Abstract
This paper addresses an integrated framework for deciding about the supplier selection in the processed food industry under uncertainty. The relevance of including tactical production and distribution planning in this procurement decision is assessed. The contribution of this paper is three-fold. Firstly, we propose a new two-stage stochastic mixed-integer programming model for the supplier selection in the process food industry that maximizes profit and minimizes risk of low customer service. Secondly, we reiterate the importance of considering main complexities of food supply chain management such as: perishability of both raw materials and final products; uncertainty at both downstream and upstream parameters; and age dependent demand. Thirdly, we develop a solution method based on a multi-cut Benders decomposition and generalized disjunctive programming. Results indicate that sourcing and branding actions vary significantly between using an integrated and a decoupled approach. The proposed multi-cut Benders decomposition algorithm improved the solutions of the larger instances of this problem when compared with a classical Benders decomposition algorithm and with the solution of the monolithic model.

Abstract
A rigorous evaluation of an artifact is one of the fundamental aspects to be considered in Design Science Research projects. This evaluation part becomes even more difficult when a large variety of artifacts must be designed for a project. This can be a challenge for the field of Collaborative Networks, which involves knowledge from several scientific disciplines. Case studies are one of the evaluation methods in Design Science Research that are used for an ex-post evaluation of artifacts. However, we argue that this method can also be used for the ex-ante evaluation, mainly when multidisciplinary research is being carried out. Therefore, the main objective of this paper is to present and discuss a multidisciplinary project of Collaborative Networks using case studies before and after the design of the artifacts. On top of the Design Science Research guidelines, this research is supported by knowledge from Business and Management Studies on how to effectively design and perform Case Study Research for artifact evaluation.

Abstract
While bottom-up approaches to object recognition are simple to design and implement, they do not yield the same performance as top-down approaches. On the other hand, it is not trivial to obtain a moderate number of plausible hypotheses to be efficiently verified by top-down approaches. To address these shortcomings, we propose a hybrid top-down bottom-up approach to object recognition where a bottom-up procedure that generates a set of hypothesis based on data is combined with a top-down process for evaluating those hypotheses. We use the recognition of rectangular cuboid shaped objects from 3D point cloud data as a benchmark problem for our research. Results obtained using this approach demonstrate promising recognition performances.

Abstract
Learning Management Systems (LMS) provide minimal support for educational use of virtual worlds. Integration efforts assume the educators are inside the virtual world, providing hooks to services in the external LMS, to setup and manage virtual world activities. We present the inverse approach, enabling educators to setup and manage virtual world activities using the traditional LMS Web interface as an integral part of the overall educational activities of a course. In our approach, the LMS enables the teacher/trainer to setup, control, track, and store virtual world activities and its elements. It is the result of a joint effort by academic and corporate teams, implemented in the Formare LMS for OpenSimulator and Second Life Grid virtual world platforms. We explain how the Multis architecture can be used for integration, with concrete cases, an approach that can be implemented in other LMS and virtual world platforms, to overcome the limitations of existing systems for organizational management of e-learning activities.

Abstract
This paper presents an innovative approach to solve the product-launch planning problem in the pharmaceutical industry, with uncertainty on the product demand and on clinical trials. A mixed integer linear programming (MILP) model, incorporating Monte Carlo simulation (MCS), was developed for optimizing the process design (process-unit allocation and scale-up decisions) and for capacity planning (acquisition of new units), considering the products that still require development, and the products that are already in commercialization. MCS is performed in a two-step procedure, based on Normal and Bernoulli distributions, in order to capture the effects of demand variability and trials pass-fail uncertainty, respectively. Product-launch decisions are made taking into account the probability distributions of alternative process designs, of new capacity requirements, and of the coefficients of the objective function. The applicability of the proposed solution approach is demonstrated in an illustrative case study.

Abstract
In nowadays competing market, companies are constantly challenged to reduce the lead time for new products design process by diminishing the time response that goes from the arise of the market opportunity to the satisfaction of the customer need. Simultaneously, companies and networked organizations face a growing number of product configurations, lower product volumes, a continuously growing appetence for personalized products, pressing the decision makers into adopting more efficient product development processes. The present paper addresses the main collaborative product development issues by proposing a responsive and efficient use of knowledge on networked environments through a lean-based framework for collaborative networks. In particular, this work describes the main lean concepts and tools that are enhancers of an efficient and distributed customizable product development process in networked environment.