A learning-based granular variable neighborhood search for a multi-period election logistics problem with time-dependent profits

Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0377221724004545#:~:text=Appendix%20A.-,Supplementary%20data,-References .Production, Manufacturing, Transportation and Logistics.Planning the election campaign for leaders of a political party is a complex problem. The party representatives, running mates, and campaign managers have to design an efficient routing and scheduling plan to visit multiple locations while respecting time and budget constraints. Given the limited time of election campaigns in most countries, every minute should be used effectively, and there is very little room for error. In this paper, we formalize this problem as the multiple Roaming Salesman Problem (mRSP), a new variant of the recently introduced Roaming Salesman Problem (RSP), where a predefined number of political representatives visit a set of cities during a planning horizon to maximize collected rewards, subject to budget and time constraints. Cities can be visited more than once and associated rewards are time-dependent (increasing over time) according to the day of the visit and the recency of previous visits. We develop a compact Mixed Integer Linear Programming (MILP) formulation complemented with effective valid inequalities. Since commercial solvers can obtain optimal solutions only for small-sized instances, we develop a Learning-based Granular Variable Neighborhood Search and demonstrate its capability of providing high-quality solutions in short CPU times on real-world instances. The adaptive nature of our algorithm refers to its ability to dynamically adjust the neighborhood structure based on the progress of the search. Our algorithm generates the best-known results for many instances

Enhanced index tracking with CVaR-based ratio measures

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Twenty years of linear programming based portfolio optimization

a b s t r a c t Markowitz formulated the portfolio optimization problem through two criteria: the expected return and the risk, as a measure of the variability of the return. The classical Markowitz model uses the variance as the risk measure and is a quadratic programming problem. Many attempts have been made to linearize the portfolio optimization problem. Several different risk measures have been proposed which are computationally attractive as (for discrete random variables) they give rise to linear programming (LP) problems. About twenty years ago, the mean absolute deviation (MAD) model drew a lot of attention resulting in much research and speeding up development of other LP models. Further, the LP models based on the conditional value at risk (CVaR) have a great impact on new developments in portfolio optimization during the first decade of the 21st century. The LP solvability may become relevant for real-life decisions when portfolios have to meet side constraints and take into account transaction costs or when large size instances have to be solved. In this paper we review the variety of LP solvable portfolio optimization models presented in the literature, the real features that have been modeled and the solution approaches to the resulting models, in most of the cases mixed integer linear programming (MILP) models. We also discuss the impact of the inclusion of the real features

Linear Programming Models based on Omega Ratio for the Enhanced Index Tracking Problem

Modern performance measures differ from the classical ones since they assess the performance against a benchmark and usually account for asymmetry in return distributions. The Omega ratio is one of these measures. Until recently, limited research has addressed the optimization of the Omega ratio since it has been thought to be computationally intractable. The Enhanced Index Tracking Problem (EITP) is the problem of selecting a portfolio of securities able to outperform a market index while bearing a limited additional risk. In this paper, we propose two novel mathematical formulations for the EITP based on the Omega ratio. The first formulation applies a standard definition of the Omega ratio where it is computed with respect to a given value, whereas the second formulation considers the Omega ratio with respect to a random target. We show how each formulation, nonlinear in nature, can be transformed into a Linear Programming model. We further extend the models to include real features, such as a cardinality constraint and buy-in thresholds on the investments, obtaining Mixed Integer Linear Programming problems. Computational results conducted on a large set of benchmark instances show that the portfolios selected by the model assuming a standard definition of the Omega ratio are consistently outperformed, in terms of out-of-sample performance, by those obtained solving the model that considers a random target. Furthermore, in most of the instances the portfolios optimized with the latter model mimic very closely the behavior of the benchmark over the out-of-sample period, while yielding, sometimes, significantly larger returns

The role of primary cilia in cancer

Primary cilia are microtubule based organelles that sense and transduce multiple extracellular signals to the interior of the cell through several signaling pathways. Ciliary defects lead to different pathologies. Interestingly, decreased ciliary expression has been associated with different types of cancer. In this review, we discuss the function and dynamics of primary cilia, their relation to different diseases and in particular their role in cancer, and how they can be explored as potential therapeutic targets.Sociedad Argentina de Fisiologí

A Closed-Form Solution of the Multi-Period Portfolio Choice Problem for a Quadratic Utility Function

In the present paper, we derive a closed-form solution of the multi-period portfolio choice problem for a quadratic utility function with and without a riskless asset. All results are derived under weak conditions on the asset returns. No assumption on the correlation structure between different time points is needed and no assumption on the distribution is imposed. All expressions are presented in terms of the conditional mean vectors and the conditional covariance matrices. If the multivariate process of the asset returns is independent it is shown that in the case without a riskless asset the solution is presented as a sequence of optimal portfolio weights obtained by solving the single-period Markowitz optimization problem. The process dynamics are included only in the shape parameter of the utility function. If a riskless asset is present then the multi-period optimal portfolio weights are proportional to the single-period solutions multiplied by time-varying constants which are depending on the process dynamics. Remarkably, in the case of a portfolio selection with the tangency portfolio the multi-period solution coincides with the sequence of the simple-period solutions. Finally, we compare the suggested strategies with existing multi-period portfolio allocation methods for real data.Comment: 38 pages, 9 figures, 3 tables, changes: VAR(1)-CCC-GARCH(1,1) process dynamics and the analysis of increasing horizon are included in the simulation study, under revision in Annals of Operations Researc

inv(11)(q13q23)

