This study investigates the complex, nonlinear dynamics of the achievement of Sustainable Development Goals (SDGs) within national innovation ecosystems (NIEs), addressing a critical gap in understanding SDG saturation. Studying this topic is crucial because, despite the growing importance of sustainability issues in innovation ecosystem research driven by social and economic pressure, the literature remains fragmented and undecided, lacking a clear understanding of complex governance and operational frameworks in NIEs. Employing the Bass diffusion model, we analyze 22 years of SDG Index score data (2000–2021) from 177 countries/regions. The model quantifies SDG saturation time, accounting for complex interdependencies and threshold effects overlooked by traditional linear models. Findings reveal characteristic S-shaped adoption curves for most countries, indicating initial slow progress, acceleration driven by imitation, and eventual saturation. Geographical and socioeconomic clustering analyses highlight distinct patterns in SDG progress that depend on the peculiarities of complex systems in national contexts. High-income countries exhibit greater innovation efforts but lower saturation potential due to the proximity to SDG ceilings, while lower-income countries face complex structural constraints. The Bass model proves effective for forecasting SDG saturation and informing policy interventions in complex systems like NIEs, emphasizing the need for context-dependent governance and resource allocation to accelerate equitable sustainable development across the globe.

INCIT-EV was an European Project financed under the Horizon2020 programme, grant agreement number 875683, that involved over 30 partners, grouped in national consortia (Spain, France, Netherlands, Estonia and Italy) and developed 7 case studies aimed at experimenting with new charging solutions for Electric Vehicles (EV) and business models capable of fostering the adoption of EVs in the European Union (EU). The project successfully ended on 30/6/2024, and the present work is aimed at illustrating the results of the research activities and experimental tests carried out, with an in-depth outlook on the future of EV charging and EV adoption based on the information gathered and the experiences of this project regarding the technology development, the economics of the EV market and the importance of the policy making activities by the governance authorities and technical committees.

Due to the peculiarities of city streets, last-mile logistics is typically organized using territory-based routing approaches, which divide the city into a set of districts and assign drivers to deliver in one or more of them. This allows drivers to develop a deep understanding of the characteristics of each district, and clients benefit from consistent service. However, these advantages must be carefully weighed against the flexibility of daily customer assignments, which enable planners to maximize driver utilization and minimize routing costs. In this paper, we propose a new holistic framework for defining districts, considering the impact on the quality of logistics decisions and fleet capacity usage. Specifically, we address how districting decisions affect the demand distribution within each district. Computational experiments on both simulated and realistic instances demonstrated a significant reduction in costs compared to benchmark techniques.

One important challenge in the planning and management of supply chains is selecting logistics service suppliers, which ensures the efficient movement and storage of goods between entities within the chain. In this article, we address this problem from the perspective of a shipper who must plan transportation capacity over a given corridor and, in doing so, must select among multiple suppliers to determine the total transportation capacity to be secured. As suppliers can differ in various ways, including their rates and the quantity of capacity units they offer, this capacity planning problem presents a significant challenge for shippers, who must negotiate such plans in advance. To tackle this problem, we propose a new generalization of the Bin Packing model with variable costs and bin sizes that explicitly incorporates the sourcing decisions, that is, the selection of the suppliers. We conduct a series of computational experiments to investigate the added complexity of explicitly considering supplier selection and to derive insights into how these decision-related aspects influence the resulting capacity plans.

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.

This study addresses last-mile delivery optimization in urban logistics by integrating occasional drivers (ODs) with preplanned itineraries and carbon emission considerations into a multi-tier distribution framework. A mixed-integer linear programming model is proposed to jointly optimize task assignment, routing, and scheduling while explicitly accounting for carbon-emission costs and flexible demand splitting at local distribution hubs. To solve the problem efficiently, we develop a hybrid heuristic that combines OD matching and adaptive large neighborhood search, enhanced by tabu-based memory, dual-informed operators, and a Great Deluge acceptance rule. Computational experiments first validate the OD assignment and pickup–delivery component on VRPTWOD-type benchmark instances, and then evaluate the complete framework on 54 benchmark instances. Under a 3600-second time limit, Gurobi obtained feasible incumbent solutions for 28 benchmark instances, whereas the proposed method consistently provides high-quality solutions across all benchmark groups; in repeated-run experiments on a prespecified 21-instance subset, the full GDALNS/TD variant achieves the best mean relative gap of 0.39%. A practical Chengdu case study further shows total cost reductions of 14.8% and 35.3% on two real-world instances, demonstrating the method’s effectiveness and scalability for sustainable last-mile planning.

