Abstract Electric vehicle routing problems (EVRPs) involve the routing of a fleet of electric vehicles (EVs) to visit a set of customers while typically minimizing the total travel and charging time. Due to their limited autonomy, EVs may need to recharge their batteries en‐route at charging stations (CSs). Thus, routing decisions also include which CSs to visit, and how much energy to charge during those visits. These decisions are compounded by the fact that charging times follow a nonlinear charging function with respect to the EV's state of charge (SoC). We propose a layered graph representation for the EVRP with nonlinear charging functions (EVRP‐NL). Specifically, the layers correspond to discretized SoC values. Therefore, the arcs' energy consumption is approximated to match those values. We develop two compact formulations based on the layered graph. Furthermore, we introduced two charging policies that facilitate aligning charging duration with practical considerations. Computational results demonstrate the effectiveness of our formulations. Our best formulation effectively handles instances with up to 40 customers. On those instances, compared to the state‐of‐the‐art compact formulation, our formulation solves 13 more instances to optimality with less than half of the computational time. Considering instances solved by both formulations to optimally, the approximation entailed by our formulation yields a 0.94% deviation on average. Since our best performing formulation is compact, it may be readily used by a broad audience. Furthermore, as the majority of algorithms for the EVPR and its variants are heuristics, our formulation could be beneficial in evaluating the performance of these methods.

Distributed ledger technologies are transforming existing business models and business relationships. In particular, blockchain allows non-trusting parties to manage a shared database in a decentralized way and improve the transparency, authenticity, and reliability of the exchanged data. Nonetheless, decentralized paradigms are not yet well established, resulting in only a fraction of blockchain-based applications being successful in the long term.In this paper, we present a blockchain-based solution for the electric vehicle supply chain that we designed in the context of the CONCORDIA project of the European Cybersecurity Competence Network. We describe the goals, the value proposition, the main design choices, and the architecture of our system. Moreover, we discuss the electric vehicle supply chain, analyzing the improvements and limitations introduced by our blockchain-based solution. We analyze our solution from the managerial and technical points of view through a lean business methodology for blockchain solutions. In particular, we developed an economic impact assessment to evaluate the potential costs and revenues of the application of blockchain technology in a supply chain context. Although the blockchain system is inspired by the supply chain of a multinational automotive company, it can be applied to any other multi-actor supply chain.

In this study, we explore the integration of machine learning algorithms into a decision support system for climate finance, focusing on the impact of rainfall on wineries in Italy. Wineries are particularly vulnerable to climate change, and accurate rainfall forecasting is critical to their success; lack of rain can reduce the quantity and quality of grapes, while flooding can damage vineyards. We identify relevant weather characteristics that cause rainfall and predict quarterly rainfall intensity using machine learning techniques. The dataset was collected from the agrometeorological office of the Piedmont region in Italy to measure the performance of three machine learning techniques (Multivariate Linear Regression, Random Forest, and Neural Network). Mean square error and mean absolute error methods were used to measure the performance of the machine learning models. A comparative analysis between precipitation estimation models based on conventional machine learning algorithms and deep learning architectures with models based on Long Short-Term Memory (LSTM) networks is performed. It shows how the Random Forest algorithm presents the best performances, both in the accuracy and explainability of the predictions. Our study contributes to the climate finance literature by showing how machine learning can support decision-makers in managing climate risks in the food chain, specifically in the wine industry in Italy.

This paper addresses a scheduling problem with parallel identical machines and sequence-dependent setup times in which the setup and the processing times are random parameters. The model aims at minimizing the total completion time while the total revenue gained by the processed jobs satisfies the manufacturer’s threshold. To handle the uncertainty of random parameters, we adopt a risk-averse distributionally robust approach developed based on the Conditional Value-at-Risk measure hedging against the worst-case performance. The proposed model is tested via extensive experimental results performed on a set of benchmark instances. We also show the efficiency of the deterministic counterpart of our model, in comparison with the state-of-the-art model proposed for a similar problem in a deterministic context.