Alexei Barbosa de Aguiar

On the concept of density control and its application to a hybrid optimization framework: Investigation into cutting problems

The Generate-and-Solve (GS) methodology is a hybrid approach that combines a metaheuristic component with an exact solver. GS has been recently introduced in the literature in order to solve cutting and packing problems, showing promising results. The GS framework includes a metaheuristic engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver. In this paper, we present an extended version of GS, focusing primarily on the concept of a new Density Control Operator (DCO). The role of this operator is to adaptively control the dimension of the reduced instances in such a way as to allow a much steadier progress towards a better solution, thereby avoiding premature convergence. In order to assess the potentials of this novel version of the GS methodology, we conducted computational experiments on a set of difficult benchmark instances of the constrained non-guillotine cutting problem. The results achieved are quantitatively and qualitatively discussed in terms of effectiveness and efficiency, showing that the proposed variant of the GS hybridization framework is highly suitable when effectiveness is a major requirement.

A Model for GSM Mobile Network Design

This work shows a mathematical and computational tool to design a GSM (Global System for Mobile Communications) network, in the point of view of BSC (Base Station Controllers) allocation and dimensioning. It optimizes the total transmission cost and BSC acquisition cost. It determines how much BSC are need, in what sites they has to be allocated, what model each one must have to support the total traffic demand without wasting money with their acquisition and what BTS (Base Transceiver Station) must be linked to what BSC for transmission cost reduction. Its core is a integer programming (IP) model as presented in Wolsey et al [8]. The approach of data generation to the model from the real world is explained too. In this model, the BSC nodes are allocated taking account both factors: Transmission and BSC acquisition costs. The transmission cost involves distance and capacity of the E1 lines. The links between BTS and BSC are allocated, and the ones between BSC and MSC are dimensioned in number of E1 lines. The choice of the BSC model that has the best capacity to the total traffic demand gives flexibility for the mobile network design comparing with fixed capacity models. It is important since in real cases, the BSC suppliers gives configuration options from low capacity and price, until high capacity with good relative cost. This model uses the traffic demand in Erlangs instead of number of voice channels. This approach allows the links between BSC and MSC (Mobile Switching Center) dimensioning using the statistic gain of telephony switches. Otherwise, simple deterministic sum of voice channels would be very simplistic, but would oversize the links too. Other important issue in this model is the fact that it addresses the new resources allocation technique of BSC switches that rises its capacity. The traditional way of resources allocation (processors, for instance) to the radio channels was deterministic and fixed. Thus, its capacity was given by total number of voice channels (4096, for instance). Nowadays, the BSC can handle a pool of resources that are allocated on-demand. The capacity rises and is given by its total traffic in Erlang.

Towards Aid by Generate and Solve Methodology: Application in the Problem of Coverage and Connectivity in Wireless Sensor Networks

The integrative collaboration of genetic algorithms and integer linear programming as specified by the Generate and Solve methodology tries to merge their strong points and has offered significant results when applied to wireless sensor networks domains. The Generate and Solve (GS) methodology is a hybrid approach that combines a metaheuristics component with an exact solver. GS has been recently introduced into the literature in order to solve the problem of dynamic coverage and connectivity in wireless sensor networks, showing promising results. The GS framework includes a metaheuristics engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver.

Handing Optimization Energy Consumption in Heterogeneous Wireless Sensor Networks

This paper presents an integer linear programming model devoted to optimize the energy consumption efficiency in heterogeneous wireless sensor networks. This model is based upon a schedule of sensor allocation plans in multiple time intervals subject to coverage and connectivity constraints. By turning off specifics sets of redundant sensors in each time interval, it is possible to reduce the total energy consumption in the network and, at the same time, avoid partitioning the whole network by losing some strategic sensors too prematurely. Since the network is heterogeneous, sensors can sense different phenomena from different demand points, with different sample rates. By resorting to this model, it is possible to provide extra lifetime to heterogeneous wireless sensor networks, reducing their setup and maintenance costs. This is an important issue to be considered when deploying sensor devices in hostile and inaccessible environments.

A Multi-layer GSM Network Design Model

GSM Network Designs usually offers big challenges for achieving an efficient cost while respecting the complex combinatorial technical constraints. This networks have hundred or thousands BTS. They have their traffic grouped in hubs, then in BSC nodes to reach the MSC. Hubs must be elected within the BTS set and BSC nodes have to be geographically allocated in the available sites. Also, the number and model of these BSC impact in the overall cost while the distances affect the transmission costs. This paper presents a mathematical model for designing a GSM network from the BTS lower layer until the MSC layer.

Scalability Analysis of a Model for GSM Mobile Network Design

This work shows a scalability analysis of the mathematical model and computational tool to design a GSM (Global System for Mobile Communications) Network, in the point of view of BSC (Base Station Controllers) allocation and dimensioning. It optimizes the total transmission cost and BSC acquisition cost. It determines how much BSC are need, in what sites they has to be allocated, what model each one must have to support the total traffic demand without wasting money with their acquisition and what BTS (Base Transceiver Station) must be linked to what BSC for transmission cost reduction. Its core is a integer programming (IP) model as presented in Wolsey et al [8]. Other important contribution in this model is the fact that it addresses the new resources allocation technique of BSC switches that rises its capacity. The traditional way of resources allocation (processors, for instance) to the radio channels was deterministic and fixed. Thus, its capacity was given by total number of voice channels (4096, for instance). Nowadays, the BSC can handle a pool of resources that are allocated on-demand. The capacity rises and is given by its total traffic in Erlang.