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Objective The aim of this study was to identify the most relevant variables defining the dietary, social, and health patterns of elderly populations with disabilities, considering their geographic profile. Methods A cross-sectional study was carried out in a sample of 354 disabled, free-living elderly adults from three different geographic profiles (metropolitan, rural, and mixed profile). The dietary data were obtained through a validated food habit questionnaire. The data regarding health status, cohabitation unit, and social benefits were obtained through the public social services. A standardized principal component analysis was used to select the most relevant variables, by considering their contributions to each principal component and their relation with the geographic factor. Results From 131 variables, we highlighted 27 (57.37% of variability explained). The variables with more contribution are, in order, the calorie intake (especially from lipids), absence of home assistance, and the difference between intake and recommended calories. The procedure was validated by assessing the prediction using a multinomial logistic regression model (88.2% and 66.7% of success rate regarding the metropolitan and rural profiles, respectively). There is a differentiated behavior based on the geographic origin of individuals, specifically regarding caloric intake, number of diseases, and the requirement for home assistance. Conclusions Older adults living in a metropolitan are tend to have a greater number of diseases as well as a lower caloric intake. The increased rural caloric consumption comes from lipids. Better health status in rural areas is associated with a lower need for home assistance.

In this paper, we study the visualization of complex structures in the context of automatic graph drawing. Constructing geometric representations of combinatorial structures, such as networks or graphs, is a difficult task that requires an expert system. The automatic generation of drawings of graphs finds many applications from software engineering to social media. The objective of graph drawing expert systems is to generate layouts that are easy to read and understand. This main objective is achieved by solving several optimization problems. In this paper we focus on the most important one: reducing the number of arc crossings in the graph. This hard optimization problem has been studied extensively in the last decade, proposing many exact and heuristic methods to minimize the total number of arc crossings. However, despite its practical significance, the min–max variant in which the maximum number of crossings over all edges is minimized, has received very little attention. We propose new heuristic methods based on the strategic oscillation methodology to solve this NP-hard optimization problem. Our experimentation shows that the new method compares favorably with the existing ones, implemented in current graph drawing expert systems. Therefore, a direct application of our findings will improve these functionality (i.e., crossing reduction) of drawing systems.

In this paper, a districting problem with stochastic demands is investigated. The goal is to divide a geographic area into p contiguous districts such that, with some given probability, the districts are balanced with respect to some given lower and upper thresholds. The problem is cast as a p -median problem with contiguity constraints that is further enhanced with chance-constrained balancing requirements. The total assignment cost of the territorial units to the representatives of the corresponding districts is used as a surrogate compactness measure to be optimized. Due to the tantalizing purpose of deriving a deterministic equivalent for the problem, a two-phase heuristic is developed. In the first phase, the chance-constraints are ignored and a feasible solution is constructed for the relaxed problem; in the second phase, the solution is corrected if it does not meet the chance-constraints. In this case, a simulation procedure is proposed for estimating the probability of a given solution to yield a balanced districting. That procedure also provides information for guiding the changes to make in the solution. The results of a series of computational tests performed are discussed based upon a set of testbed instances randomly generated. Different families of probability distributions for the demands are also investigated, namely: Uniform, Log-normal, Exponential, and Poisson.

In this paper we study the hub location problem, where the goal is to identify an optimal subset of facilities (hubs) to minimize the transportation cost while satisfying certain capacity constraints. In particular, we target the single assignment version, in which each node in the transportation network is assigned to only one hub to route its traffic. We consider here a realistic variant introduced previously, in which the capacity of edges between hubs is increased in a modular way. This reflects the practical situation in air traffic where the number of flights between two locations implies a capacity in terms of number of passengers. Then, the capacity can be increased in a modular way, as a factor of the number of flights. We propose heuristic methods to obtain high-quality solutions in short computing times. Specifically, we implement memory structures to create advanced search methods and compare them with previous heuristics on a set of benchmark instances. Memory structures have been widely implemented in the context of the tabu search methodology, usually embedded in local search algorithms. In this paper we explore an alternative design in which the constructive method is enhanced with frequency information and the local search is coupled with a path relinking post-processing. Statistical tests confirm the superiority of our proposal with respect to previous developments.

This paper explores the concept of capacity transfer in the context of capacitated facility location problems. This is accomplished by assuming that facilities with surplus capacity/production can cooperate with those facing shortage by transferring part of that capacity/production. Such a transfer incurs a cost that nonetheless may be compensated by savings both in the installation costs and in the distribution costs. Mixed-integer mathematical programming models are proposed for the problem. A distinction is made between the case in which the triangle inequality holds for the transfer costs and the case in which it does not. We present compact models, which are enhanced with valid inequalities that are separated in a branch-and-cut fashion. A comprehensive computational study with several hundreds of instances is reported showing the value of transferring capacities. Overall, this work investigates a problem that is at the core of more comprehensive models emerging in the context of logistics network design.

Scatter search is a population-based method that has been shown to yield high-quality outcomes for combinatorial optimization problems. It uses strategies for combining solution vectors that have proved effective in a variety of problem settings. In this paper, we present a scatter search implementation for an NP -hard variant of the classic p-hub median problem. Specifically, we tackle the uncapacitated r-allocation p-hub median problem, which consists of minimizing the cost of transporting the traffics between nodes of a network through special facilities that act as transshipment points. This problem has a significant number of applications in practice, such as the design of transportation and telecommunications networks.As it is customary in metaheuristic implementations, we design specific methods to create an efficient solving procedure based on the scatter search methodology. In particular, we propose mechanisms to generate, combine, and improve solutions for this problem. Special mention deserves the use of path-relinking as an extension of the classical combination method. Extensive computational experiments establish the effectiveness of our procedure in relation to approaches previously identified to be best.

In this paper we propose a heuristic for the Uncapacitated r-Allocation p-Hub Median Problem. In the classical p-hub location problem, given a set of nodes with pairwise traffic demands, we must select p of them as hub locations and route all traffics through them at a minimum cost. We target here an extension, called the r-allocation p-hub median problem, recently proposed by Yaman [19], in which every node is assigned to r of the p selected hubs (r@?p) and we are restricted to route the traffic of the nodes through their associated r hubs. As it is usual in this type of problems, our method has three phases: location, assignment and routing. Specifically, we propose a heuristic based on the GRASP methodology in which we consider three local search procedures. The combinatorial nature of this problem makes them time-consuming. We therefore propose a filtering mechanism to discard low-quality constructions and skip its improvement, saving its associated running time. We perform several experiments to first determine the values of the key-search parameters of our method and then to compare with previous algorithms. Computational results on 465 instances show that while only small instances can be optimally solved with exact methods, the heuristic is able to find high-quality solutions on larger instances in short computing times. Moreover, when targeting the classical p-hub versions (with r=1 or r=p), our heuristic is competitive with the state of the art methods.