Abstract
E-government has become a priority for many governments around the world and one of the main change drivers in the provision of new services in the public administration context. As public services are part of a complex network in which citizens are the main players, public authorities are increasingly trying to identify and deploy programmes that promote the modernisation and simplification of public services based on knowledge management. This chapter aims to understand the impact of e-government initiatives on consumer behaviour, as well as the importance of knowledge management for value creation in the public administration context. The Simplex programme, the main initiative in Portugal to develop administrative simplification and e-government, is analysed within two contexts: the central administration and the local municipality of Oporto. This chapter highlights that e-government has been an important factor contributing to both the administrative simplification and the improvement in the quality of public services.

Abstract
To cope with today market challenges and guarantee adequate competitive performances, companies have been decreasing their products life cycles, as well as increasing the number of product varieties and respective services available on their portfolio. Consequently, it has been observed an increasing in complexity in all domains, from product and process development, factory and production planning to factory operation and management. This reality implies that organizations should be able to compile and analyze, in a more agile way, the immense quantity of data generated, as well as apply the suitable tools that, based on this knowledge, will supports stakeholders to take decision envisioning future performance scenarios. Aiming to pursuing this vision was developed a proactive performance management framework, composed by a performance thinking methodology and a performance estimation engine. While the methodology developed is an extension of the Systems Dynamics approach for complex systems' performance management, on the other hand, the performance estimation engine is an innovative IT solution responsible by capturing lagging indicators, as well as estimate future performance behaviors. As main outcome of this research work, it was demonstrated that following a systematic and formal approach, it is possible to identify the feedback loops and respective endogenous and exogenous variables responsible by hindering the systems behavior, in terms of a specific KPI. Moreover, based on this enhanced understanding about manufacturing systems behavior, it was proved to be possible to estimate with high levels of confidence not only the present but also future performance behavior. From the combination of both qualitative and quantitative approaches, it was explored an enhanced learning machine algorithm capable to specify the curve of behavior, characteristic from a specific manufacturing system, and thus estimate future behaviors based on a set of leading indicators. In order to achieve these objectives, both Neural Networks and Unscented Kalman Filter for nonlinear estimation were applied. Important results and conclusions were extracted from an application case performed within a real automotive plant, which demonstrated the feasibility of this research towards a more proactive management approach.

Abstract

Abstract
Unicer, a major Portuguese beverage company, improved its tactical distribution planning decisions and study alternative scenarios for its supply strategies and network configuration as result of an operations research (OR)-driven process. In this paper, we present the decision support system responsible for this new methodology. At the core of this system is a mathematical programming-based heuristic that includes decision variables that address transportation and inventory management problems. Unicer runs a set of production and distribution platforms with various characteristics to fulfill customers demand. The main challenge of our work was to develop a tactical distribution plan, which Unicer calls an annual distribution budget, as realistically as possible without jeopardizing the nature of the strategic and tactical tool. The company had a complex tactical distribution planning problem because of the increasing variety of its stock-keeping units and its need for a flexible distribution network to satisfy its customers, who demand a very fragmented set of products. Atypical flows of finished products from Unicer's distribution centers to its production platforms are a major cause of this complexity, which yields an intricate supply chain. The quality of the solutions we provided and the implementation of a user-friendly interface and editable inputs and outputs for our decision support system motivated company practitioners to use it. Unicer saves approximately two million euros annually and provides better information to its decision makers. As a result, these decision makers now view their operations from a more OR-based perspective.

Abstract
This work addresses an integrated framework for deciding about the supplier selection in processed food supply chains that accounts for tactical production and distribution planning. We are especially concerned with the option of producing with local or mainstream raw materials. The contribution of this paper is two-fold. Firstly, it proposes a new multi-objective two-stage stochastic mixed-integer programming model for the supplier selection that maximizes the profit and minimizes the risk of a low customer service. Secondly, the main complexities of processed food supply chains management are considered: perishability of raw materials and final products, uncertainty at downstream and upstream parameters, and customer willingness to pay. Results indicate that dual sourcing is a strategy to be pursued across several scenarios. The multi-objective approach shows that a small decrease in the expected value of profit results in a significant increase in the customer service. Acknowledging the increase in customers willing to pay for local products is also fundamental.

Abstract
The possibilities of combining simulation and optimization are vast and the appropriate design highly depends on the problem characteristics. Therefore, it is very important to have a good overview of the different approaches. The taxonomies and classifications proposed in the literature do not cover the complete range of methods and overlook some important criteria. We provide a taxonomy that aims at giving an overview of the full spectrum of current simulation-optimization approaches. Our study may guide researchers who want to use one of the existing methods, give insights into the cross-fertilization of the ideas applied in those methods and create a standard for a better communication in the scientific community. Future reviews can use the taxonomy here described to classify both general approaches and methods for specific application fields.