Abstract
Airports have traditionally been considered as public utility providers, with little potential to develop significant market opportunities. Liberalisation of the air transport market around the globe has introduced new dynamics into the airport industry. The emergence of competition and the transition in ownership (towards privatisation or commercialisation) demand a different perspective in airport management. This paper aims at explaining the complexity of the airport business, in particular in what concerns the role of the airport as a firm that operates in a network of stakeholders, to produce a set of service packages targeted at several types of customers. (C) 2013 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Scientific Committee

Abstract
This paper presents an innovative and flexible approach for recommending the number, size and composition of purchasing groups, for a set of hospitals willing to cooperate, while minimising their shared supply chain costs. This approach makes the financial impact of the various cooperation alternatives transparent to the group and the individual participants, opening way to a negotiation process concerning the allocation of the cooperation costs and gains. The approach was developed around a hybrid Variable Neighbourhood Search (VNS)/Tabu Search metaheuristic, resulting in a flexible tool that can be applied to purchasing groups with different characteristics, namely different operative and market circumstances, and to supply chains with different topologies and atypical cost characteristics. Preliminary computational results show the potential of the approach in solving a broad range of problems.

Abstract
The paper firstly reviews the relevant concepts on virtual enterprise operations as well as industrial maintenance processes. Then a virtual enterprise enabling platform is presented. The architecture of the platform and its main modules are briefly introduced. Within this platform, a smart object extension is highlighted. This smart object is used to collect data from remote equipment and pass it to the Virtual Enterprise Management Platform (VEMP) through a gateway. The data collected by the smart object will be aggregated and monitored, using the business intelligence tools of the platform, enabling the implementation of maintenance strategies, rising fault conditions that will trigger a repair business process. In the final part of the paper, it is discussed a business case for a SME with worldwide operations.

Abstract
The use of optimization techniques is well established in forest short-term planning and decision-making. Yet, existing techniques may pose some limitations for tackling with stochastic factors impacting in the execution of forest operations, such as delays, equipment breakdowns and other unexpected events. This paper explores the potential of using optimization techniques in combination with discrete-event simulation (DES) models for planning harvesting and logistics operations acknowledging uncertainty. DES models may be useful for assessing the performance and identifying bottlenecks associated with the execution of the deterministic plans retrieved with optimization techniques, when such stochastic events occur. This paper further presents an approach for the combination of a heuristic and a DES model developed in SIMIO. This approach was used to solve the raw material reception problem (RMRP) at a Portuguese pulp mill. This paper concludes with the analysis of the performance of deterministic schedules for the wood trucks considering uncertainty in their arrival at the mill.

Abstract
Neural regression provides a rapid solution to modeling complex systems with minimal computation effort. Recurrent structures such as fuzzy cognitive map (FCM) enable for drawing cause effect relationships among system variables assigned to graph nodes. Accordingly, the obtained matrix of edges, known as adjacency model, represents the overall behavior of the system. With this, there are many applications of semantic networks in data mining, computational geometry, physics-based modeling, pattern recognition, and forecast. This article examines a methodology for drawing application-specific adjacency models. The idea is to replace crisp neural weights with functions such as polynomials of desired degree, a property beyond the current scope of neural regression. The notion of natural adjacency matrix is discussed and examined as an alternative to classic neural adjacency matrix. There are examples of stochastic and complex engineering systems mainly in the context of modeling residential electricity demand to examine the proposed methodology.

Abstract
Highly perishable food products can lose an important part of their value in the distribution process. We propose a novel multi-objective model that decouples the minimization of the distribution costs from the maximization of the freshness state of the delivered products. The main objective of the work is to examine the relation between distribution scenarios and the cost-freshness trade-off. Small size instances adapted from the vehicle routing problem with time windows are solved with an epsilon-constraint method and for large size instances a multi-objective evolutionary algorithm is implemented. The computational experiments show the conflicting nature of the two objectives.