Crowdshipping is an emerging delivery paradigm in urban areas, where ordinary people can become real carriers in exchange for an incentive. Nevertheless, the adoption process of crowdshipping initi...

Purpose: This study aims to provide a systematic literature review on the intersection of artificial intelligence (AI) and justice, analysing the evolution of AI driven innovation in the legal sector within the Justice 5.0 paradigm. The research classifies existing literature into three main discussion topics – predictive justice, human–machine combination and robot judges – through a multidisciplinary approach that includes technological, ethical and legal perspectives. By exploring AI’s transformative role, this study highlights the innovative integration of technology in legal decision-making and policy development. Design/methodology/approach: The research follows the PRISMA methodology to systematically review 140 relevant papers from the Scopus database. It combines qualitative and quantitative analyses, including bibliometric mapping, visualization techniques and bibliographic coupling. A theory-building approach is adopted to identify key trends, challenges and opportunities in AI-driven innovation, emphasizing its impact on modern judicial systems. Findings: The review highlights the increasing innovation in legal processes through AI applications, offering enhanced efficiency and predictive capabilities while raising ethical concerns regarding bias, transparency and human oversight. The findings categorize AI-based innovations in justice into three key areas: (1) predictive justice, where AI tools analyse jurisprudential data to support legal decision-making; (2) human–machine collaboration, where AI enhances legal professionals’ efficiency in case management and legal research and (3) the concept of robot judges, which explores the potential and limitations of fully automated legal decisions. The study also emphasizes the transition from Justice 4.0 to Justice 5.0, promoting human-centred AI innovation in judicial systems. Research limitations/implications: While this study comprehensively maps AI-driven innovations in justice, the rapid evolution of AI technologies may introduce new developments beyond the scope of this review. Future research should focus on empirical studies to assess the real-world effectiveness and fairness of AI-driven legal innovation. Practical implications: The findings offer valuable insights for policymakers, legal practitioners and AI developers, guiding the responsible implementation of AI innovations in justice systems. Understanding the interplay between technological innovation and law is crucial for ensuring transparent and equitable legal decision-making. Social implications: The integration of AI-based innovations in justice potentially improves legal accessibility and efficiency while also posing risks related to algorithmic bias and the erosion of human judicial discretion. Addressing these concerns is vital for fostering trust in AI-assisted legal frameworks. Originality/value: This work contributes to the literature by offering a systematic classification of AI-based innovations in justice, providing a structured overview of technological advancements and ethical concerns. It establishes a foundational reference for future research and policymaking, highlighting critical challenges and opportunities in AI-enhanced legal innovation.

In this paper, we focus on a location-routing problem (LRP) in the dairy industry. This problem consists of locating a cold storage warehouse, from which vehicles of limited capacity are dispatched to serve a given number of supermarkets with uncertain service requirements, and determining the order of supermarkets served by each vehicle. First, the LRP is solved by using a classical approach based on a deterministic model where the service requirements, i.e. customer demands, are defined through sample means. Second, we propose an indifference zone approach to the LRP. The indifference zone procedures are specific ranking and selection methods aimed at selecting the best option from a set of alternative configurations. In particular, they attempt to guarantee the probability of correct choice, while minimising the computational effort. The numerical results presented in the paper highlight the risk of biased decision making when mere sample means are used in a deterministic model. In addition, they show the effectiveness of indifference zone approaches to the dairy products distribution activity.

The first part of the paper gives an overview of the environmental conditions with which a future two stroke powered vehicle must comply and explains the reasons for which a direct gasoline injection into the combustion chamber offers a potential solution. The paper continues with a description of the fuel/air mixture injection used in the F.A.S.T. concept and gives a detailed overview of the layout of the 125 cc engine to which it is applied. The structure of its electronic engine management system, mandatory for the necessary control precision, is presented. Hereafter is made a short introduction to the visualization and numerical computation tools used for the engine design optimization. The paper concludes with a detailed presentation and discussion of the experimental results obtained with the engine operated, either in steady state and transient conditions on an engine test rig, and mounted in a classic small dimension two-wheel vehicle submitted to road tests.