Abstract
The progress in medical science and the decline of altruistic behavior of couples introduced to the world the ageing problem. The existence of ageing is more experienced by developed countries. Researchers and policy makers are constantly trying to find ways to study the impacts of ageing since the issue is unprecedented in our history. However, the majority of the literature focus more on immediate mechanisms such as public expenditures and somehow neglected the influence of ageing on manufacturing sector. Thus, through panel data, we studied the influence of ageing on manufacturing sectors. The empirical study was carried out on six developed countries namely Japan, Germany, Italy, Greece, Finland and Portugal that have high ageing population. Our results suggest that the growth of the old age group over 65 years old will have significantly negative influence on percentage contribution of manufacturing to the GDP of these countries. Moreover, the results also demonstrate that a country with a higher proportion of old age group over working group will face fall in the manufacturing.

Abstract
Aggressive marketing causes rapid changes in consumer behavior and some significant impact in the retail business. In this context, the sales forecasting at the SKU level can help retailers to become more competitive by reducing inventory investment and distribution costs. Sales forecasts are often obtained combining basic univariate forecasting models with empirical judgment. However, more effective forecasting methods can be obtained by incorporating promotional information, including price, percentage of discount (direct discount or loyalty card discount), calendar events and weekend indicators not only from the focal product but also from its competitors. To deal with the high dimensionality of the variable space, we propose a two-stage LASSO regression to select optimal predictors and estimate the model parameters. At the first stage, only focal SKUs promotional explanatory variables are included in the Autoregressive Distributed Lag model. At the second stage, the in-sample forecast errors from the first stage are regressed on the explanatory variables from the other SKUs in the same category with the focal SKU, and to use that information more effectively three different approaches were considered: select the five top sales SKUs, include all raw promotional information, and preprocess raw information using Principal Component Analysis. The empirical results obtained using daily data from a Portuguese retailer show that the inclusion of promotional information from SKUs in the same category may improve the forecast accuracy and that better overall forecasting results may be obtained if the best model for each SKU is selected.

Abstract
Sales forecasts gained more importance in the retail industry with the increasing of promotional activity, not only because of the considerable portion of products under promotion but also due to the existence of promotional activities, which boost product sales and make forecasts more difficult to obtain. This study is performed with real data from a Portuguese consumer goods retail company, from January 2012 until April 2015. To achieve the purpose of the study, dynamic regression is used based on information of the focal product and its competitors, with seasonality modelled using Fourier terms. The selection of variables to be included in the model is done based on the lowest value of AIC in the train period. The forecasts are obtained for a test period of 30 weeks. The forecasting models overall performance is analyzed for the full period and for the periods with and without promotions. The results show that our proposed dynamic regression models with price and promotional information of the focal product generate substantially more accurate forecasts than pure time series models for all periods studied.

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
Increasing demand for containerization compels container terminals to improve their performance. Uncoordinated scheduling of operations is one of the main factors accounting for poor performance at automated container terminals (ACTs). To increase land utilization efficiency and lower operational times, a new storage system called the split-platform automated storage/retrieval system (SP-AS/RS) has been introduced for temporary storage of containers. This paper describes a multi-objective mixed-integer programming (MIP) model that is based on a combination of multiple interacting sub-tasks. It is aimed at optimizing the integrated scheduling of handling and storage operations in ACTs. The MIP model objective function is to minimize delays in the loading/unloading tasks of the cranes and the travel time of vehicles and platforms in the SP-AS/RS. At the same time, a simulated annealing algorithm (SAA) that provides near-optimal solutions for the problem in a reasonable computation time is appraised. The results of this study show that the objective function of the MIP model is, on average, 58 % lower than that of the non-integrated scheduling method. On the other hand, the best objective function values obtained by the SAA indicate only a 3.7 % disadvantage in comparison with optimal values determined by the MIP model, demonstrating that the SAA is able to provide near-optimal solutions for the integrated scheduling of handling and storage operations.