Abstract
The concept of combining the power of several independent factories to achieve complex manufacturing processes as so-called virtual manufacturing enterprises is not new and has been addressed by several research projects in recent years. However, there is still a need for adequate methodological support and tools for modelling, structuring and controlling of the next generation of manufacturing systems, such as the virtual factory. In this research, a conceptual virtual factory reference model is presented with the goal to provide companies with general guidelines to manage and monitor the business processes that are needed to create, execute, and dissolve a virtual factory. The virtual factory reference model was built taking into account industrial's requirements and by reviewing the literature in several relevant fields of research such as collaborative networks, supply networks, manufacturing networks, supply chain management, and business processes. Afterwards, it has been validated through its application to future virtual factories of three different industrial sectors: machinery, energy, and semiconductor.

Abstract
Several fisheries across the world are managed by a quota regime. These quotas can be set yearly, monthly, weekly or daily. However, for some fish species demand seasonality may occur, which should be taken into consideration in the establishment of the quota (especially in those fisheries managed by daily or monthly quotas). This would allow fishermen to catch more fish at times of the year with higher demand in detriment of periods when demand is low. The present work investigates the existence of demand seasonality for bivalves from the artisanal dredge fleet. This fleet operates along the entire coast of the Portugal mainland. The analysis of fleets' revenue efficiency is assessed with Data Envelopment Analysis models, and the monthly seasonality effects on the revenue efficiency were tested using a Tobit regression. The results revealed that on the South coast there is a strong demand in the summer whereas on the western coast (northwest and southwest fishing areas) demand increases during Christmas and New Year festivities. Since this fishery is managed by weekly/daily quotas, it is proposed that these quotas should be redistributed in order to adjust them to periods of higher demand, thereby increasing the profitability of the vessels. The approach followed could be applied to similar fisheries worldwide.

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
In the ambit of Distribute/Agile/Virtual Enterprises, the resources/partners selection process is a critical issue in order to guarantee the success of such enterprises. The selection process is complex in the large sense of the word and for that we advocate the necessity of a broker to perform that task, conveniently assisted by a tool. In order to contribute to its construction, this paper presents the resolution of a resources system selection problem, designated by Dependent or Integral Selection Method without Pre-selection of Transport Resources, with two algorithms, an exact solution algorithm and an approximate one. The results demonstrate that the exact solution algorithm limitations can be covered by the approximate algorithm. With those results, the broker has the knowledge to perform the selection with the most adequate algorithm for each case of the problem (depending of the number of tasks and pre-selected resources) addressed in this paper. This paper brings a contribution to broker performance for the selection process. (C) 2014 The Authors. Published by Elsevier Ltd.

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.