Abstract
Deterministic models based on most likely forecasts can bring simplicity to the electricity power planning but do not explicitly consider uncertainties and risks which are always present on the electricity systems. Stochastic models can account for uncertain parameters that are critical to obtain a robust solution, requiring however higher modelling and computational effort. The aim of this work was to propose a methodology to identify major uncertainties presented in the electricity system and demonstrate their impact in the long-term electricity production mix, through scenario analysis. The case of an electricity system with high renewable contribution was used to demonstrate how renewables uncertainty can be included in long term planning, combining Monte Carlo Simulation with a deterministic optimization model. This case showed that the problem, of including risk in electricity planning could be explored in short running time even for large real systems. The results indicate that high growth demand rate combined with climate uncertainty represent major sources of risk for the definition of robust optimal technology mixes for the future. This is particularly important for the case of electricity systems with high share of renewables as climate change can have a major role on the expected power output.

Abstract
The Brazilian power generation sector faces a paradigm change driven, on one hand, by a shift from a hydropower dominated mix and, on the other, by international goals for reducing greenhouse gases emissions. The objective of this work was to evaluate five scenarios for the Brazilian power system 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. Taking into account the results from multicriteria analysis, scenario preference followed the order of increasing share of renewables in the power system. The preferable option for the future Brazilian power system 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 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.

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Abstract
Nowadays, it has been observed an increasing awareness and understanding on the subject of sustainable companies and business models, addressing multi-disciplinary approaches that cover not only economical problems, but also social and environmental challenges. Supply chains and especially collaborative networks managers are increasingly aware of these sustainability issues, continuously seeking to meet current human needs while preserving environmental safety. Only this way, focusing on its sustainable growing, it is possible to preserve companies' steadiness. In order to achieve this goal, sustainable networks must ensure that each partner is fully aligned and committed with economic, environmental and social axes that rule the network operational behaviour. Nevertheless, in order to achieve this level of maturity within such complex and turbulent environments, organisations need to improve the quality of their performance assessment approaches, integrating the different sustainability perspectives. To accomplish this, it is critical to establish specific indicators responsible to formalise and evaluate partners' behaviour, according to well-identified objectives, as well as fuse this information in a comprehensive and user-friendly way. This paper presents a new approach, based on a fuzzy logic-based algorithm, for sustainable network performance and risk assessment. © 2016 Inderscience Enterprises Ltd.

Abstract
Complexity in manufacturing systems appears under a variety of aspects, namely product, processes and operations and systems. Considering that the manufacturing environment is rapidly and constantly changing, with higher levels of customization and complexity, there is higher demand for flexibility and adaptability from companies. In this context, it seems essential to explore new approaches that can support decision-makers to take better decisions concerning the action plans that they need to launch to achieve the expected strategic and operational performance and alignment goals. Companies should become able to analyse their performance drivers, understand their meaning and the feedback loops that affect them. Therefore, decision makers can look into the future, and act even before these causes affect the transformation systems efficiency and effectiveness. This paper presents an approach oriented to multi-performance measurement in complex manufacturing environments. With this approach it is expected to overcome the gap between the operational and strategic layers of a manufacturing system, in order to reduce time when measuring performance and reacting to unexpected behaviours, as well as reduce errors when taking decisions. Moreover, it is expected to decrease the time necessary to calculate an indicator or to introduce a new one into performance management process, reducing the operational costs.

Abstract