This paper proposes a novel agent-based model for new-product diffusion, grounded in the kinetic theory of statistical mechanics. The model describes a population of utility-driven agents whose adoption decisions emerge from belief dynamics shaped by peer influence, advertising, and stochastic factors. By adapting the Boltzmann equation, a closed-form expression for the adoption curve is derived as an emergent property of decentralized, compromise-based interactions. Unlike aggregate models such as the Generalized Bass Model or the Bass Logit Diffusion Model, the proposed Kinetic Innovation Diffusion (KID) model links micro-level behavioral rules to macro-level adoption dynamics without relying on top-down assumptions. Empirical validation on benchmark products—including color televisions, air conditioners, clothes dryers, and freezers—demonstrates that the KID model outperforms existing methods in both fit and early-stage forecast accuracy. Thanks to its tractable structure, the model enables estimation of key strategic quantities from minimal data, making it a valuable tool for pre-launch planning and early diffusion monitoring. Its flexibility also supports extensions to incorporate heterogeneity, abandonment, or strategic firm behavior—offering a unified, analytically grounded framework for innovation diffusion.

Accurate prediction of the diffusion of new transportation technologies such as electric vehicles (EVs) is critical for defining policy, infrastructure planning, and anticipating impacts on energy, emissions, and mobility systems. The Bass diffusion model is widely used to forecast technology adoption, but its parameters traditionally do not explicitly account for market-specific factors driving diffusion. This research proposes a generalized Bass model incorporating the effects of policy, economic, technological, and social variables derived from expert surveys. However, limited survey sample sizes introduce uncertainty that must be addressed. We develop a novel approach using extreme value theory to robustly estimate the Bass parameters while accounting for errors from imperfect survey data. Our approach links forecasting models with market factors from multiple data sources while rigorously handling uncertainties, supporting the design, evaluation, and impact assessment of transportation policies. The methodology is applied to forecast the adoption of regional electric vehicles throughout Europe in different policy scenarios related to factors such as charging infrastructure, purchase incentives, battery costs, and environmental awareness.

In the nowadays common two-tier logistic systems for delivering products in urban areas, the last leg of the distribution chain, the so-called last mile, is by far the most problematic. The modeling of the last-mile delivery problem as a variable cost and size bin packing problem with time-dependent costs emerged recently as the best solution for planning the delivery operations. No exact solution has been yet proposed for efficiently solving this variant of the bin packing problem. In this paper, we present a new problem formulation and devise an exact branch-and-bound method for effective and efficient problem resolution. Upon the resulting tailored solution approach, we devise a procedure to speed up the problem resolution further. Numerical results collected on large-sized instances reveal the dramatic reduction of the computation time obtained with our solution approach, which turns out to be up to ten times faster than the commercial solver. The improvement in solving large-sized instances with a relatively easy-to-implement approach is remarkably relevant for real-world applications.

Blockchain provides several advantages, including decentralization, data integrity, traceability, and immutability. However, despite its advantages, blockchain suffers from significant limitations, including scalability, resource greediness, governance complexity, and some security related issues. These limitations prevent its adoption in mainstream applications. Artificial Intelligence (AI) can help addressing some of these limitations. This survey provides a detailed overview of the different blockchain AI-based optimization and improvement approaches, tools and methodologies proposed to meet the needs of existing systems and applications with their benefits and drawbacks. Afterwards, the focus is on suggesting AI-based directions where to address some of the fundamental limitations of blockchain.

PurposeBlockchain and distributed ledger technologies are increasingly prominent, yet their adoption remains complex. This paper addresses the common misalignment between blockchain technology and actual needs, often leading to project failure. It introduces a decision-making framework focused on the technological aspects of blockchain adoption.Design/methodology/approachWe designed the framework by analyzing key decision drivers from existing literature and applied it to a real-world use case in the electric vehicle supply chain. The blockchain solution was tested with live production data.FindingsBlockchain is beneficial for use cases requiring decentralized governance, but it often needs to be supplemented with additional technologies in industrial applications.Originality/valueThe framework provides a set of managerial-level questions that simplify the decision-making process for those without deep technical expertise, helping determine when blockchain is appropriate, valuable and superior to other technologies.

The rapid urbanization and population growth in major cities worldwide have led to a significant increase in medical waste generation, often containing infectious materials that require stringent handling protocols. To address the complexity of vehicle allocation and routing in this context, efficient planning methods are essential. This study introduces a comprehensive approach to the medical waste location-routing problem, incorporating multiple practical constraints such as vehicle capacity, hospital classification, infection risks, and time-window restrictions. Our novel solution integrates an exact algorithm for optimizing transfer center locations and collection routes at the upper level, combined with an improved adaptive large neighborhood search (IALNS) for routing optimization at the lower level. The IALNS leverages enhanced neighborhood exploration techniques and Pareto ranking with reward adjustment method to balance total cost and infection risk. Simulations based on real-world data from Chengdu, China, validate the effectiveness of the proposed method. Additionally, comparisons with Gurobi and other representative metaheuristic algorithms on randomly generated instances and benchmark datasets further demonstrate the superior efficiency and solution quality of the IALNS algorithm. This research provides government authorities with a practical and robust strategy for transporting infectious medical waste, enhancing both operational efficiency and public health safety.

