with other heuristics approaches and the exact strategy from the optimization software CPLEX for this problem.

When it comes to steel processes, it is known that refractory materials are responsible for a significant portion of the steel production costs. For this reason, this work aimed to understand the high variability and the low durability of the refractory campaign that compose a process of continuous casting in a large LD mill in the state of Minas Gerais , identifying the relationship between the process variables so it was possible to make estimates about the duration of its refractory campaign. For the selection of the explanatory factors, a variation of the method Stepwise was used. In each step of the algorithm, a model based on linear programming was responsible for the calculations of the linear regression coefficients. In the end, a prediction model was obtained for the duration of the campaign containing 12 explanatory factors and 97.66% of statistical significance.

Consumers of goods and services in a particular market are attracted by a set of value attributes that directly or indirectly qualify them in relation to their competitors. On the other hand, although the “price” is only one among the various attributes, it is often interpreted as a response variable that gathers the others, precisely because of their relevance level to the consumers budget constraint. This paper proposes to evaluate the level of influence and correlation of a set of explanatory variables in the prices of residential properties offered for rent and sale in the city of João Monlevade-MG, using multivariate linear regression models. The methodology is based on real information regarding the prices offered in the city and its structural and locational characteristics, to quote: number of rooms and parking spaces; number of police occurrences; proximity to the center, health centers and schools more nearby. As a result, it was possible to obtain a set of mathematical equations able to explain the price according to the predictor variables, as well as to understand the relation between these variables.

The management problem of the New Product Development Project Process (PDNP) is recurrent in the literature, as it reflects a question that exists in R&D companies, which is to decide which product project portfolio which will minimize the necessary development costs while maximizing the return for the organization. In this context, the present study aims to use two multi-criteria approaches - TOPSIS and PROMETHEE II - using the Analytic Hierarchy Process (AHP) method to establish, in a non-partial way, the weights and to determine which approach yields the best profit for NPDP, and raise the question of which approach is most appropriate for this problem. In addition, a practical example was proposed that shows the impact between the different orderings present in the work, to assist in achieving the goal. As a result, it was possible to obtain a study in which the non-compensatory approach is better for the practical example, making the present work the beginning of deeper studies on the subject.

Even though the spatial competition is a crucial aspect of the competitive strategy of firms, it is still little approached in the competitive and marketing strategy literature. As such, the association between formal spatial models and the practice of spatial competition between firms should be approached. Addressing this gap in this article, we propose a more formal view of the spatial competition. Based on the concepts of the value of displacement for the customer and the value of location we present several aspects related to the difference in the location of outlets in a hypothetical city. Several measures such as market share, competitive advantage, and value of location, are calculated in this model. This article opens a new perspective to study the competitive positioning of firms in spatial markets.

O setor automotivo tem relevante participação na estrutura industrial nacional e dado aos seus encadeamentos pode influenciar na produção de vários outros setores. Diante disso e da competitividade acirrada entre os concorrentes, é de extrema necessidade que este setor busque por táticas para garantir sua presença no mercado e sobrevivência financeira. Assim sendo, o trabalho identificou o perfil estratégico de cada montadora por meio de inferência estatística, tendo por objetivo construir cenários de competição estratégica via Teoria dos Jogos para um mercado constituído de seis montadoras, que representam 85% do mercado de automóveis. O jogo foi modelado na categoria sedan segmentando modelos a uma faixa de preço de 75 a 100 mil reais com motores de 1.5 à 2.4, a fim de trabalhar com modelos que compitam de forma mais direta entre si.

This paper addresses the Uncapacitated Single Allocation p-hub Maximal Covering Problem (USApHMCP), which aims to determine the best allocation for the p-hubs within a node network in order to maximize the network coverage. We proposed a search strategy-based heuristic VNS (Variable Neighborhood Search [7]) to solve the problem. Two different sets of literature test instances, Civil Aeronautics Board (CAB) and Australian Post (AP), were used to evaluate the performance of VNS and compare it to the Tabu Search metaheuristic (TS) proposed by [11]. As a result, the VNS presented equal or slightly smaller bounds for large-scale AP instances, as well as a similar behavior was shown regarding the quality of the solutions for CAB instances. In addition, it was observed that VNS performs better than the TS algorithm in CPU running times for both CAB and AP instances.

O mercado brasileiro de caminhões é um mercado dinâmico e oligopolizado e, portanto, o desenvolvimento de ações estratégicas se faz necessário para que a empresa se estabeleça com melhor posição no mercado. Consequentemente, torna-se significativo entender melhor estrutura do mercado a partir das necessidades dos consumidores. Nesse contexto, este estudo objetiva retratar a competição existente no mercado de caminhões por meio da composição de um modelo baseado em Teoriados Jogos, além de analisar o comportamento estratégico de duas montadoras decaminhões de acordo com as categorias de produto. Como resultado, obteve-se ummodelo de jogo mais completo do que outros artigos presentes na literatura e que se demonstrou consistente frente às suposições estabelecidas.