New simple and efficient heuristics for the uncapacitated single allocation hub location problem

Hub-and-spoke networks are widely studied in the area of location theory. They arise in several contexts, including passenger airlines, postal and parcel delivery, and computer and telecommunication networks. Hub location problems usually involve three simultaneous decisions to be made: the optimal number of hub nodes, their locations and the allocation of the non-hub nodes to the hubs. In the uncapacitated single allocation hub location problem (USAHLP) hub nodes have no capacity constraints and non-hub nodes must be assigned to only one hub. In this paper, we propose three variants of a simple and efficient multi-start tabu search heuristic as well as a two-stage integrated tabu search heuristic to solve this problem. With multi-start heuristics, several different initial solutions are constructed and then improved by tabu search, while in the two-stage integrated heuristic tabu search is applied to improve both the locational and allocational part of the problem. Computational experiments using typical benchmark problems (Civil Aeronautics Board (CAB) and Australian Post (AP) data sets) as well as new and modified instances show that our approaches consistently return the optimal or best-known results in very short CPU times, thus allowing the possibility of efficiently solving larger instances of the USAHLP than those found in the literature. We also report the integer optimal solutions for all 80 CAB data set instances and the 12 AP instances up to 100 nodes, as well as for the corresponding new generated AP instances with reduced fixed costs. Published by Elsevier Ltd.

The single machine earliness and tardiness scheduling problem: lower bounds and a branch-and-bound algorithm

This paper addresses the single machine scheduling problem with a common due date aiming to minimize earliness and tardiness penalties. Due to its complexity, most of the previous studies in the literature deal with this problem using heuristics and metaheuristics approaches. With the intention of contributing to the study of this problem, a branch-and-bound algorithm is proposed. Lower bounds and pruning rules that exploit properties of the problem are introduced. The proposed approach is examined through a computational comparative study with 280 problems involving different due date scenarios. In addition, the values of optimal solutions for small problems from a known benchmark are provided.

Some heuristic algorithms for total tardiness minimization in a flowshop with blocking

The flowshop scheduling problem with blocking in-process is addressed in this paper. In this environment, there are no buffers between successive machines: therefore intermediate queues of jobs waiting in the system for their next operations are not allowed. Heuristic approaches are proposed to minimize the total tardiness criterion. A constructive heuristic that explores specific characteristics of the problem is presented. Moreover, a GRASP-based heuristic is proposed and Coupled with a path relinking strategy to search for better outcomes. Computational tests are presented and the comparisons made with an adaptation of the NEH algorithm and with a branch-and-bound algorithm indicate that the new approaches are promising. (c) 2007 Elsevier Ltd. All rights reserved.

A generalized multi-period mean-variance portfolio optimization with Markov switching parameters

In this paper, we deal with a generalized multi-period mean-variance portfolio selection problem with market parameters Subject to Markov random regime switchings. Problems of this kind have been recently considered in the literature for control over bankruptcy, for cases in which there are no jumps in market parameters (see [Zhu, S. S., Li, D., & Wang, S. Y. (2004). Risk control over bankruptcy in dynamic portfolio selection: A generalized mean variance formulation. IEEE Transactions on Automatic Control, 49, 447-457]). We present necessary and Sufficient conditions for obtaining an optimal control policy for this Markovian generalized multi-period meal-variance problem, based on a set of interconnected Riccati difference equations, and oil a set of other recursive equations. Some closed formulas are also derived for two special cases, extending some previous results in the literature. We apply the results to a numerical example with real data for Fisk control over bankruptcy Ill a dynamic portfolio selection problem with Markov jumps selection problem. (C) 2008 Elsevier Ltd. All rights reserved.

Two-level network design with intermediate facilities: An application to electrical distribution systems

We consider the two-level network design problem with intermediate facilities. This problem consists of designing a minimum cost network respecting some requirements, usually described in terms of the network topology or in terms of a desired flow of commodities between source and destination vertices. Each selected link must receive one of two types of edge facilities and the connection of different edge facilities requires a costly and capacitated vertex facility. We propose a hybrid decomposition approach which heuristically obtains tentative solutions for the vertex facilities number and location and use these solutions to limit the computational burden of a branch-and-cut algorithm. We test our method on instances of the power system secondary distribution network design problem. The results show that the method is efficient both in terms of solution quality and computational times. (C) 2010 Elsevier Ltd. All rights reserved.

