Abstract
Several collaborative business frameworks have been developed in order to support organizations to attain interoperability in today's networked business arena. However, the diversity of frameworks generates selection problems. The goal of this article is to present an analytical method supporting the comparison of collaborative business frameworks, which may also be used by decision makers in the framework selection process. The results obtained by comparing four collaborative business frameworks are discussed.

Abstract
Partner selection in virtual enterprises (VE) can be viewed as a multi-criteria decision making problem that involves assessing trade-offs between conflicting tangible and intangible criteria. In general, this is a very complex problem due to the dynamic topology of the network, the large number of alternatives and the different types of criteria. In this paper we propose an iterative and interactive exploratory process to help the decision maker identify the companies that best suit the needs of each particular project. This is achieved by using cluster analysis to distinguish companies according to some selected features. We present an example to illustrate this approach.

Abstract
Due to its combinatorial nature, the Unit Commitment problem has for long been an important research challenge, with several optimization techniques, from exact to heuristic methods, having been proposed to deal with it. In line with one current trend of research, metaheuristic approaches have been studied and some interesting results have already been achieved and published. However, a successful utilization of these methodologies in practice, when embedded in Energy Management Systems, is still constrained by the reluctance of industrial partners in using techniques whose performance highly depends on a correct parameter tuning. Therefore, the application of metaheuristics to the Unit Commitment problem does still justify further research. In this paper we propose a new search strategy, for Local Search based metaheuristics, that tries to overcome this issue. The approach has been tested in a set of instances, leading to very good results in terms of solution cost, when compared either to the classical Lagrangian Relaxation or to other metaheuristics. It also drastically reduced the computation times. Furthermore, the approach proved to be robust, always leading to good results independently of the metaheuristic parameters used.

Abstract
Nesting problems are particularly hard combinatorial problems. They involve the positioning of a set of small arbitrarily-shaped pieces on a large stretch of material, without overlapping them. The problem constraints are bidimensional in nature and have to be imposed on each pair of pieces. This all-to-all pattern results in a quadratic number of constraints. Constraint programming has been proven applicable to this category of problems, particularly in what concerns exploring them to optimality. But it is not easy to get effective propagation of the bidimensional constraints represented via finite-domain variables. It is also not easy to achieve incrementality in the search for an improved solution: an available bound on the solution is not effective until very late in the positioning process. In the sequel of work on positioning non-convex polygonal pieces using a CLP model, this work is aimed at improving the expressiveness of constraints for this kind of problems and the effectiveness of their resolution using global constraints. A global constraint "outside" for the non-overlapping constraints at the core of nesting problems has been developed using the constraint programming interface provided by Sicstus Prolog. The global constraint has been applied together with a specialized backtracking mechanism to the resolution of instances of the problem where optimization by Integer Programming techniques is not considered viable. The use of a global constraint for nesting problems is also regarded as a first step in the direction of integrating Integer Programming techniques within a Constraint Programming model.

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Abstract