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
This paper introduces a mathematical model (together with a relaxed version) and solution approaches for the multi-facility glass container production planning (MF-GCPP) problem. The glass container industry covers the production of glass packaging (bottle and jars), where a glass paste is continuously distributed to a set of parallel molding machines that shape the finished products. Each facility has a set of furnaces where the glass paste is produced in order to meet the demand. Furthermore, final product transfers between facilities are allowed to face demand. The objectives include meeting demand, minimizing inventory investment and transportation costs, as well as maximizing the utilization of the production facilities. A novel mixed integer programming formulation is introduced for MF-GCPP and solution approaches applying heuristics and meta-heuristics based on mathematical programming are developed. A multi-population genetic algorithm defines for each individual the partitions of the search space to be optimized by the MIP solver. A variant of the fix-and-optimize improvement heuristic is also introduced. The computational tests are carried on instances generated from real-world data provided by a glass container company. The results show that the proposed methods return competitive results for smaller instances, comparing to an exact solver method. In larger instances, the proposed methods are able to return high quality solutions.

Abstract
The scarcity of federal resources allocated to the Ministry of Defense causes the need for Brazilian Armed Forces to implement strategies that may allow achieving "more with less". For the Navy of Brazil, this strategy includes the need to improve and implement risk management tools and techniques in large defense projects. In this sense, the present study aims at contributing to this process through the design and validation of a synthetic risk model for military shipbuilding, containing the main risk events, causes and associated effects that may affect the success of these projects, in terms of costs and deadlines. This model will help in the prediction and control of these variables and their possible effects, enabling the anticipation of responses and the reduction of uncertainty. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract
This paper describes the process of formulation and validation of a synthetic risk network model for the Brazilian military shipbuilding industry. This model is composed of risk events and their potential causes that will be the basis of the process of identification and analysis of future risks on similar projects. This process involved an extensive literature review to collect and list an initial catalogue of risks, causes and related effects, followed by an analysis to summarise and schematically group the risks. Then, the Delphi method with a panel of experts from the Brazilian Navy was used to analyse and assess the proposed model.

Abstract
Large forest fires are notorious for their environmental and socio-economic impacts and are assigned a disproportionately high percentage of the fire management budget. This study addresses extremely large fires (ELF, C2500 ha) in Portugal (2003-2013). We analysed the effect of fire-suppression force variation on ELF duration, size and growth rate, versus the effect of the concomitant fire environment (namely fuel and weather) conditions. ELF occurred in highly flammable landscapes and typically were impelled by extreme fire weather conditions. Allocation of suppression resources (normalized per unit of burned area or perimeter length) was disparate among fires, suggesting inadequate incident management. Fire-suppression effort did not affect time to containment modelled by survival analysis. Regression tree analysis indicated ELF spread to be negatively affected by higher fire-suppression resourcing, less severe fire weather, lower time to containment and higher presence of <9-year-old fuels, by decreasing order of importance; regional variability was relevant. Fire environment-to-fire suppression ratios of influence were 3: 1 for fire size and 1: 1 for fire growth rate, respectively, explaining 76 and 60 % of the existing variability. Results highlight the opportunistic nature of large-fire containment. To minimize the area burned by ELF, management and operational improvements leading to faster containment are recommended, rather than higher fire-suppression resourcing; more effective identification and exploration of containment opportunities are preferable to the accumulation of suppression resources.

Abstract
Today, the variety of complex products, low volume and decreasing life cycles require a combination of multiple skills that, often, do not exist in a single organization. This raises the need to extend the traditional organization towards the extended virtual enterprise. During the last decade several research projects developed concepts, methods and tools to support the design and operation of the virtual enterprises. However, the impact in industry remains low mainly due to the lack of vertical and horizontal integration, both at business and technical level. Industry 4.0 may be the missing enabler for effective virtual enterprises, once it integrates both business entities and technical entities into a single concept - the Industry 4.0 component - thus enabling enhanced interoperability. This paper presents innovative Industry 4.0 approaches, concepts, methods and tools applied to real manufacturing environments, showing how they enable the creation of cyber-physical production systems leading to a flexible, efficient and seamlessly virtual enterprise.