Spacetime noncommutativity with a bifermionic parameter

We consider a Moyal plane and propose to make the noncommutativity parameter Theta(mu nu) bifermionic, i.e. composed of two fermionic (Grassmann odd) parameters. The Moyal product then contains a finite number of derivatives, which avoid the difficulties of the standard approach. As an example, we construct a two-dimensional noncommutative field theory model based on the Moyal product with a bifermionic parameter and show that it has a locally conserved energy-momentum tensor. The model has no problem with the canonical quantization and appears to be renormalizable.

U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem

This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. (C) 2009 Elsevier Ltd. All rights reserved.

Predicting random effects with an expanded finite population mixed model

Prediction of random effects is an important problem with expanding applications. In the simplest context, the problem corresponds to prediction of the latent value (the mean) of a realized cluster selected via two-stage sampling. Recently, Stanek and Singer [Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 119-130] developed best linear unbiased predictors (BLUP) under a finite population mixed model that outperform BLUPs from mixed models and superpopulation models. Their setup, however, does not allow for unequally sized clusters. To overcome this drawback, we consider an expanded finite population mixed model based on a larger set of random variables that span a higher dimensional space than those typically applied to such problems. We show that BLUPs for linear combinations of the realized cluster means derived under such a model have considerably smaller mean squared error (MSE) than those obtained from mixed models, superpopulation models, and finite population mixed models. We motivate our general approach by an example developed for two-stage cluster sampling and show that it faithfully captures the stochastic aspects of sampling in the problem. We also consider simulation studies to illustrate the increased accuracy of the BLUP obtained under the expanded finite population mixed model. (C) 2007 Elsevier B.V. All rights reserved.

Existence of orthogonal geodesic chords on Riemannian manifolds with concave boundary and homeomorphic to the N-dimensional disk

In this paper we give a proof of the existence of an orthogonal geodesic chord on a Riemannian manifold homeomorphic to a closed disk and with concave boundary. This kind of study is motivated by the link (proved in Giambo et al. (2005) [8]) of the multiplicity problem with the famous Seifert conjecture (formulated in Seifert (1948) [1]) about multiple brake orbits for a class of Hamiltonian systems at a fixed energy level. (C) 2010 Elsevier Ltd. All rights reserved.

Stacked central configurations for the spatial six-body problem

In this paper we show the existence of three new families of stacked spatial central configurations for the six-body problem with the following properties: four bodies are at the vertices of a regular tetrahedron and the other two bodies are on a line connecting one vertex of the tetrahedron with the center of the opposite face. (c) 2009 Elsevier B.V. All rights reserved.

Efeito da oxigenoterapia hiperbárica: relato de caso de paciente com síndrome hepato-pulmonar em lista de espera para transplante hepático

INTRODUÇÃO: As doenças hepáticas apresentam índices de morbidade e mortalidade elevados e quando em estágio avançado têm o transplante do fígado como forma de tratamento potencialmente curativo e eficaz, embora este não possa ser oferecido a todos os pacientes. Isso faz com que essas doenças sejam consideradas problema de saúde pública em todo o mundo. Os cuidados clínicos para manter o paciente com condições de esperar e suportar o transplante continua um desafio. RELATO DO CASO: Mulher com 65 anos de idade, procedente do Recife, com diagnóstico de cirrose hepática secundária a vírus C apresentava dispnéia importante aos mínimos esforços tendo PaO2 de repouso de 60 mmHg e O2 de 90%, com espirometria normal. Realizou eco-Doppler que evidenciou shunt pulmonar importante. Durante a triagem em lista de transplante (MELD de 16 em agosto de 2006) foi optado pelo início de sessões de oxigenioterapia em câmara hiperbárica a fim de melhorar a sintomatologia respiratória da síndrome hepato-pulmonar. Apresentava melhora substancial da tolerância ao exercício após a terapia hiperbárica, assim como os valores do PaO2 à gasometria. Realizou 10 sessões de oxigenioterapia em câmara hiperbárica. Realizou transplante hepático em outubro de 2007 e vem em acompanhamento ambulatorial com boa evolução e melhora substancial da dispnéia. CONCLUSÃO: Constatou-se melhora da condição hepato-pulmonar após oxigenoterapia hiperbárica. Desta forma, ela surge como mais uma ferramenta para o tratamento das doenças hepáticas, devendo ser realizados outros estudos que avaliem sua utilização clínica.

