- Petri Nets, Optimization Algorithms, Stochastic Programming, Operational Research, Operational Reaserch and optimization, Planeacion Operativa De Un Departamento De Informatica, and 10 moreConvex Optimization, Artificial Intelligence, Computer Science, Public Health, Metaheuristics (Informatics), Bin Packing Problem, Tabu Search, Lower Bound, Optimal Solution, and Urban Studiesedit
- Professor Titular - Departamento de Informática - UERN / RNedit
International audienc
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
International audienc
Research Interests:
International audienc
Research Interests:
Um Método De Estimação De Sequências Espaço-Temporais De Dados Codificados Usando a Máquina De Suporte Vetorial: Aplicações Na Área De Segurança Da Informação " / "A Method of Estimating Space-Temporary Sequences of Coded Data Using the Vector Support Machine: Applications in the Information Secu...more
We propose in this work a hybrid improvement procedure for the bin packing problem, based on the progressive increase of the number of bins used by a possibly feasible solution. This heuristic has several features: the incorporation of... more
We propose in this work a hybrid improvement procedure for the bin packing problem, based on the progressive increase of the number of bins used by a possibly feasible solution. This heuristic has several features: the incorporation of lower bounding; the
Research Interests:
In this article we consider a bi-objective vehicle routing problem in which, in addition to the classical minimization of the total routing cost, the operator is also required to minimize the maximum diameter of the routes, this is the... more
In this article we consider a bi-objective vehicle routing problem in which, in addition to the classical minimization of the total routing cost, the operator is also required to minimize the maximum diameter of the routes, this is the maximum distance between any two customers serviced within the same route. This problem arises in applications in which a decision planner needs to integrate the routing decisions into his tactical planning so as to reduce the cost of a potential derouting under uncertainty. In addition to the problem description, we provide a formal linear-integer formulation of the problem and an ad-hoc -constraint method capable of handling small-size problems. We also introduce a variable neighborhood search-based algorithm for the solution of larger problems. We provide a critical analysis of the results obtained after executing our algorithms on some classical instances of the capacitated vehicle routing problem. Acknowledgments: Diego Rocha thanks the Programa ...
Research Interests:
The objective in terms of the facility location problem with limited distances is to minimize the sum of distance functions from the facility to its clients, but with a limit on each of these distances, from which the corresponding... more
The objective in terms of the facility location problem with limited distances is to minimize the sum of distance functions from the facility to its clients, but with a limit on each of these distances, from which the corresponding function becomes constant. The problem is applicable in situations where the service provided by the facility is insensitive after given threshold distances. In this paper, we propose a polynomial-time algorithm for the discrete version of the problem with capacity constraints regarding the number of served clients. These constraints are relevant for introducing quality measures in facility location decision processes as well as for justifying the facility creation.
Research Interests:
Research Interests:
Research Interests:
bins without violating thecapacity constraints. The bin packing problem is NP-hard, see [5].We propose an improvement procedure for the bin packing problem, based on progressively increas-ing the number of bins used by a possibly feasible... more
bins without violating thecapacity constraints. The bin packing problem is NP-hard, see [5].We propose an improvement procedure for the bin packing problem, based on progressively increas-ing the number of bins used by a possibly feasible solution. The basic structure of this procedure is thefollowing:
Research Interests:
The bin packing problem consists in nding the minimum number of bins of given capacity which are necessary to pack a certain number of itens. In this work, we propose an improvement procedure for the bin packing problem, based on the... more
The bin packing problem consists in nding the minimum number of bins of given capacity which are necessary to pack a certain number of itens. In this work, we propose an improvement procedure for the bin packing problem, based on the progressive reduction of the number of bins used by a previously constructed solution. Since bin eliminations often lead to unfeasible solutions, a local search feasibility operator based on the diierencing method for number partition is used. Encouraging computational results on benchmark instances are reported.
