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.

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.

A crescente procura por alimentos saudáveis e sem agrotóxico e o ambiente propício para o convívio social atrai pessoas de diferentes perfis às feiras livres. Dessa forma, o objetivo do presente estudo é avaliar os aspectos essenciais referentes à estrutura física, social e econômica da Feira Livre de João Monlevade sob perspectiva de consumidores, não consumidores e feirantes e propor um novo sistema de governança de feira. Para isto, foram utilizados recursos como visitas in loco, observação, verbalizações estruturais, conversas informais e pesquisas bibliográficas. Neste contexto, esta pesquisa possibilitou o conhecimento mais aprofundado sobre a realidade da Feira Livre de João Monlevade e consequentemente, através do apoio dos órgãos responsáveis locais, será possível realizar reparos adequados que possam torná-la uma importante forma de manifestação cultural na cidade e um local de entretenimento para os diferentes públicos presentes na sociedade.

We introduce the Gateway Hub Location Problem (GHLP) to design global air transportation systems. Relying on a three-level hub network structure and on having nodes located in different geographic regions, the GHLP consists of locating international gateways and domestic hubs, activating arcs to induce a connected gateway and hub network, and routing flows within the network at minimum cost. Most previous studies focus on a typical hub-and-spoke network, in which local and global flows are not differentiated. Here to better represent a world wide air transportation system, global flows can only leave or enter a given geographic region by means of a gateway, while local flows can only use hubs within their respective region. As routing local or global flows involved different agents, this study presents a mixed integer programming formulation that exploits these differences to model both the local and global flows. Due to the formulation's characteristics, two algorithm variants based on Benders decomposition method are devised to solve the problem. A new repair procedure produces optimality Benders cuts whenever feasibility Benders cuts would rather be expected. While the monolithic version failed to solve medium size instances, our algorithms solved lager ones in reasonable time.

This work presents a hybrid multi-start algorithm for solving generic binary linear programs. This algorithm, called HMS, is based on a Multi-Start Metaheuristic and combines exact and heuristic strategies to address the problem. The initial solutions are generated by a strategy that applies linear programming and constraint propagation for defining an optimized set of fixed variables. In order to refine them, a local search, guided by a Variable Neighborhood Descent heuristic, is called, which, in turn, uses Local Branching cuts. The algorithm was tested in a set of binary LPs from the MIPLIB 2010 library and the results pointed out its competitive performance, resulting in a promising matheuristic.

This work addresses the multi-level capacitated minimum spanning tree (MLCMST) problem. It consists of finding a minimal cost spanning tree such that the flow to be transferred from a central node (root) to the other nodes is bounded by the edge capacities. In this paper, a hybrid algorithm, combining the Variable Neighborhood Search (VNS) metaheuristic and one mathematical programming formulation of the literature, is used for solving it. The formulation is used to give an initial solution to VNS. Five neighborhoods are used for exploring the solution space. Results show that the VNS is able to improve the initial solutions and to obtain small gap solutions for all instance sets.

In this paper we propose some mathematical models for the transmission of dengue using a system of reaction–diffusion equations. The mosquitoes are divided into infected, uninfected and aquatic subpopulations, while the humans, which are divided into susceptible, infected and recovered, are considered homogeneously distributed in space with a constant total population. We find Lie point symmetries of the models and we study theirs temporal dynamics, which provides us the regions of stability and instability, depending on the values of the basic offspring and the basic reproduction numbers. Also, we calculate the possible values of the wave speed for the mosquitoes invasion and dengue spread and compare them with those found in the literature.