A fuzzy reed-frost model for epidemic spreading

In this paper, we present a fuzzy approach to the Reed-Frost model for epidemic spreading taking into account uncertainties in the diagnostic of the infection. The heterogeneities in the infected group is based on the clinical signals of the individuals (symptoms, laboratorial exams, medical findings, etc.), which are incorporated into the dynamic of the epidemic. The infectivity level is time-varying and the classification of the individuals is performed through fuzzy relations. Simulations considering a real problem with data of the viral epidemic in a children daycare are performed and the results are compared with a stochastic Reed-Frost generalization

Reducing mean flow time in permutation flow shop

This paper deals with the traditional permutation flow shop scheduling problem with the objective of minimizing mean flowtime, therefore reducing in-process inventory. A new heuristic method is proposed for the scheduling problem solution. The proposed heuristic is compared with the best one considered in the literature. Experimental results show that the new heuristic provides better solutions regarding both the solution quality and computational effort.

Evaluation of two round baling systems for harvesting understory biomass

The objective of this study was to evaluate the performance and to estimate costs of two round baling systems for harvesting understory biomass. One system was a cutter-shredderbaler prototype (Bio-baler). The other system required two successive operations. The first operation was cutting and shredding with a Supertrak tractor equipped with a Fecon mulcher head. The second operation was baling with a Claas baler. The machines were evaluated in three different pine stands on the Osceola National Forest in Florida, United States. Data collection included time study, fuel consumption and bale measurements. Material was collected from a sample of bales for heat and moisture content determination. On the most representative site (Site 2), the Bio-baler recovered 8.05 green t ha(-1) while the mulcher and the Claas baler recovered 9.75 green t ha(-1) (43 and 52 percent of original understory biomass, respectively). Productivity was 0.30 ha h(-1) for the Bio-baler and 0.51 ha h(-1) for the Claas baler. Density of the bales was 321 green kg m(-3) for the Bio-baler and 373 green kg m(-3) for the Claas baler. Average net heat content was 6263 MJ bale(-1) for the Bio-baler and 6695 MJ bale(-1) for the Claas baler with biomass containing 38 percent of moisture content on a wet basis. cost per unit area was less with the Bio-baler (US$320.91 ha(-1)) than with the mulcher-baler system (US$336.62-US$596.77 ha(-1)). Published by Elsevier Ltd.

PVALB, a New Hurthle Adenoma Diagnostic Marker Identified through Gene Expression

Context: A better means to accurately identify malignant thyroid nodules and to distinguish them from benign tumors is needed. We previously identified markers for detecting thyroid malignancy, with sensitivity estimated at or close to 100%. One lingering problem with these markers was that false positives occurred with Hurthle cell adenomas (HCA) which lowered test specificity. Methods: To locate accurate diagnostic markers, we profiled in depth the transcripts of a HCA and a Hurthle cell carcinoma (HCC). From 1146 differentially expressed genes, 18 transcripts specifically expressed in HCA were tested by quantitative PCR in a wide range of thyroid tumors (n = 76). Sensibility and specificity were calculated using receiver operating characteristic (ROC). Selected markers were further validated in an independent set of thyroid tumors (n = 82) by immunohistochemistry. To define the panel that would yield best diagnostic accuracy, these markers were tested in combination with our previous identified markers. Results: Seventeen of the 18 genes showed statistical significance based on a mean relative level of expression (P < 0.05). KLK1 (sensitivity = 0.97) and PVALB (sensitivity = 0.94) were the best candidate markers. The combination of PVALB and C1orf24 increased specificity to > 97% and maintained sensitivity for detection of carcinoma. Conclusion: We identified tumor markers that can be used in combination for a more accurate preoperative diagnosis of thyroid nodules and for postoperative diagnosis of thyroid carcinoma in tumor sections. This improved test would help physicians rapidly focus treatment on true malignancies and avoid unnecessary treatment of benign tumors, simultaneously improving medical care and reducing costs. (J Clin Endocrinol Metab 96: E151-E160, 2011)