Research Interests:
Research Interests:
This paper presents a hybrid computational model based on regression techniques, machine learning and physicomathematical algorithms developed for assistance in locating victims in the Brumadinho tragedy in 2019. The physicomathematical... more
This paper presents a hybrid computational model based on regression techniques, machine learning and physicomathematical algorithms developed for assistance in locating victims in the Brumadinho tragedy in 2019. The physicomathematical model, which provided results to help search teams, is based on integral and vector calculus, and fluid mechanics concepts. In addition, from data provided by the physicomathematical algorithm, two hybrid model were developed. One of them uses regression statistical and the other one uses support vector regression which is a type of machine learning. With good prospects of the advances in research, it is expected in future work, a more accurate model that can be used in other possible situations of dam-break. Moreover the model can be applied to situations involving computational fluid dynamics in general
Research Interests:
Abstract The classical set partitioning problem is defined on a graph G = ( V, E ) and consists of partitioning V in disjoint subsets. In this study, we propose a Split procedure to minimize the number of partitions for a given sequence... more
Abstract The classical set partitioning problem is defined on a graph G = ( V, E ) and consists of partitioning V in disjoint subsets. In this study, we propose a Split procedure to minimize the number of partitions for a given sequence of nodes, considering local and global capacity constraints on the partitions. The procedure relies on dynamic programming and it is applied here to the SONET Ring Assignment Problem. It is used as a decoding function in a Biased Random Key Genetic Algorithm and as a constructive heuristic in a hybrid multistart Evolutionary Local Search. High-quality results are computed on classic benchmarks.
Research Interests:
O presente trabalho apresenta um algoritmo evolutivo memetico com vocabulary building para a resolucao do Problema de Roteamento de Unidades Moveis de Pistoneio - PRUMP. Por se tratar de um problema de otimizacao combinatoria NP-Dificil,... more
O presente trabalho apresenta um algoritmo evolutivo memetico com vocabulary building para a resolucao do Problema de Roteamento de Unidades Moveis de Pistoneio - PRUMP. Por se tratar de um problema de otimizacao combinatoria NP-Dificil, uma modelagem matematica para o problema foi utilizada, permitindo obter a solucao exata de um conjunto de instâncias testes, as quais foram usadas nos experimentos computacionais e apresentaram resultados competitivos, comprovando a eficiencia do metodo proposto e validando a estrategia metaheuristica proposta. Palavras-chave: Algoritmo Memetico; Vocabulary Building; Roteamento de Veiculos; Unidades Moveis de Pistoneio; Otimizacao Combinatoria; NP-arduo.
This work presents a reliability study conducted among on-shore oil fields in the Potiguar Basin (RN/CE) belonging to PETROBRAS, Brazil. The main study objective was to verify the existence of a relationship between oil pump lifetime and... more
This work presents a reliability study conducted among on-shore oil fields in the Potiguar Basin (RN/CE) belonging to PETROBRAS, Brazil. The main study objective was to verify the existence of a relationship between oil pump lifetime and some characteristics such as the elevation method, the amount of water produced in the well (BSW), the gas/oil ratio (RGO), the depth of the production pump, the oil field operational unit, among others. The study was based on a retrospective sample of 450 oil columns from all that were functioning between 2000 and 2006. Statistical hypothesis tests under a Weibull regression model fitted to the first failure data allowed the selection of some covariates in the set considered to explain the failure time in the oil pumps.
