The rapid growth of e-commerce has increased the complexity of supply chain management, particularly in urban logistics where efficiency and sustainability are critical concerns. In response, this study proposes a selection hyper-heuristic framework for time-dependent green logistics, incorporating key factors such as economic costs, carbon emissions, rider types, real-time traffic conditions, and time-window constraints. To address the complexities introduced by these real-world factors, we design a two-layer distribution model with crowdsourced delivery that covers the flow from city distribution centers to regional hubs and ultimately to end customers. The first layer involves location selection and the delivery process, while the second layer focuses on order allocation and last-mile delivery. For the location selection and order allocation problems, exact optimization models are developed to obtain high-quality solutions. In the delivery process, we integrate Deep Reinforcement Learning to replace the traditional adaptive layer of the Adaptive Large Neighborhood Search algorithm, enabling dynamic and intelligent adjustments during the search. Comparative analyses against existing and traditional methods across various benchmark instances demonstrate the superior efficiency and solution quality of the proposed framework. Simulation experiments based on a real-world road network in China validate the effectiveness of the proposed framework. In addition, the well-trained model can be directly applied to various scenarios, highlighting its strong generalization capability.

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.

Evaluating risks against IT Systems is a complex yet crucial process that requires significant resources and competencies. This paper proposes RiskMan, an expert system for the automatic assessment of cyber risks that computes a risk score using information gathering and vulnerability assessment tools, public databases, and leaks from the dark web without involving cybersecurity experts. Moreover, RiskMan uses AI-driven techniques to determine risks also when only partial information is available.

In the maritime sector, 5G enables innovative applications and services to improve efficiency, security, safety and optimise operations. In this context, it is interesting to identify the 5G-enabled applications that foster technological development in the maritime sector. We deploy the survey’s results performed in the context of the European project 5G-LOGINNOV and combine them with Twitter data. Thanks to words frequency and sentiment analysis, we found that in the maritime sector, there are several 5G technological enablers related to real-time information transfer. Furthermore, 5G-enabled applications for cybersecurity, are the most promising technologies.

Blockchain is a technology that creates trust among non-trusting parties without relying on any intermediaries. Consequently, it has attracted the interest of companies operating in a multitude of sectors. However, due to the number of different blockchain solutions that have emerged in the last few years and their rapid changes, it is challenging for such companies to orient their technological decisions. This paper presents a comparative analysis of the key dimensions—namely, governance, maturity, support, latency, privacy, interoperability, flexibility, efficiency, resiliency, and scalability—of some of the most-used permissioned blockchain platforms. Moreover, we present the results of a performance evaluation considering the following frameworks: Hyperledger Fabric 2.2, Hyperledger Sawtooth 1.2, and ConsenSys Quorum 21.1 (with both the GoQuorum client and the Hyperledger Besu client). The platforms were tested under similar conditions, and official releases were used, such that our findings provide a reference for companies establishing their technological orientation.

Logistics 4.0 is a revolution based on information sharing and digitalization. Thus, Logistics 4.0 leads to the generation of huge amounts of data in short periods, and the data bloating problem must be addressed. One possible solution is the interplanetary file system (IPFS), which guarantees data replication and availability while limiting the storage of overlapping data. This study is the first literature review on IPFS and focuses on its application to the logistic sector. The main findings of this study are: the topic is gaining interest, but the solutions proposed in the literature were still in the early stages; IPFS was always coupled with the blockchain technology, and all of the authors used similar strategies to integrate them; the authors identified many advantages in the use of IPFS, but did not analyze in-depth the related disadvantages.

Since the global request for freight transportation is increasing as a consequence of the increasing requirements of the modern economy, logistics processes need to be optimized through the application of innovative technologies, to ensure a high level of quality, flexibility, and effectiveness in logistics operations. The adoption of innovative technologies allows the creation and development of new products and services, able to optimize the existing logistics processes and create value. In particular, one of the most promising technology for logistics applications is the 5G communication network that allows, together with companion technologies such as the Internet of Things, Artificial Intelligence, and the Cloud, the collection, integration, and sharing of a large amount of data from different sources. However, to ensure the market adoption of innovative products and services, the different actors and stakeholders of the logistics chain must be involved from the early stages of the development. This allows them to keep into account their actual needs in the development process of the business models and for the future exploitation of the solutions. This paper analyzes the process of development of collaborative business models in the context of 5G-LOGINNOV, a project aimed at the development of 5G-based solutions to optimize the logistics operations in ports and retro-ports.