This work presents a modified version of the SCO (Supervisory Control and Optimization) methodology, proposed in Pena et al. (Inf Sci 329:491–502, 2016) to deal with planning problems in flexible manufacturing systems. Although having proved to be an alternative to deal with this class of problems, the SCO methodology is limited by the fact that it can only be applied to deal with small batches of products. Previous works show that when considering manufacturing systems of a moderate degree of complexity, this approach is only efficient to generate solutions for batches containing very few products, as for larger batches, the necessary computational time to process a solution is very high. It is obvious that, for the problems in the real world, this dimension of production is very small, which, at first, makes the application of SCO methodology quite limited. Therefore, this work proposes a complementary approach to SCO, here called SCO-Concat, developed to carry out the planning in larger batches of production. The proposed methodology was tested in a plant of moderate size, and the results obtained show that planning for batches as large as desired can be achieved in an efficient manner by SCO-Concat at a very reduced computational cost.

Multiechelon supply chains are complex logistics systems that require flexibility and coordination at a tactical level to cope with environmental uncertainties in an efficient and effective manner. To cope with these challenges, mathematical programming models are developed to evaluate supply chain flexibility. However, under uncertainty, supply chain models become complex and the scope of flexibility analysis is generally reduced. This paper presents a unified approach that can evaluate the flexibility of a four-echelon supply chain via a robust stochastic programming model. The model simultaneously considers the plans of multiple business divisions such as marketing, logistics, manufacturing, and procurement, whose goals are often conflicting. A numerical example with deterministic parameters is presented to introduce the analysis, and then, the model stochastic parameters are considered to evaluate flexibility. The results of the analysis on supply, manufacturing, and distribution flexibility are presented. Tradeoff analysis of demand variability and service levels is also carried out. The proposed approach facilitates the adoption of different management styles, thus improving supply chain resilience. The model can be extended to contexts pertaining to supply chain disruptions; for example, the model can be used to explore operation strategies when subtle events disrupt supply, manufacturing, or distribution.

A fuzzy inference framework based on fuzzy relations is developed, adapted and applied to temperature and humidity measurements from a specific coffee crop site in Brazil. This framework consists of fuzzy relations over possibility distributions, resulting in a model analogous to a Bayesian inference process. The application of this fuzzy model to a data set of experimental measurements and its correspondent forecasts of temperature and humidity resulted in a set of revised forecasts, that incorporate information from a historical record of the problem. Each set of revised forecasts was compared with the correspondent set of experimental data using two different statistical measures, MAPE (Mean Absolute Percentage Error) and Willmott's D. This comparison showed that the sets of forecasts revised by the fuzzy model exhibited better results than the original forecasts on both statistical measures for more than two thirds of the evaluated cases.

The incomplete hub location problem with and without hop-constraints is modeled using a Leontief substitution system approach. The Leontief formalism provides a set of important theoretical properties and delivers formulations with tight linear bounds that can explicitly incorporate hop constraints for each origin-destination pair of demands. Furthermore, the proposed formulations are amenable to a Benders decomposition technique which can solve large scale test instances. The performance of the devised algorithm is primarily due to a new general scheme for separating Benders feasibility cuts. The novel cuts render a stabilizing effect that is directly responsible for the solution of instances up to 80 nodes.

Educational timetabling problems require the assignment of times and resources to events, while sets of required and desirable constraints must be considered. The XHSTT format was adopted in this work because it models the main features of educational timetabling and it is the most used format in recent studies in the field. This work presents new cuts and reformulations for the existing integer programming model for XHSTT. The proposed cuts improved hugely the linear relaxation of the formulation, leading to an average gap reduction of 32%. Applied to XHSTT-2014 instance set, the alternative formulation provided four new best known lower bounds and, used in a matheuristic framework, improved eleven best known solutions. The computational experiments also show that the resulting integer programming models from the proposed formulation are more effectively solved for most of the instances.

Design of Hub-and-spoke networks is an extension of classical facility location problem and it is very important due to its applications in cargo, passenger and telecommunication systems. The problem consists in determining the number and location of the hubs, besides define the allocation of non-hub nodes to the installed hubs, aiming to minimize the total costs. This problem is known to be NP-hard and it has been tackled by heuristic based approaches. In this paper it is proposed an efficient multi-start heuristic composed by a simple construction phase, a perturbation mechanism and an adaptive local search. Computational experiments using standard benchmark problems shows that the proposed approach is competitive when compared with the best heuristics in the literature.