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.

Abstract

Abstract
Given a log L, a control-flow discovery algorithm f, and a quality metric m, this paper faces the following problem: what are the parameters in f that mostly influence its application in terms of m when applied to L? This paper proposes a method to face this problem, based on sensitivity analysis, a theory which has been successfully applied in other areas. Clearly, a satisfactory solution to this problem will be crucial to bridge the gap between process discovery algorithms and final users. Additionally, recommendation techniques and meta-techniques like determining the representational bias of an algorithm may benefit from solutions to the problem considered in this paper. The method has been evaluated over a set of logs and two different miners: the inductive miner and the flexible heuristic miner, and the experimental results witness the applicability of the general framework described in this paper.