This paper investigates a dynamic and stochastic vehicle routing problem with time windows that considers the use of multiple delivery options and crowd drivers, reflecting the synchromodality in the urban context. We propose a multi-stage stochastic model, and we solve the problem by using a simulation-optimization strategy. It relies on a Monte Carlo simulation and a large neighborhood search (LNS) heuristic for optimization. We conduct a case study in the medium-sized city of Turin (Italy) to measure the potential impact of integrating cargo bikes and crowd drivers in parcel delivery. Experimental results show that combining crowd drivers and green carriers with the traditional van to manage the parcel delivery is beneficial in terms of economic and environmental cost-saving, while the operational efficiency decreases. Besides, the green carriers and crowd drivers are promising delivery options to deal with online customer requests in the context of stochastic and dynamic parcel delivery. The resulting set of policies are part of the outcomes of the Logistics and Mobility Plan 2019–2021 in the Piedmont region.

Smart home is increasing in popularity within the IoT applications. This thanks to its ability to change house equipment into being more intelligent and interconnected and thus, delivering a connected life experience to the user. This paper investigates the literature and state-of-the-art on the smart home. Then, the paper presents a real case study that describes how smart home concepts have been applied to a heritage building, raised in 1922 and located in the City of Turin (Italy).

The recent technological progress has started a revolution in the logistic and supply chain environment, known as Logistics 4.0. Such a revolution is strongly based on information sharing and digitalization. For this reason, distributed ledger technologies (and blockchain in particular) are attracting the interest of countries and companies. However, the proper verification of data coming from oracles is a huge issue. This paper proposes a solution integrating Blockchain and IoT, by exploiting the enhanced data security and integrity provided by Narrowband-IoT. A preliminary performance evaluation of Hyperledger Fabric and Hyperladger Sawtooth is also presented.

Tactical Capacity Planning (TCP) is becoming a crucial part of logistics in the current environment of demand-driven economics. This paper proposes an innovative approach in the TCP setting, consisting of using the collected historical data of the geographical position and the volume of the orders to plan the capacity requirements for the next day. To this end, the clustering of the city to microzones is introduced using K-means clustering. Then, four different methods (Gaussian Process regression, ARIMA model, Neural Network regression, and Long Short Term Memory network) are used to forecast the next day order volume for each of the clusters. Finally, the Variable Cost and Size Bin Packing problem solved with the predicted demand to outline the usage of a heterogeneous fleet required to serve the next time period. Through experiments on the real data, we conclude, that the proposed algorithm is satisfying the decision safety framework with completely unknown demand and could also be used for other demand forecast applications.

In the field of detection and prediction of company defaults and bankruptcy, significant effort has been devoted to evaluating financial ratios as predictors using statistical models and machine learning techniques. This problem becomes crucially important when financial decision-makers are provided with predictions on which to act, based on the output of prediction models. However, research has shown that such predictors are sufficiently accurate in the short-term, with the focus mainly directed towards large and medium-large companies. In contrast, in this paper, we focus on mid- and long-term bankruptcy prediction (up to 60 months) targeting small and/or medium enterprises. The key contribution of this study is a substantial improvement of the prediction accuracy in the short-term (12 months) using machine learning techniques, compared to the state-of-the-art, while also making accurate mid- and long-term predictions (measure of the area under the ROC curve of 0.88 with a 60 month prediction horizon). Extensive computational tests on the entire set of companies in Italy highlight the efficiency and accuracy of the developed method, as well as demonstrating the possibility of using it as a tool for the development of strategies and policies for entire economic systems. Considering the recent COVID-19 pandemic, we show how our method can be used as a viable tool for large-scale policy-making.

Blockchain is a disruptive technology that can be adopted in several business models. However, its applicability in the Supply Chain and in the context of the Logistics 4.0 and Smart Logistics revolution in particular, must still be proved from both an economic and an efficiency standpoint. This paper describes a Hyperledger Sawtooth-based framework for Supply Chain and Smart Logistics. The performance evaluation tests are performed on two Smart Logistics system settings. The results underline the performance decay of the system when concurrent transactions are submitted to multiple nodes.

The concept of "Smart City" became widely debated, including different components for building a truly sustainable urban environment. In the literature, there is a huge number of contributions inherent to the definition of a smart city, however, a broad view of the field is still missing. The aim of this paper is twofold. Firstly, to provide a repeatable and scalable methodology that can be applied to unstructured documents on smart cities projects considering all the multi-facet aspects of a smart city (e.g., business model, technology). Secondly, to propose an analysis carried out with a taxonomy to a database of 25 outstanding smart city projects in Europe, to discuss the current direction in which they are moving, identifying success factors and analyzing new trends and future paths.

