This paper discusses horizontal cooperation in road transportation and supports the relevance of this praxis as a way of reducing delivery costs and greenhouse gas emissions. In a competitive market, costs reduction due to economies of scale in short and long run constitutes a key issue for small and medium enterprises. The paper reviews the existing literature on the topic and then examines different scenarios in order to quantify the savings in route costs that can be attained throughout horizontal cooperation. This numerical analysis is based on a set of well-known benchmarks for the Multidepot Vehicle Routing Problem, which has been adapted to illustrate a realistic but seldom considered example of horizontal cooperation. An Iterated Local Search algorithm is proposed to obtain high-quality solutions for this collaborative scenario, while noncollaborative scenarios are solved using a well-tested algorithm for the Capacitated Vehicle Routing Problem. The savings in routing costs-both regarding distance-based and environmental costs-are computed taking into account different geographical distributions of customers with respect to their assigned service providers.

The present work develops an accurate prediction model of the COVID-19 pandemic, capable not only of fitting data with a high regression coefficient but also to predict the overall infections and the infection peak day as well. The model is based on the Verhulst equation, which has been used to fit the data of the COVID-19 spread in China, Italy, and Spain. This model has been used to predict both the infection peak day, and the total infected people in Italy and Spain. With this prediction model, the overall infections, the infection peak, and date can accurately be predicted one week before they occur. According to the study, the infection peak took place on 23 March in Italy, and on 29 March in Spain. Moreover, the influence of the total and partial lockdowns has been studied, without finding any meaningful difference in the disease spread. However, the infected population, and the rate of new infections at the start of the lockdown, seem to play an important role in the infection spread. The developed model is not only an important tool to predict the disease spread, but also gives some significant clues about the main factors that affect to the COVID-19 spread, and quantifies the effects of partial and total lockdowns as well.

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Machining processes, including turning, are a critical capability for discrete part production. One limitation to high material removal rates and reduced cost in these processes is chatter, or unstable spindle speed-chip width combinations that exhibit a self-excited vibration. In this paper, an artificial neural network (ANN)—a data learning model—is applied to model turning stability. The novel approach is to use a physics-based process model—the analytical stability limit—to generate a (synthetic) data set that trains the ANN. This enables the process physics to be combined with data learning in a hybrid approach. As anticipated, it is observed that the number and distribution of training points influences the ability of the ANN model to capture the smaller, more closely spaced lobes that occur at lower spindle speeds. Overall, the ANN is successful (>90% accuracy) at predicting the stability behavior after appropriate training.