Abstract
The hierarchical planning concept is commonly used for production planning. Dividing the planning process into subprocesses which are solved separately in the order of the hierarchy decreases the complexity and fits the common organizational structure. However, interaction between planning levels is crucial to avoid infeasibility and inconsistency of plans. Furthermore, optimizing subproblems often leads to suboptimal results for the overall problem. The alternative, a monolithic model integrating all planning levels, has been rejected in the literature because of several reasons. In this study, we show that some of them do not hold for an integrated production planning model combining the planning tasks usually attributed to aggregate production planning and master production scheduling. Therefore, we develop a hierarchical and an integrated model considering both levels, aggregate production planning and master production scheduling. Computational tests show that it is possible to solve the integrated model and that it outperforms the hierarchical approach for all instances. Moreover, an indication is given why and when integration is beneficial.

Abstract
This paper aims to present, define and structure the car rental fleet management problem, which includes operational fleet management issues and problems traditionally studied under the revenue management framework. The car rental business has challenging and distinctive characteristics, which are mainly related with fleet and decision-making flexibility, and that render this problem relevant for academic research and practical applications. Three main contributions are presented: an in-depth literature review and discussion on car rental fleet and revenue management issues, a novel integrating conceptual framework for this problem, and the identification of research directions for the future development of the field.

Abstract

Abstract
This paper develops a decision support tool that can help fishery authorities to forecast bivalve landings for the dredge fleet accounting for several contextual conditions. These include weather conditions, phytotoxins episodes, stock-biomass indicators per species and tourism levels. Vessel characteristics and fishing effort are also taken into account for the estimation of landings. The relationship between these factors and monthly quantities landed per vessel is explored using multiple linear regression models and data mining techniques (random forests, support vector machines and neural networks). The models are specified for different regions in the Portugal mainland (Northwest, Southwest and South) using six years of data 2010-2015). Results showed that the impact of the contextual factors varies between regions and also depends on the vessels target species. The data mining techniques, namely the random forests, proved to be a robust decision support tool in this context, outperforming the predictive performance of the most popular technique used in this context, i.e. linear regression.

Abstract
In a high-volume production environment it is particularly important to ensure that key process variables and parameters are within the specification limits. Often, adjustments and changes to the process are required to ensure all applicable quality requirements. In most cases, problems are not caused by an isolated factor. In fact, they are the result of interactions between several factors, including quality ingredients, parameter settings and other processing conditions. In the biscuit manufacturing industry, the thickness and weight of the biscuit are two of the most important product's quality characteristics. Regardless of the product purchased by the customer, the declared weight is considered a specification, so any deviation of these characteristics from their nominal values leads to a change in the weight of the packages. In this paper, we explore a six-sigma approach in the improvement of an industrial biscuit production.

Abstract
Public transport networks were, in the past, mainly designed to maximize the efficiency of commuting trips. However, with such perspective there are considerable risks to marginalize some specific population groups (e.g. disabled, elderly, children, pregnant, people in poverty). For enhancing social inclusion and improving the accessibility of more vulnerable citizens, such networks are often redesigned and adjusted. Nevertheless, even with such adjustments, it is sometimes difficult to provide efficient services that fully address the real needs and capabilities of travelers, partially because of the failure in following the fast technological and demanding changes of modern societies. Taking in mind these challenges, we have developed a conceptual model to support knowledge sharing and decision-making in urban mobility, and to improve the way travel information is addressed. The multi-user integrated platform proposed in this work is supported by the idea that information from different channels must be centralized, organized, managed and properly distributed. This idea is grounded in two main principles: (i) past and real-time information from a wide range of sources is combined for knowledge extraction, and such knowledge is going to be used not only to allow travelers to better plan their trips, but also to help transport providers to develop services adapted to the needs and preferences of their customers; and (ii) information is provided in a personalized way taking into account socio-economical differences between groups of travelers. (C) 2017 The Authors. Published by Elsevier B.V.