Blockchains commonly employ tree data structures (e.g., Merkle trees) to represent state. While tree structures enable fast and compact state correctness checks, they introduce constraints when it comes to parallelizing transaction execution. In particular, concurrent transaction execution can lead to multiple trees representing the same state, hindering consensus among blockchain peers. We characterize this phenomenon as the ambiguous state representation problem and propose an optimistic algorithm that guarantees the creation of the same state tree across multiple peers. We integrated our solution into Cosmos SDK framework, a popular production blockchain system, allowing applications to benefit from parallel transaction execution without modifying their existing codebase. We report on the performance of parallel transaction execution under a variety of conditions in a network of up to 40 peers.

Last-mile delivery is regarded as an essential, yet challenging problem in city logistics. One of the most common initiatives, implemented to streamline and support last-mile activities, are satellite depots. These intermediate logistics facilities are used by companies in urban areas to decouple last-mile activities from the rest of the distribution chain. Establishing a business model that considers different stakeholders' interests and balances the economic and operational dimensions, is still a challenge. In this paper, we introduce a novel problem that broadly covers such a setting, where the delivery to customers is managed through satellite depots. The interplay and the hierarchical relation between the problem agents are modeled in a bi-level framework. Two mathematical models and an exact solution approach, properly customized for our problem, are presented. To assess the validity of the proposed formulations and the efficiency of the solution approach, we conduct an extensive set of computational experiments on benchmark instances. In addition, we present managerial insights for a case study on parcel delivery in Turin, Italy.

This paper focuses on the tactical planning problem faced by a shipper which seeks to secure transportation and warehousing capacity, such as containers, vehicles or space in a warehouse, of different sizes, costs, and characteristics, from a carrier or logistics provider, while facing different sources of uncertainty. The uncertainty can be related to the loads to be transported or stored, the cost and availability of ad-hoc capacity on the spot market in the future, and the availability of the contracted capacity in the future when the shipper needs it. This last source of uncertainty on the capacity loss on the contracted capacity is particularly important in both long-haul transportation and urban distribution applications, but no optimization methodology has been proposed so far. We introduce the Stochastic Variable Cost and Size Bin Packing with Capacity Loss problem and model that directly address this issue, together with a metaheuristic to efficiently address it. We perform a set of extensive numerical experiments on instances related to long-haul transportation and urban distribution contexts and derive managerial insights on how such capacity planning should be performed. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

The integration of Business Process Model and Notation (BPMN) and process mining techniques offers a promising path for analysing and improving judicial proceedings. This study investigates into the utilization of BPMN for modelling judicial processes and employs process mining to analyse process logs obtained from the Employment section of the Turin Court. Data extraction and identification of the attributes were followed by a Statistical analysis, to reveal general patterns of the logs. Preprocessing included removal of outliers, cleaning of inconsistencies and redundancies, data aggregation. The result allowed our interdisciplinary team to focus only on critical elements. Thanks to a process mining tool (Apromore) we could visualize and analyse the process map, allowing comparison with the BPMN model from previous work. The "Hearing waiting time and hearing" activity significantly impacts the duration of the proceeding. This activity may occur repeatedly, and its duration is especially noticeable for the initial hearing. The analysis of process variations concentrated on differences in the closing phase of the proceeding and its influence on the overall duration. Variations ending with premature and anomalous termination show the smallest average time, followed by proceedings involving settlements or Fornero procedures. Understanding the factors influencing these outcomes contributes to a deeper understanding of process dynamics. Overall, this research underscores the value of process-oriented analysis in judicial settings, offering useful vision to improve efficiency and performance. By focusing on specific activities and phases, our study provides a foundation for further investigations into court proceedings, with broader implications for judicial practice and policy.

Artificial intelligence (AI) techniques are becoming more and more widespread. This is directly related to technology progress and aspects as the flexibility and adaptability of the algorithms considered, key characteristics that allow their use in the most variegated fields. Precisely the increasing diffusion of these techniques leads to the necessity of evaluating their robustness and reliability. This field is still quite unexplored, especially considering the automotive sector, where the algorithms need to be prepared to answer noise problems in data acquisition. For this reason, a methodology directly linked to previous works in the heavy vehicles field is presented. In particular, the same is focused on the estimation of rollover indexes, one of the main issues in road safety scenarios. The purpose is to expand the cited works, addressing the LSTM networks performance in case of strongly disturbed signals.