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
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.

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Abstract
Cutting and packing (C&P) is an important area of operational research and its problems arise in various industries such as: textile, wood, glass and shipbuilding. The main objective is to maximize the efficiency of a layout by rearranging and/or reassigning items inside containers in order to reduce costs and environmental impact. In this work, a raster solution to the bidimensional irregular strip packing problem, which consists of placing irregular shapes items inside a single rectangular container with variable length, is studied. In raster methods, the selection of the grid size is very important to the outcome of the algorithm. It influences the size of the search space, the overlap algorithm efficiency, as well as the memory requirements of the packing algorithm. An analysis of the impact of the choice of grid size is performed using 15 benchmark cases from the literature and, through careful observation of such test results, a simple rule to define the grid size is suggested.

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.