Ricardo Poley Martins Ferreira

Multiple allocation hub-and-spoke network design under hub congestion

The multiple allocation hub-and-spoke network design under hub congestion problem is addressed in this paper. A non-linear mixed integer programming formulation is proposed, modeling the congestion as a convex cost function. A generalized Benders decomposition algorithm has been deployed and has successfully solved standard data set instances up to 81 nodes. The proposed algorithm has also outperformed a commercial leading edge non-linear integer programming package. The main contribution of this work is to establish a compromise between the transportation cost savings induced by the economies of scale exploitation and the costs associated with the congestion effects.

Review: Multi-agent modeling and simulation of an Aedes aegypti mosquito population

The present work deals with the simulation of a mosquito Aedes aegypti population. The mosquito population was modeled using an individual-based approach. The model consists of agents representing A. aegypti mosquitoes, human beings, some mammals and objects found in urban environments such as walls and water containers. We describe the model which was implemented by multi-agent systems in the Repast framework. Simulations were performed and the results were compared with those obtained in a biological experiment, and data obtained from a local Zoonozes control center. Comparisons between real and simulated data showed high correlation indices. We simulated cases to study whether or not an artificial trap can be effectively used as an active based population control measure. We studied how the number of traps and their localization can affect the population dynamics.

A performance guarantee heuristic for electronic components placement problems including thermal effects

In this work, Benders decomposition algorithm is used to deal with a computer motherboard design problem. Amongst all the possible formulations for the component placement problem, the chosen one creates an instance of the extensively studied Quadratic Assignment Problem (QAP). This problem arises as a great challenge for engineers and computer scientists. The QAP inherent combinatorial structure makes the most efficient optimization algorithms to exhibit low performance for real size instances. It is also considered here the important addition of linear costs. This approach is directly responsible for the performance gain presented by our decomposition method. Coupled with the placement problem, it is under investigation the maximum temperature rising on the board surface. In order to solve the Energy Conduction Equation the Finite Volume Method is implemented, becoming possible to derive a secondary quality solution criterion. A set of test instances is then solved and the corresponding results are reported.

A hybrid Outer-Approximation/Benders Decomposition algorithm for the single allocation hub location problem under congestion

Abstract An efficient procedure that concurrently generates Outer-Approximation and Benders cuts is devised to tackle the single allocation hub location problem under congestion, an MINLP. The proposed method is able to optimally solve large instances (up to 200 nodes) in reasonable time. The combination of both cuts is an algorithmic novelty.

Discrete capacity and flow assignment algorithms with performance guarantee

The joint problem of selecting routing and a capacity value for each link in networks is considered. We apply an alternative approach for some models that have been addressed to (store-and-forward) packet-switched computer network discrete capacity allocation and routing problems. The network topology and traffic characteristics are supposed to be given. The goal is to obtain a feasible solution with minimum total cost, where the total cost include both leasing capacity and congestion costs. Heuristic algorithms with performance guarantee based on lower bounds and on the separability of the objective function are proposed. Experiments were conducted to verify the performance and to confirm the efficiency of the proposed algorithms.

Hub location under hub congestion and demand uncertainty: the Brazilian case study

In this work, a mixed integer nonlinear programming model combining direct service links, demand uncertainty and congestion effects is proposed. This model is efficiently solved by Generalized Benders Decomposition, for instances of moderate sizes and reasonable number of scenarios. The deployed algorithms are further used for re-designing the Brazilian air transportation network, enabling the analysis of future demand scenarios and providing decision support about the optimal investment policy for Brazil.

Global optimization of capacity expansion and flow assignment in multicommodity networks

This paper describes an exact algorithm to solve a nonlinear mixed-integer programming model due to capacity expansion and flow assignment in multicommodity networks. The model combines continuous multicommodity flow variables associated with nonlinear congestion costs and discrete decision variables associated with the arc expansion costs. After establishing precise correspondences between a mixed-integer model and a continuous but nonconvex model, an implicit enumeration approach is derived based on the convexification of the continuous objective function. Numerical experiments on medium size instances considering one level of expansion are presented. The results reported on the performance of the proposed algorithm show that the approach is efficient, as commercial solvers were not able to tackle the instances considered.

A swarm intelligent approach for multi-objective optimization of compact heat exchangers

Design optimization of heat exchangers is a very complicated task that has been traditionally carried out based on a trial-and-error procedure. To overcome the difficulties of the conventional design approaches especially when a large number of variables, constraints and objectives are involved, a new method based on a well-established evolutionary algorithm, particle swarm optimization, weighted sum approach and a novel constraint handling strategy is presented in this study. Since the conventional constraint handling strategies are not effective and easy-to-implement in multi-objective algorithms, a novel feasibility-based ranking strategy is introduced which is both extremely user-friendly and effective. A case study from industry has been investigated to illustrate the performance of the presented approach. The results show that the proposed algorithm can find the near pareto-optimal with higher accuracy when it is compared to conventional non-dominated sorting genetic algorithm II. Moreover, the difficulties of a trial-and-error process for setting the penalty parameters are solved in this algorithm.

Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments

Long length of stay and overcrowding in emergency departments (EDs) are two common problems in the healthcare industry. To decrease the average length of stay (ALOS) and tackle overcrowding, numerous resources, including the number of doctors, nurses and receptionists need to be adjusted, while a number of constraints are to be considered at the same time. In this study, an efficient method based on agent-based simulation, machine learning and the genetic algorithm (GA) is presented to determine optimum resource allocation in emergency departments. GA can effectively explore the entire domain of all 19 variables and identify the optimum resource allocation through evolution and mimicking the survival of the fittest concept. A chaotic mutation operator is used in this study to boost GA performance. A model of the system needs to be run several thousand times through the GA evolution process to evaluate each solution, hence the process is computationally expensive. To overcome this drawback, a robust metamodel is initially constructed based on an agent-based system simulation. The simulation exhibits ED performance with various resource allocations and trains the metamodel. The metamodel is created with an ensemble of the adaptive neuro-fuzzy inference system (ANFIS), feedforward neural network (FFNN) and recurrent neural network (RNN) using the adaptive boosting (AdaBoost) ensemble algorithm. The proposed GA-based optimization approach is tested in a public ED, and it is shown to decrease the ALOS in this ED case study by 14%. Additionally, the proposed metamodel shows a 26.6% improvement compared to the average results of ANFIS, FFNN and RNN in terms of mean absolute percentage error (MAPE).

An exact penalty function based on the projection matrix

This paper proposes an exact penalty function based on the projection matrix concept. The proposed penalty function finds solutions which satisfy the necessary optimality conditions of the original problem. Some theoretical results are presented showing that every regular point provides an absolute minimum to the proposed penalty function if and only if it satisfies the necessary conditions of the original constrained problem. As a general rule, penalty functions may have spurious local minima. An advantage of the proposed penalty function is its ability to identify if an obtained minimum is spurious. The proposed penalty function was applied to solve equality constrained problems from the Hock-Schittkowski Collection. Some solutions were obtained more efficiently using the new penalty function than by using a conventional constrained optimization method.