Abstract
The Brazilian power generation sector faces a paradigm change driven by, on one hand, a shift from a hydropower dominated mix and, on the other hand, international goals for reducing greenhouse gas emissions. The objective of this work is to evaluate five scenarios for the Brazilian power sector until 2050 using a multi-criteria decision analysis tool. These scenarios include a baseline trend and low carbon policy scenarios based on carbon taxes and carbon emission limits. To support the applied methodology, a questionnaire was elaborated to integrate the perceptions of experts on the scenario evaluation process. Considering the results from multi-criteria analysis, scenario preference followed the order of increasing share of renewables in the power sector. The preferable option for the future Brazilian power sector is a scenario where wind and biomass have a major contribution. The robustness of the multi-criteria tool applied in this study was tested by a sensitivity analysis. This analysis demonstrated that, regardless of the respondents' preferences and backgrounds, scenarios with higher shares of fossil fuel sources are the least preferable option, while scenarios with major contributions from wind and biomass are the preferable option to supply electricity in Brazil through 2050.

Abstract
In the vehicle routing problem with heterogeneous fleet and variable costs (HFVRP), the group of routes to be developed to satisfy the demand of the customer must be determined, considering the minimization of the total costs of the travelled distance. Heuristic algorithms based on local searches use simple movements (neighborhoods) to generate feasible solutions to problems related to route design. In this article, we conduct a comparative analysis of granular neighborhoods in a Tabu Search for the HFVRP, in terms of the quality of the obtained solution. The computational experiments, performed on instances of benchmarking for the HFVRP, showed the efficiency and effectiveness of implementing some neighborhoods in metaheuristic algorithms of path, such as the Tabu Search.

Abstract
This paper develops a sustainable approach in the productive context associated with the fish industry. The supply network design problem is addressed by using mathematical programming in order to avail the best configuration for the productive system. In this study, some environmental constraints for each echelon have been presented. Two mathematical models are proposed: cost and profit model. The models are validated with actual data obtained from a Colombian fish industry. The obtained results indicate an improved performance of the new supply network design by considering the sustainability approach derived from sustainable supply chain management (SSCM).

Abstract

Abstract
This paper proposes a mathematical model and two heuristic procedures to solve the cutting stock problem with usable leftovers, relating the implications of the model with aspects considering sustainability in terms of environmental, economic and social issues. The possibility of generating leftovers that can be used or sold, reduces raw material waste during the cutting process and, consequently, increases companies’ profits. By reducing waste and increasing profits, companies can become more competitive in the market. They can also integrate environmental aspects into their operational strategies and, therefore, create a better self-image and profitability, generating more jobs and contributing to a stronger local economy. We believe that the model is more likely to be adopted by smaller companies, which generally face numerous barriers but at the same time have a significant social impact, generating income and jobs. Based on the knowledge of the authors, this is the first study that relates a cutting problem with its implications for sustainability. Computational tests were performed, and the obtained results are discussed considering the win-win approach to sustainability.

Abstract
Sugarcane cultivation is important for the economy of many countries, particularly for Brazil. This plant has been used to produce sugar, ethanol, second generation ethanol, fertilizers, as well as bioelectricity. Due to production growth and the establishment of mechanized sugarcane harvesting, this process needs to be optimized. High costs are linked to mechanized harvesting, which affect the total cost of production. One of the costs of harvesting is related to the long time the sugarcane harvesting machine takes to change the crop row to be cut. To help reduce costs, this work proposes a mathematical model to the Route Planning Problem for Mechanized Harvesting. This mathematical model minimizes the time of maneuvering the harvesting machine and, consequently, reduces fuel and labor costs, among others. Computer tests were performed using data supplied by a company from the sugarcane energy sector located in the state of São Paulo, Brazil. The results were compared to the traditional routes used by the company and proved the efficiency of the mathematical model in supplying solutions that minimize the time of harvesting machine maneuvers. Not only are there economic benefits, but also environmental ones that can be obtained.