Abstract in English:

ABSTRACT The present research aims to propose a Supplier Selection Model that creates collaborative relationships in Supply Chains, so that suppliers can be previously categorized into the cooperation, the coordination and the collaboration levels. Applied, quantitative, exploratory and descriptive research methods were used. A bibliographical research, a questionnaire, and the quantitative modeling were adopted as methodological procedures. The managers responsible for the Supplier Selection process in the Brazilian Wind Energy companies participated in this research. First, a criteria framework for the Selection of Supplier of goods and services was developed. Second, a Multicriteria Decision Aiding Model was created and validated, enabling the classification of the suppliers according to the relationship levels in a systematic way in terms of their performance from a set of criteria by implementing the ELECTRE TRI method. Firms can use the Model periodically in order to revise the supplier assessment and, if needed, direct them to either an inferior or superior class.

Abstract in English:

ABSTRACT Maritime shipping is vital to worldwide commerce. Due to the high flow in ports throughout the world, the efficient allocation of vessels in berths has become a problem. A new mathematical model and several algorithms are proposed in this paper to planning the allocation of the vessels in berths and the allocation of resources to the service of each vessel. Those resources, in general, are machines to load or unload vessels. The mathematical model was implemented on Cplex and can solve small scale instances, due to its high complexity. To solve larger instances, a genetic algorithm-based metaheuristic, a first-in first-out heuristic, and a machine allocation algorithm are also proposed in this paper. The model and the algorithms produce very useful and interesting results. Comparing, the results produced by the GA are, on average, 94% better than the results of the Cplex and 26% better than the results of FIFO.

Abstract in English:

ABSTRACT The assessment of port efficiency through Data Envelopment Analysis (DEA) is a valuable tool for ensuring accurate investment decisions in this capital-intensive industry. This study analyses the application of DEA on container ports. It assesses the efficiency of Brazilian container ports by proposing the inclusion of an intangible input related to the management practices of the terminals. In order to verify the effects of this input inclusion, the efficiency of 13 terminals is evaluated through the application of an IDEA-CRS model, which enables an appropriate handling of qualitative variables, with an orientation to the maximization of a single output, the annual container throughput, and inputs related to infrastructure, superstructure, and management unchanged. Model findings stress out the relevance of this intangible variable and the result that only three terminals are assessed as efficient, wherein the ones with the most robust management structure were not necessarily the most efficient.

Abstract in English:

ABSTRACT Black and Litterman proposed a portfolio selection model that blends investor’s views on asset returns with market equilibrium concepts to construct optimal portfolios. However, the model efficiency relies on the performance of investors’ views regarding tradable assets, which is challenging in practice. Venturing to improve Black-Litterman practical application, this work provides new insights based on views about macroeconomic factors, which are largely available, though not directly tradable. The main advantage is that market players usually provide predictions on these factors publicly. We present a case study based on the information disclosed by the Brazilian Central Bank to validate the proposed framework. The out-of-sample, risk-adjusted returns obtained incorporating the players’ macroeconomic expectations applying the proposed framework outperformed the traditional mean-variance model as well as the Brazilian stock index benchmark.

Abstract in English:

ABSTRACT Level of repair analysis (LORA) aims to determine the optimal repair policy for complex systems’ components. A repair policy is an a priori decision about which faulty components to discard or repair, and where these actions should take place. Traditionally, LORA models have assumed the maintenance network as pre-defined and identified the resources required to perform the maintenance at each facility as an output. In this paper, the maintenance network itself is an output rather than an input. Other advantages are the ability to deploy different types of resources at the operational level and to allow precise identification of the faulty component. We propose a mixed integer programming (MIP) formulation for the optimization problem, associated with a flow model. Experiments using a set of hypotheticals, but adequate for the purposes of the study, instances provide strong evidence that the formulation’s capabilities can lead to significant cost savings.

Abstract in English:

ABSTRACT This paper presents an approach to deal with horizontal cargo stability in container loading problems. Cargo stability has been explored mainly with support factors that constrain the minimum area of each box’s faces to be supported by other boxes. On the other hand, we propose an approach based on the static equilibrium of rigid bodies to check the static stability of a given packing. The approach is used as a cutting plane routine in a branch-and-cut framework to the single container loading problem. This framework considers the resolution of an integer linear programming model to obtain feasible packings next checked with the proposed approach to avoid unstable packings. The computational experiments consider 180 benchmark instances on which stable solutions of the proposed approach have better container fill rates than the support factor approach. In terms of lateral support, the proposed approach provides the minimum value inferior to 70% on average, which is satisfactorily smaller and less restrictive than the full support. Results also indicate that more unstable solutions emerge from refined grids and fewer types of boxes available.

Abstract in English:

ABSTRACT The generalized additive model (GAM) has been used in many epidemiological studies where frequently the response variable is a nonnegative integer-valued time series. However, GAM assume that the observations are independent, which is generally not the case in time series. In this paper, an autoregressive moving average (ARMA) component is incorporated to the GAM. The resulting GAM-ARMA model is based on the generalized linear autoregressive moving average (GLARMA) model where some linear components are replaced by natural splines. Numerical simulations are presented and show that the ARMA component influences the estimation. In a real data analysis of the effects of air pollution on respiratory disease in the metropolitan area of Belo Horizonte, Brazil, it is shown that the proposed model presents a better fit when compared to the classical GAM approach, that does not take into account the autocorrelation of the data.

