Abstract
The fast-moving consumer goods sector relies on economies of scale. However, its assortments have been overextended as a means of market share appropriation and top-line growth. This paper studies the se-lection of the optimal set of products for fast-moving consumer goods producers to offer, as there is no previous model for product line selection that satisfies the requirements of the sector. Our mixed -integer programming model combines a multi-category attraction model with a capacitated lot-sizing problem, shared setups and safety stock. The multi-category attraction model predicts how the demand for each product responds to changes within the assortment. The capacitated lot-sizing problem allows us to account for the indirect production costs associated with different assortments. As seasonality is prevalent in consumer goods sales, the production plan optimally weights the trade-off between stocking finished goods from a long run with performing shorter runs with additional setups. Finally, the safety stock extension addresses the effect of the demand uncertainty associated with each assortment. With the computational experiments, we assess the value of our approach using data based on a real case. Our findings suggest that the benefits of a tailored approach are at their highest in scenarios typical fast-moving consumer goods industry: when capacity is tight, demand exhibits seasonal patterns and high service levels are required. This also occurs when the firm has a strong competitive position and consumer price-sensitivity is low. By testing the approach in two real-world instances, we show that this decision should not be made based on the current myopic industry practices. Lastly, our approach obtains profits of up to 9.4% higher than the current state-of-the-art models for product line selection.

Abstract
When making long-term plans for their asset portfolios, decision-makers have to define a priori a maintenance budget that is to be shared among the several assets and managed throughout the planning period. During the planning period, the a priori budget is then allocated by managers to different operation and maintenance interventions ensuring the overall performance of the system. Because asset degradation is stochastic, a considerable amount of uncertainty is associated with this problem. Hence, to define a robust budget, it is essential to account for several degradation scenarios pertaining to the individual condition of each asset. This paper presents a novel mathematical formulation to tackle this problem in a heterogeneous multiasset portfolio. The proposed mathematical model was formulated as a mixed-integer programming two-stage stochastic optimization model with mean-variance constraints to minimize the number of scenarios with an insufficient budget. A Gamma process was used to model the condition of each individual asset while taking into consideration different technological features and operating conditions. We compared the solutions obtained with our model to alternative practices in a set of generated instances covering different types of multi-asset portfolios. This comparison allowed us to explore the value of modeling uncertainty and how it affects the generated solutions. The proposed approach led to gains in performance of up to 50% depending on the level of uncertainty. Furthermore, the model was validated using real-world data from a utility company working with portfolios of power transformers. The results obtained showed that the company could reduce costs by as much as 40%. Further conclusions showed that the cost-saving potential was higher in asset portfolios in worse condition and that defining a priori operation and maintenance interventions led to worse results. Finally, the results showcased how different decision-maker risk-levels affect the value of taking uncertainty into account.

Abstract
Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.

Abstract
The injection mold is a high precision tool responsible for the production of most plastic parts used everywhere. Its design is considered critically important for the quality of the product and efficient processing, as well as determinant for the economics of the entire injection molding process. However, typically, no formal engineering analysis is carried out during the mold design stage. In fact, traditionally, designers rely on their skills and intuition, following a set of general guidelines. This does not ensure that the final mold design is acceptable or the best option. At the same time, mold makers are now highly pressured to shorten both leading times and cost, as well as to accomplish higher levels of mold performance. For these reasons, it is imperative to adopt new methods and tools that allow for faster and higher integrated mold design. To that end, a new global approach, based on the integration of well-known quantitative techniques, such as Design for Six Sigma (DFSS), Structural Equation Modeling (SEM), Axiomatic Design (AD) and Multidisciplinary Design Optimization (MDO) is presented. Although some of these methods have been largely explored, individually or in combination with other methodologies, a quantitative integration of all aspects of design, in such a way that the whole process becomes logical and comprehensible, has not yet been considered. To that end, the DFSS methodology, through its IDOV roadmap, was adopted. It is based on the ICOV Yang and El-Haik proposal, establishing four stages for the design process: Identify, which aims to define customers’ requirements/expectations; Design, where the creation of a product concept, and its system-level design, is performed; Optimization, in which all the detailed design, through product optimization, is handled; and finally, Validation, where all product design decisions are validated, in order to verify if the new designed entity indeed meets customer and other requirements. As a result, this approach tackles the design of an injection mold in a global and quantitative approach, starting with a full understanding of customer requirements and converting them into optimal mold solutions. In order to validate it, an integrated platform was developed, where all different analysis modules were inserted and optimized through an overseeing code system. The results attained highlight the great potential of the proposed framework to achieve mold design improvements, with consequent reduction of rework and time savings for the entire mold design process. © 2011 by Nova Science Publishers, Inc.

Abstract
In their quest for sustainability, cities design and deploy smart mobility solutions aiming to improve the efficiency and management of transportation systems and to provide better services to citizens. Those solutions are often based on Information and Communication Technologies (ICT) and on digital services, but their maintenance and management are a greater challenge than their implementation. Problems can be difficult to identify since they can be exogenous or endogenous to the service provider. Usually, in their effort to maintain good service levels, companies implement complex and expensive information systems that use sensors to monitor infrastructure and hardware but ignore other sources of valuable information. In a digitalized world, customers easily report problems that are a cause of lower quality of service and worse user experience. However, for several reasons, service providers do not always pay due attention to these complaints. As communication channels are already open, we claim that customer participation through these reports can be used to significantly enhance the delivery and quality of mobility services. In this work we propose a methodology that takes advantage of customers’ participation in the maintenance and management of smart city solutions. With this methodology, we aim to redesign the process of customer interaction with service providers in order to improve the overall efficiency and the service experience. Our research is based on a case study from a public transport service in the metropolitan area of Porto, in Portugal. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Origin-destination matrices help understand the movement of people within cities. This work is built upon the premise that stakeholders, e.g. decision makers, need to analyze mobility flows from spatio-temporal perspectives that are appropriate to their context of analysis. The data retrieved from sensors and Intelligent Transportation Systems are useful for this purpose due to their lower acquisition costs and fine granularity, although it is complex to use such data in an integrated way, as they might have heterogeneous representations of spatio-temporal attributes and granularities. Most of the related works on the analysis of OD flows consider matrices with a fixed spatio-temporal aggregation level, and do not explore the intrinsic issue of data heterogeneity. Herein we report our findings on building the semantic foundation of knowledge-assisted visualization tools for analyzing OD matrices from multiple stakeholder levels. We propose a set of ontology design patterns for modeling the semantics of OD data, and the relations between the spatio-temporal constructs that stakeholders ought to choose when visualizing urban mobility flows. Our approach aims to be reusable by researchers and practitioners. We describe a practical implementation using estimated flows from smart card data from Porto, Portugal.