Research Interests: Production and Produção
Research Interests:
RN) dario@dima p.ufrn.br Resumo: Este artigo apresenta algoritmos e estratégias que estão sendo desenvolvidas para solucionar o Problema do Gerenciamento das Intervenções em poços petrolíferos por Sondas de Produção Terrestre na Bacia... more
RN) dario@dima p.ufrn.br Resumo: Este artigo apresenta algoritmos e estratégias que estão sendo desenvolvidas para solucionar o Problema do Gerenciamento das Intervenções em poços petrolíferos por Sondas de Produção Terrestre na Bacia Petrolífera Potiguar. Estes algoritmos têm por objetivo encontrar o melhor itinerário para a frota de sondas disponíveis visando minimizar o tempo de atendimento das solicitações, maximizando a produção média diária da bacia petrolífera. A decisão de qual sonda encaminhar a uma determinada solicitação de serviço depende de fatores como: produção do poço, distância para a chegada da sonda, do tempo e do custo da intervenção, dentre vários outros fatores. Palavras Chaves: Otimização Combinatória, Metaheurísticas, Sondas de Produção Terrestre. Introdução Este trabalho está inserido no contexto do projeto de Otimização do Gerenciamento Dinâmico de Sondas de Produção Terrestre -OtimSPT. Projeto este que está sendo financiado pelo CTPETRO/FINEP, tendo por ob...
Resumo: O problema Bin Packing clássico unidimensional, objetiva minimizar o número de bins (regiões bem definidas como: caixas, vagões, trilhas de um disco rígido, containeres, etc.) de igual capacidade, necessários para o acomodamento... more
Resumo: O problema Bin Packing clássico unidimensional, objetiva minimizar o número de bins (regiões bem definidas como: caixas, vagões, trilhas de um disco rígido, containeres, etc.) de igual capacidade, necessários para o acomodamento de uma dada lista de objetos. Este artigo apresenta um Algoritmo Memético de Grupamento (AMG), que utiliza heurísticas comuns disponíveis na literatura e conceitos de Problemas de Grupamento. Utiliza-se uma função de avaliação na qual admite que somatório dos itens ultrapasse a capacidade dos bins, o que chamamos de inviabilidade, e operadores meméticos de maior complexidade. Estes operadores utilizam uma busca local que procura melhorar a qualidade da distribuição dos itens nos bins. Testes computacionais realizados utilizando instâncias da OR-Library, permitiram avaliar o desempenho da meta-heurística desenvolvida e sua eficiência. Abstract: The classic one-dimensional Bin Packing problem, aims decrease the number of same capacity bins (well define...
RESUMO Otimização por Nuvem de Partículas (ONP) é uma metaheurística evolucionária que surgiu da intenção de se simular o comportamento de um conjunto de pássaros em vôo com seu movimento localmente aleatório, mas globalmente determinado.... more
RESUMO Otimização por Nuvem de Partículas (ONP) é uma metaheurística evolucionária que surgiu da intenção de se simular o comportamento de um conjunto de pássaros em vôo com seu movimento localmente aleatório, mas globalmente determinado. Esta técnica tem sido muito utilizada na resolução de problemas contínuos não-lineares e pouco explorada em problemas discretos. Este artigo tem como objetivo apresentar o funcionamento desta metaheurística e as adaptações necessárias para aplicação em problemas de otimização discreta. Além disso, são propostas algumas alterações feitas a fim de melhorar os resultados do algoritmo padrão. Os testes foram realizados em instâncias da TSPLIB a fim de demonstrar a eficiência do método. Palavras-chave: Nuvem de partículas, Otimização Global, Otimização Combinatória. ABSTRACT Particle Swarm Optimization (PSO) is an evolutionary metaheuristic that emerged from the intention of simulating the behavior of a group of birds in flight with its locally random m...
This work presents a reliability study conducted among on-shore oil fields in the Potiguar Basin (RN/CE) belonging to PETROBRAS, Brazil. The main study objective was to verify the existence of a relationship between oil pump lifetime and... more
This work presents a reliability study conducted among on-shore oil fields in the Potiguar Basin (RN/CE) belonging to PETROBRAS, Brazil. The main study objective was to verify the existence of a relationship between oil pump lifetime and some characteristics such as the elevation method, the amount of water produced in the well (BSW), the gas/oil ratio (RGO), the depth of the production pump, the oil field operational unit, among others. The study was based on a retrospective sample of 450 oil columns from all that were functioning between 2000 and 2006. Statistical hypothesis tests under a Weibull regression model fitted to the first failure data allowed the selection of some covariates in the set considered to explain the failure time in the oil pumps.
