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Dive into the research topics where Ricardo Saraiva de Camargo is active.

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Featured researches published by Ricardo Saraiva de Camargo.


Transportation Science | 2009

Benders Decomposition for Hub Location Problems with Economies of Scale

Ricardo Saraiva de Camargo; Gilberto de Miranda; Henrique Pacca Loureiro Luna

When considering hub-and-spoke networks with multiple allocation, the classical models of the literature compute solutions with large discount factors for small flows on interhub connections. Addressing the economies of scale issue, a tighter formulation for this problem is presented, bringing forward a special structure. A specialized version of Benders decomposition is then developed to solve large instances in reasonable time.


European Journal of Operational Research | 2013

An improved Benders decomposition algorithm for the tree of hubs location problem

Elisangela Martins de Sá; Ricardo Saraiva de Camargo; Gilberto de Miranda

The tree of hubs location problem is a particularly hard variant of the so called hub location problems. When solving this problem by a Benders decomposition approach, it is necessary to deal with both optimality and feasibility cuts. While modern implementations of the Benders decomposition method rely on Pareto-optimal optimality cuts or on rendering feasibility cuts based on minimal infeasible subsystems, a new cut selection scheme is devised here under the guiding principle of extracting useful information even when facing infeasible subproblems. The proposed algorithm outperforms two other modern variants of the method and it is capable of optimally solving instances five times larger than the ones previously reported on the literature.


Operations Research Letters | 2011

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

Ricardo Saraiva de Camargo; Gilberto de Miranda; Ricardo Poley Martins Ferreira

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.


Transportation Science | 2015

The Hub Line Location Problem

Elisangela Martins de Sá; Ivan Contreras; Jean-François Cordeau; Ricardo Saraiva de Camargo; Gilberto de Miranda

This paper presents the hub line location problem in which the location of a set of hub facilities connected by means of a path or line is considered. Potential applications arise in the design of public transportation and rapid transit systems, where network design costs greatly dominate routing costs and thus full interconnection of hub facilities is unrealistic. Given that service time is the predominant objective in these applications, the problem considers the minimization of the total weighted travel time between origin/destination nodes while taking into account the time spent to access and exit the hub line. An exact algorithm based on a Benders decomposition of a strong path-based formulation is proposed. The standard decomposition method is enhanced through the incorporation of several features such as a multicut strategy, an efficient algorithm to solve the subproblem and to obtain stronger optimality cuts, and a Benders branch-and-cut scheme that requires the solution of only one master problem. Computational results obtained on benchmark instances with up to 100 nodes confirm the efficiency of the proposed algorithm, which is considerably faster and able to solve larger instances than a general purpose solver.


Electronic Notes in Discrete Mathematics | 2013

A TABU SEARCH APPROACH FOR THE PRIZE COLLECTING TRAVELING SALESMAN PROBLEM

Odivaney Pedro; Rodney R. Saldanha; Ricardo Saraiva de Camargo

Abstract The Prize Collecting Traveling Salesman Problem is a generalization of the Traveling Salesman Problem. A salesman collects a prize for each visited city and pays a penalty for each non visited city. The objective is to minimize the sum of the travel costs and penalties, but collecting a minimum pre-established amount of prizes. This problem is here addressed by a simple, but efficient tabu search approach which had improved several upper bounds of the considered instances.


Pesquisa Operacional | 2011

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

Gilberto de Miranda Júnior; Ricardo Saraiva de Camargo; Luiz Ricardo Pinto; Samuel Vieira Conceição; Ricardo Poley Martins Ferreira

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.


A Quarterly Journal of Operations Research | 2017

A multi-objective capacitated rural school bus routing problem with heterogeneous fleet and mixed loads

Fátima Machado Souza Lima; Davi Simões Pereira; Samuel Vieira Conceição; Ricardo Saraiva de Camargo

Four multi-objective meta-heuristic algorithms are presented to solve a multi-objective capacitated rural school bus routing problem with a heterogeneous fleet and mixed loads. Three objectives are considered: the total weighted traveling time of the students, the balance of routes among drivers, and the routing costs. The proposed methods were compared with one from the literature, and their performance assessed observing three multi-objective metrics: cardinality, coverage, and hyper-volume. All four devised methods outperformed the one from the literature. The algorithm with a path relinking procedure embedded during the crowding distance selection scheme had the best overall performance.


Computers & Operations Research | 2018

Benders decomposition applied to a robust multiple allocation incomplete hub location problem

Elisangela Martins de Sá; Reinaldo Morabito; Ricardo Saraiva de Camargo

Abstract This paper focuses on a multiple allocation incomplete hub location problem in which a hub network can be partially interconnected by hub arcs, direct connections between non-hub nodes are allowed, and uncertainty is assumed for the data of origin-destination demands and hub fixed costs. This problem consists of locating hubs, activating hub arcs and routing the demand flows over the designed network such that the total cost is minimized. The total cost is composed of fixed setup costs for hubs and hub arcs, and of transportation costs. This problem has economical and social appeals for designers of public transportation systems and other hub networks. A robust optimization approach is chosen to address the data uncertainty considering that demand flows and fixed setup costs are not known with certainty in advance. The computational experiments on benchmark instances from the hub location literature showed that the proposed robust model renders better assurance of not violating budget constraints than the deterministic version. Further, two specialized Benders decomposition frameworks and an ILS-VND stochastic local search procedure are also devised to tackle larger problem instances with up to 100 nodes in reasonable computational times.


Pesquisa Operacional | 2012

Addressing congestion on single allocation hub-and-spoke networks

Ricardo Saraiva de Camargo; Gilberto de Miranda

When considering hub-and-spoke networks with single allocation, the absence of alternative routes makes this kind of systems specially vulnerable to congestion effects. In order to improve the design of such networks, congestion costs must be addressed. This article deploys two different techniques for addressing congestion on single allocation hub-and-spoke networks: the Generalized Benders Decomposition and the Outer Approximation method. Both methods are able to solve large scale instances. Computational experiments show how the adoption of advanced solution strategies, such as Pareto-optimal cut generation on the Master Problem branch-and-bound tree, may be decisive. They also demonstrate that the solution effort is not only associated with the size of the instances, but also with their combination of the installation and congestion costs.


PLOS ONE | 2018

Flexibility evaluation of multiechelon supply chains

João Flávio de Freitas Almeida; Samuel Vieira Conceição; Luiz Ricardo Pinto; Ricardo Saraiva de Camargo; Gilberto de Miranda Júnior

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.

Collaboration


Dive into the Ricardo Saraiva de Camargo's collaboration.

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Gilberto de Miranda

Universidade Federal de Minas Gerais

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Alexandre Xavier Martins

Universidade Federal de Ouro Preto

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Samuel Vieira Conceição

Universidade Federal de Minas Gerais

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Elisangela Martins de Sá

Universidade Federal de Minas Gerais

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Gilberto de Miranda Júnior

Universidade Federal de Minas Gerais

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Rodney R. Saldanha

Universidade Federal de Minas Gerais

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Bruno N. Gomes

Universidade Federal de Minas Gerais

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Luiz Ricardo Pinto

Universidade Federal de Minas Gerais

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James F. Campbell

University of Missouri–St. Louis

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