Abstract in English:

ABSTRACT The purpose of this paper is to investigate the prioritization of Radio Frequency Identification Technology (RFID) logistics projects in a developing economy. This paper adopts a Multi-Criteria DecisionMaking (MCDM) method to assess RFID logistics projects. The study examines the use of RFID in the Brazilian aerospace industry and contributes to both the RFID and MDCA literature. The main contribution of this article is to present RFID applications in industrial processes and their strategic approaches, seeking to improve the efficiency, flexibility and the inventory visibility. The approach proposed a decision process to assess RFID logistics projects in the Brazilian aeronautics industry.

Abstract in English:

ABSTRACT This study associates graph theory and a multi-criteria decision aid technique, presenting a different process for doing the investigation of criminal networks. In the criminal subject, privacy concerns limit identification. For this reason, the database composed of 110 actors, involving criminals and peripheral characters to the network, was identified by numbers, without names and penalties. The discrimination of critical actors in criminal networks can help law enforcement officers to conduct a more detailed investigation for their disruption. Communication between drug traffickers was transformed into different centrality indices for each actor in their social network. Centralities and actors compose a decision matrix, analyzed by the Composition of Probabilistic Preferences to identify the most relevant actors in the criminal network. Results indicated that the five actors highlighted in the real investigation have a clear distinction of importance in the network, which in a way have been ratified.

Abstract in English:

ABSTRACT Recent empirical results show that forecast combinations and cross-learning schemes are winning approaches in the time series field. Although many competition-winning combination methods - with cross-learning or not - use static weights along the forecasting horizon, we could not find extensive work about the effects of using horizon-optimized weights. This paper proposes a forecast combination framework and provides a considerably sizeable empirical investigation into the use of horizon-optimized weights, i.e., weights that may vary over the forecasting horizon. We build on cross-learning, time series clustering and cross-validation to form Horizon-Optimized Convex Combinations (HOC2) of forecasts from five methods: Automated exponential smoothing, Automated ARIMA, Theta, TBATS, and Seasonal naïve. Our combinations were tested with data from the previous M1, M3 and M4 forecast competitions, comprising 104,004 time series with different frequencies and lengths. The results shall be helpful to support future research on how horizon-optimized weights can be used interchangeably with static ones.

Abstract in English:

ABSTRACT This article aims to analyze the shares that make up the Brazilian IBRX100 index, verifying which sectors had the greatest influence on the Stock Exchange in 2018, 2019, and 2020. For this purpose, the theory of graph centrality measures was used to discover the most central shares. A balance analysis of the graphs was also performed, since balanced graphs are more stable, generating a more predictable stock portfolio. This study may help investors to compose a safer stock portfolio and identify which stocks are most correlated with each other. The most central shares can aid in perceiving stock market trends.

Abstract in English:

ABSTRACT Optimal Phasor Measurement Unit (PMU) Placement (OPP) aims to reduce equipment costs while maintaining electrical systems observability. PMUs synchronized phasor data provide electrical systems information with high quality and frequency enabling the implementation of faster than real-time state estimators. We applied Chemical Reaction Optimization (CRO) method at OPP problem, testing it successfully at IEEE power system databases (14, 30, 57 and 118 bars) through two distinct models. The relationships between CRO parameters and the occurrence of elementary reactions was exploited, achieving better results through specific reactions. Due to problems similarity and larger scales, the software was adapted to Beasley OR-Library Set Covering (SC) problems. In the process to achieve GAPs smaller than 10% for some files, we tried out dedicated local searches, disturbance recurrence limits and stop condition changes. However, we suggest continuing to evaluate CRO method adherence to SC problem using different data structures to decrease computational times.

Abstract in English:

ABSTRACT We propose a mathematical model including queueing theory to reduce the negative consequences generated by large queues of vehicles as a result of traffic incidents. The mathematical model maximizes the number of incidents’ responses, and minimizes the non-recurrent congestion generated by them, considering a predetermined time to clear them, and an average or maximum response time. The model is applied to real-world incident data from Avenida Brasil, Rio de Janeiro, and the results showed that it is possible to concentrate the attendance in the critical areas of the expressway. The mathematical model proposed in this work can be used as a decision tool to help the government defining the acceptable average time for incident response, and the number of tow trucks necessary to achieve a pre-established service level.

Abstract in English:

ABSTRACT The main goal of this paper is to propose a methodology for selecting investment portfolios designed to generate financial returns and a socio-environmental impact. This work suggests a seven-step protocol that prioritizes the use of scientific evidence, quality data sources and the interdependence of investment alternatives. In particular, it proposes a multi-objective evolutionary optimization model to estimate the efficient frontier and select the portfolio according to the decision maker’s preferences. By an innovative extension of the concept of portfolio diversification, already well understood in traditional portfolio management, this study concludes that it is possible for impact investors to build more efficient portfolios. Due to the scarcity of similar studies in the literature and the rare use of rigorous methods for implementing impact investments, this study, therefore, presents an academic contribution to the field and illustrates the use of the proposed methodology to improve management decisions.