Research Interests: Production and Produção
ABSTRACT We take into account a parallel heterogenous machine scheduling problem arising in maintenance planning of heterogeneous wells. This problem particularly arises in the context of workover rig scheduling. The oil wells need... more
ABSTRACT We take into account a parallel heterogenous machine scheduling problem arising in maintenance planning of heterogeneous wells. This problem particularly arises in the context of workover rig scheduling. The oil wells need regular maintenance to ensure an optimal level of production. After oil production being decreased at some wells, appropriate workover rigs with compatible service capacity, are deployed to serve the wells at discrete locations. Every well needs a certain level of maintenance and rehabilitation services that can only be offered by compatible workover rigs. A new mixed integer linear programming model is propose for this problem that is an arc-time-indexed formulation. We propose a heuristic selection type hyper-heuristic algorithm, which is guided by a learning mechanism resulting in a clever choice of moves in the space of heuristics that are applied to solve the problem. The output is then used to warm start a branch, price and cut algorithm. Our numerical experiments are conducted on instances of a case study of Petrobras, the Brazilian National Petroleum Corporation. The computational experiments prove the efficiency of our hyper-heuristic in searching the right part of the search space using the right alternation among different heuristics and confirms the high quality of solutions obtained by our hyper-heuristic.
Research Interests:
Research Interests:
ABSTRACT The objective in the continuous facility location problem with limited distances is to minimize the sum of distance functions from the facility to the customers, but with a limit on each of the distances, after which the... more
ABSTRACT The objective in the continuous facility location problem with limited distances is to minimize the sum of distance functions from the facility to the customers, but with a limit on each of the distances, after which the corresponding function becomes constant. The problem has applications in situations where the service provided by the facility is insensitive after a given threshold distance. In this paper, we propose a global optimization algorithm for the case in which there are in addition lower and upper bounds on the numbers of customers served.
Research Interests:
ABSTRACT The bi-objective minimum diameter-cost spanning tree problem (bi-MDCST) seeks spanning trees with minimum total cost and minimum diameter. The bi-objective version generalizes the well-known bounded diameter minimum spanning tree... more
ABSTRACT The bi-objective minimum diameter-cost spanning tree problem (bi-MDCST) seeks spanning trees with minimum total cost and minimum diameter. The bi-objective version generalizes the well-known bounded diameter minimum spanning tree problem. The bi-MDCST is a NP-hard problem and models several practical applications in transportation and network design. We propose a bi-objective multiflow formulation for the problem and effective multi-objective metaheuristics: a multi-objective evolutionary algorithm and a fast nondominated sorting genetic algorithm. Some guidelines on how to optimize the problem whenever a priority order can be established between the two objectives are provided. In addition, we present bi-MDCST polynomial cases and theoretical bounds on the search space. Results are reported for four representative test sets.
Research Interests:
Introduction Given a set of n items with weights w i ,i=1,...,n, the bin packing (BP) problem consists of finding the minimum number of bins of capacity C necessary to pack the items, without violating the capacity constraint. This... more
Introduction Given a set of n items with weights w i ,i=1,...,n, the bin packing (BP) problem consists of finding the minimum number of bins of capacity C necessary to pack the items, without violating the capacity constraint. This formulation also entails another problem (DBP), in which we seek to minimize the capacity of a fixed number m of identical bins, so that all items fit in the m bins without violating the capacity constraints. The bin packing problem is NP-hard, see [5]. We propose an improvement procedure for the bin packing problem, based on progressively increasing the number of bins used by a possibly feasible solution. The basic structure of this procedure is the following: . Reductions: apply reduction techniques to eliminate some items and fix some bins. . Bounds: computer lower and upper bounds LB and UB for (BP). If LB = UB, then stop. . Construction: apply a greedy algorithm to build a solution for (DBP) using exactly LB bins. . Redistribution: apply load