Abstract
In this paper we present a Decision Support System (DSS) to deal with the partner selection problem taking place in the formation or re-organization of a Virtual Enterprise (VE). This DSS is based on a multi-criteria model and handles several types of data (numerical, interval, linguistic and binary). This approach is used to facilitate the expression of the decision maker's preferences and assessments about the potential partners and can be performed individually or by group. The system also allows the assignment of a degree of confidence to each linguistic statement. the operation of the DSS is structured in two phases. In the first phase it determines the set of non-dominated alternatives (potential VEs) through the use of meta-heuristics. The second phase ranks the alternatives for a possible network of enterprises configuring the VE. This is achieved through a procedure based on linguistic analysis and distance measures.

Abstract
Ranking problems arise from the knowledge of several binary relations defined on a set of alternatives, which we intend to rank. In a previous work, the authors introduced a tool to confirm the solutions of multi-attribute ranking problems as linear extensions of a weighted sum of preference relations. An extension of this technique allows the recognition of critical preference pairs of alternatives, which are often caused by inconsistencies. Herein, a confirmation procedure is introduced and applied to confirm the results obtained by a multi-attribute decision methodology on a tender for the supply of buses to the Porto Public Transport Operator.

Abstract
This paper develops a framework for performance appraisal in the context of a bank branch network. A data envelopment analysis model is advocated that can identify both input and output inefficiencies from a cost minimization perspective. The model is then applied to the analysis of a bank branch network and the production and value-added approaches are deployed jointly to give a comprehensive assessment of bank branch efficiency. The sources of underperformance are identified and their managerial implications are explored.

Abstract
This paper enhances cost efficiency measurement methods to account for different scenarios relating to input price information. These consist of situations where prices are known exactly at each decision making unit (DMU) and situations with incomplete price information. The main contribution of this paper consists of the development of a method for the estimation of upper and lower bounds for the cost efficiency (CE) measure in situations of price uncertainty, where only the maximal and minimal bounds of input prices can be estimated for each DMU. The bounds of the CE measure are obtained from assessments in the light of the most favourable price scenario (optimistic perspective) and the least favourable price scenario (pessimistic perspective). The assessments under price uncertainty are based on extensions to the Data Envelopment Analysis (DEA) model that incorporate weight restrictions of the form of input cone assurance regions. The applicability of the models developed is illustrated in the context of the analysis of bank branch performance. The results obtained in the case study showed that the DEA models can provide robust estimates of cost efficiency even in situations of price uncertainty.

Abstract
This paper describes an order planning system for dynamic supply chains, addressing the requirements of a make-to-order business environment. A distributed and decentralized information system based on an architecture of agents and extensively using the internet was designed and implemented, so as to enable new and more powerful decision support. The system aims at responding to the basic requirements of cooperativeness, integration and configurability. It was developed under the scope of the IST European Project Co-OPERATE and implements the functionality defined in the context of the 'request feasibility studies for the network' (ReFS) business solution.

Abstract
One major practical problem when applying traditional metaheuristics seems to be their strong dependency on parameter tuning. This issue is frequently pointed out as a major shortcoming of metaheuristics and is often a reason for Decision-Makers to reject using this type of approach in practical situations. In this paper we present a new search strategy - Constraint Oriented Neighbourhoods - that tries to overcome the referred drawback. The aim is to control the grade of randomness of metaheuristics, by defining "special" neighbourhood movements, that lead to a more robust heuristic, less dependent on parameter tuning. This is achieved by selecting and applying particular movements that take into account the potential violation of problem constraints. The strategy is illustrated in a real problem arising in the area of Power Systems Management - the Unit Commitment Problem, the computational experiments on a set of problem instances systematically outperforming those presented in the literature, both in terms of efficiency, quality of the solution and robustness of the algorithm.