Review on inv(11)(q13q23), with data on clinics, and the genes involved

Valor preditivo da medida da cintura e da relação cintura-quadril no diagnóstico do diabetes melito e da dislipidemia

Purpose: Obesity rates are increasing in Brazil. Waist measurement (WM) is a good measure to evaluatelocalized obesity and it is easier to perform when compared to wait-hip-ratio (WHR). However the effects of regionalobesity were better described between WHR and cardiovascular risk factors. Aim: to test whether waist measurement alone is better than WHR to predict diabetes and high cholesterol. Methods: We analyzed positive and negative likelihood ratio (LR)of WM and WHR in a sample of 202 apparently healthy men and women aged 20-74 years as a predictive factor for diabetes and dislipidemia. Results: (1) For women, WHR was a better predictive factor for diabetes(LHR+ = 2,5 e LHR- = 0,2) and dislipidemia (LHR+ = 2,5, LHR- = 0,2) diagnosis than WM (diabetes, LHR+ = 1,9 eLHR- = 0,4; dislipidemia, LHR+ = 1,6 e LHR- = 0,6); (2) For men, WM was a better predictive factor for diabetes (LHR+ = 2,4e LHR- = 0,6) and dislipidemia (LHR+ = 5 e LHR- = 0,5) than for WHR (diabetes, LHR+ = 1,0 e LHR- = 0,9;dislipidemia, LHR+ = 2,1 e LHR- = 0,7 diagnosis. Conclusion: WM and WHR have different behaviors in men and women.Introdução: O índice de obesidade está aumentando no Brasil.A medida da cintura (MC) é uma boa medida para a avaliação da obesidade localizada e é mais fácil de ser adquirida do que a relação cintura quadril (RCQ). Entretanto, os efeitos da obesidade regional foram melhores descritos entre a RCQ e fatores de risco cardiovasculares. Objetivo: testar se a medida da cintura isolada é melhor do que a RCQ para predizer diabetes e dislipidemia. Métodos: analisamos a razão de verossimilhança positiva e negativa da MC e RCQ numa amostra de 202 homens e mulheres com idade variando de 20 a 74 anos e aparentemente saudáveis como fator preditivo para diabetes e dislipidemia. Resultados: (1) Para mulheres, a RCQ foi melhor fator preditivo para o diagnostico dediabetes (RV+ = 2,5 e RV- = 0,2) e dislipidemia (RV+2,5 e RV-=0,2) comparado a MC (diabetes, RV+ = 1,9 e RV- = 0,4; dislipidemia, RV+ = 1,6 e RV- = 0,6); (2) Para homens, a MC foi melhor fator preditivo para o diagnostico de diabetes (RV+ = 2,4 e RV- = 0,6) e dislipidemia (RV+ = 5 e RV- = 0,5) do que a RCQ (diabetes, RV+ = 1,0 e RV- = 0,9; dislipidemia, RV+ = 2,1 e RV- = 0,7). Conclusão: A MC e a RCQ se comportam diferentes entre homens e mulheres

Why do UK banks securitize?

Working paper seriesThe eight years from 2000 to 2008 saw a rapid growth in the use of securitization by UK banks. We aim to identify the reasons that contributed to this rapid growth. The time period (2000 to 2010) covered by our study is noteworthy as it covers the pre- nancial crisis credit- boom, the peak of the nancial crisis and its aftermath. In the wake of the nancial crisis, many governments, regulators and political commentators have pointed an accusing nger at the securitization market - even in the absence of a detailed statistical and economic analysis. We contribute to the extant literature by performing such an analysis on UK banks, fo- cussing principally on whether it is the need for liquidity (i.e. the funding of their balance sheets), or the desire to engage in regulatory capital arbitrage or the need for credit risk trans- fer that has led to UK banks securitizing their assets. We show that securitization has been signi cantly driven by liquidity reasons. In addition, we observe a positive link between securitization and banks credit risk. We interpret these latter ndings as evidence that UK banks which engaged in securitization did so, in part, to transfer credit risk and that, in comparison to UK banks which did not use securitization, they had more credit risk to transfer in the sense that they originated lower quality loans and held lower quality assets. We show that banks which issued more asset-backed securities before the nancial crisis su¤ered more defaults after the nancial crisis.The eight years from 2000 to 2008 saw a rapid growth in the use of securitization by UK banks. We aim to identify the reasons that contributed to this rapid growth. The time period (2000 to 2010) covered by our study is noteworthy as it covers the pre-financial crisis credit- boom, the peak of the financial crisis and its aftermath. In the wake of the financial crisis, many governments, regulators and political commentators have pointed an accusing finger at the securitization market - even in the absence of a detailed statistical and economic analysis. We contribute to the extant literature by performing such an analysis on UK banks, fo- cussing principally on whether it is the need for liquidity (i.e. the funding of their balance sheets), or the desire to engage in regulatory capital arbitrage or the need for credit risk trans- fer that has led to UK banks securitizing their assets. We show that securitization has been significantly driven by liquidity reasons. In addition, we observe a positive link between securitization and banks credit risk. We interpret these latter findings as evidence that UK banks which engaged in securitization did so, in part, to transfer credit risk and that, in comparison to UK banks which did not use securitization, they had more credit risk to transfer in the sense that they originated lower quality loans and held lower quality assets. We show that banks which issued more asset-backed securities before the financial crisis suffered more defaults after the financial crisis