Abstract
Production scheduling is a function that can contribute strongly to the competitive capacity of companies producing goods and services. Failure to stagger tasks properly causes enormous waste of time and resources, with a clear decrease in productivity and high monetary losses. The efficient use of internal resources in organizations becomes a competitive advantage and can thus dictate their survival and sustainability. In that sense, it becomes crucial to analyze and develop production scheduling models, which can be simplified as the function of affecting tasks to means of production over time. This report is part of a project to develop a dynamic scheduling tool for decision support in a single machine environment. The system created has the ability, after a first solution has been generated, to trigger a new solution as some tasks leave the system and new ones arrive, allowing the user, at each instant of time, to determine new scheduling solutions, in order to minimize a certain measure of performance. The proposed tool was validated in an in-depth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model.

Abstract
Nowadays, companies are facing a constant need to develop and increase coordination between operational functions to respond rapidly and accurately to customer requests. Linked with this need, an increasing number of practitioners are resorting to an established and integrated business management methodology, the Sales and Operations Planning (S&OP). The concept of S&OP has gained increased recognition over the years by several authors and companies. This project describes the S&OP implementation in Sogrape Vinhos (wines) S.A., a Portuguese wine producer and distributer. The company was confronted with low accuracy in the establishing the forecast demand plans, especially on a long-term horizon. In order to increase the demand plans accuracy, the company started a S&OP implementation program. This paper describes the company's current planning process, explains the S&OP's implementation model presenting the selected parameters adequate to the company's context, and finally, evaluate the expected outcomes of this project. Preliminary results from the S&OP implementation project at Sogrape indicate significant savings at the operational level and greater effectiveness in developing the company's demand plans. (C) 2019 The Authors. Published by Elsevier Ltd.

Abstract
Companies that faces transport challenges and require logistics efficiency need to implement solutions based on automation, provided in Industry 4.0. Intelligent Logistics Management, satisfying the demanding requirements of the customer, using new technologies and advanced tools to control and to act along the supply chain are mandatory. To address this topic, this paper presents an application of an Automated Intelligent Vehicle.

Abstract
This project aimed to analyze the practical difficulties in storing and moving a high range of products making the process easier and more efficient. The objectives were to improve the use of available space in the raw materials warehouse and ensure the effectiveness, efficiency and reduction of the periods between the transportation from raw materials to production lines. The work developed focused on improving internal processes, storage practices and internal transportation. In fact, for the production flow to be continuous, the supply must be aligned with the production to ensure that there is no failure of materials.

Abstract
In this paper, we propose a branch-and-price approach for solving the problem of maximizing the expected number of transplants in Kidney Exchange Programs (KEPs). In these programs, the decision on which transplants will be conducted is usually made with the support of optimization models with the assumption that all operations will take place. However, after a plan of transplants is defined, a pair may leave the KEP or a more accurate compatibility evaluation exam may invalidate a transplant. To model these possible events we consider probabilities of failure of vertices and of arcs and the objective of maximizing the expected number of transplants. The proposed approach is based on the so-called cycle formulation, where decision variables are associated with cycles. Built on the concept of type of cycle a branch-and-price algorithm is conceived. One subproblem is defined for each type of cycle. We present computational results of the proposed branch-and-price algorithm and compare them with solving directly the cycle formulation (with a general purpose mixed integer programming solverCPLEX) showing that the proposed approach is the only one suitable for larger instances.

Abstract
This paper proposes the linear frequency cepstral coefficients as highly discriminative features for anomaly detection in ball bearings using vibration sensor data. These features are based on cepstral analysis and are capable of encoding the patterns of a spectral magnitude profile. Incipient damages on bearings can grow rapidly under normal use resulting in vibration and harsh noise. If left undetected, this damage will worsen, leading to high maintenance costs or even injury. Multiple interferences in an industrial environment contaminate the signal, making it a challenge to correctly identify the bearings' condition. Many studies have attempted to overcome this issue at the signal level. However, the discriminative capacity of the current vibration signal features is still vulnerable to interference, which motivates this work. In order to demonstrate the benefits of these features, we (1) show that they are computationally efficient and suitable for real-time incremental training; (2) conduct discriminative analysis by evaluating the separability performance and comparing it with the state of the art; and (3) test the robustness of the proposed features under noise interference, which is ideal for use in the harsh operating conditions of industrial machinery. The data was obtained from a laboratory workbench setting that reproduces bearing fault scenarios. Results show that the proposed features are fast, competitive when compared to state-of-the-art features, and resilient to high levels of interference. Despite the higher performance when using the quadratic model, the proposed features remain highly discriminative when used with several other discriminant function.