Abstract
This study explores strategic agility of an automotive corporate group and its influence on facility layouts and operational performance. Strategic agility is viewed here as a firm's strategic intent to achieve agile operations through collaboratively deploying the layouts of a set of facilities, driven by a management focus on improving its responsiveness and adaptability to customers' requirements. Our "collaborative multi-facility layout problem" involves the physical organization of departments between and inside several facilities geographically dispersed, that collaborate in manufacturing a complex product in a given time window. The model proposed in this work allows us to analyse the benefits of new horizontal collaboration forms with respect to several objectives, namely costs (material handling inside and between facilities, re-layout) and adjacency between departments. A case study of a first tier supplier in the automotive industry shows the applicability potential of the approach to real-life problems. The results show that horizontal collaboration among the facilities can positively influence the performance of the corporate group as a whole, and that of each firm individually.

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.

Abstract

Abstract

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.