Data Marketplace will be the engine of economic progress in smart cities. To foster the new Business Models of the Data and the Machine-to-Machine Economy, there are two needs: managing the plethora of sources of data and link them by a trusted, decentralized and scalable infrastructure. The two disruptive technologies able to foster this innovation are the Internet of Things (IoT) on the data-sources side and the Blockchain on the data marketplace side. In this paper we present a distributed data marketplace allowing different actors to purchase and monitor data streams coming from the smart city thanks to the use of IOTA technology, a Blockchain specifically developed for IoT networks. The marketplace is public and can be the starting point of other IOTA-based solutions.

Due to the diffusion of Internet of Things (IoT), many devices such as water meters, smart dumpsters, and many other objects have the capacity to record data. Gathering these data from the devices is a problem that could be solved in three ways: by building a huge network infrastructure, by using regular workforce or by using opportunistic IoT networks, i.e. by using as mobile hotspots the devices of selected users. The latter is cheaper than the others, requiring only the payment of a reward to the users. In this paper, we introduce a Multi Period Assignment problem, i.e. a problem for planning the operations of Opportunistic IoT networks. The problem minimizes the sum of user rewards, while gathering data from all devices. An effective heuristic method able to deal with realistic-sized instances is presented. The heuristic is able to find, by using a reasonable amount of time, the optimum for 124 out of 128 instances and reach gaps smaller than 0.1% for the remaining 4 instances.

Interest in City Logistics has been increasing among scholars, the industry, local administrations and various stakeholders. In particular, a number of projects have been developing with the aim of overcoming the negative impact of urban parcel delivery and making it more efficient and sustainable, from both the operations and environmental standpoints. However, most of these initiatives are more focused on technical solutions, regardless of the potential long-term economic feasibility and fitting with the stakeholders' needs. In order to take into account the actual feasibility of CL initiative, this paper presents the case study of the Urban Electronic Logistics (URBeLOG) project, carried out with the aim to develop an ICT platform to support and improve urban last-mile logistics in two pilot cities, namely Turin and Milan (Italy). The definition of the business model of the solution has been obtained through two sessions of the Business Model Canvas wherein all the partners of the project together with a representative of the public authority have taken part. The project combines the emphasis on the ICT solution with a stakeholder-driven approach to business model design.

Even though the first attempt to develop a vehicle-sharing system can be traced around 1950, only in the last decade it was possible to see an increase both in members and in service providers. This increase is mainly fostered by strong developments in the IT sector, which allow car-sharing companies to offer easier solutions to their members, such as vehicle localization and booking and electronic payments. A crucial point in setting up a car-sharing is to identify the willingness of the potential users to use the service. Aim of this study is to analyze this point through a survey, conducted on the summer of 2015, on the mobility behavior of the citizens in Turin, and their behavior toward car-sharing in particular. The main objective of this survey was to analyze how citizens changed their mobility behavior immediately after the introduction of the free-floating service mode. Furthermore, the survey was mainly focused on young persons, being this target of users more opened to innovations and more affected by budget limitations. Over a sample of about 1300 respondents (this number makes it one of the largest in the literature so far), more than 70% are male, in about 60% of the cases the age is between 18 and 24 years old, they mainly live in households of 4 persons, with 2 cars for each household.

The socio-economic development leads people to a great mobility. Thus the flights identification and management is becoming a key factor for the economic growth of the areas nearby the airports. The airport management is constantly looking for methods to improve its performance, both in terms of profitability and quality of service and the proper planning of passenger flows. To address these issues, scientific research provides methods and tools for decision support at all planning levels (i.e., strategic, tactical, operational, real time). In recent literature, it is now widely recognized that the hybridization of simulation and optimization systems is a very reliable technique for such decisions. This work intends to present an efficient Decision Support System framework based on the hybridization of a discrete event simulator and a Logit model. In order to show the effectiveness of the framework, we show the results of a real case study in North Italy.

One of the main issues in addressing three-dimensional packing problems is finding an efficient and accurate definition of the points at which to place the items inside the bins, because the performance of exact and heuristic solution methods is actually strongly influenced by the choice of a placement rule. We introduce the extreme point concept and present a new extreme point-based rule for packing items inside a three-dimensional container. The extreme point rule is independent from the particular packing problem addressed and can handle additional constraints, such as fixing the position of the items. The new extreme point rule is also used to derive new constructive heuristics for the three-dimensional bin-packing problem. Extensive computational results show the effectiveness of the new heuristics compared to state-of-the-art results. Moreover, the same heuristics, when applied to the two-dimensional bin-packing problem, outperform those specifically designed for the problem.