Annarita De Maio

Cultural and archaeological tourism is increasingly acknowledged as a catalyst for regional development, particularly in areas beyond mainstream travel circuits. While itinerary planning has attracted academic attention, the optimization of multi-period cultural routes across dispersed archaeological landscapes remains underexplored. This paper proposes a novel bi-objective optimization model–belonging to the class of Vacation Planning Problems and formulated as a variant of the Team Orienteering Problem–for constructing multi-period archaeological itineraries. The model maximizes cultural value and minimizes travel distance, subject to some logistics constraints. A real-world application in Calabria, Italy–a region rich in archaeological heritage but limited in tourism infrastructure–demonstrates the capability of the model to generate efficient, thematically coherent routes. The findings underscore the potential of operations research methods to enhance sustainable tourism planning in marginal cultural landscapes.

This study explores the evolution of City Logistics 4.0 (CL4.0) by systematically reviewing the literature to provide a structured framework integrating management practices with technology and sustainability perspectives. Using the PRISMA methodology, a scientometric analysis of 899 Scopus-indexed papers reveals key research trends, authorship networks, and thematic developments. The findings highlight three core disciplinary perspectives—logistics, technology, and sustainability—offering a unified definition of CL4.0 and outlining future research directions. Practical implications equip policymakers and logistics professionals with insights for implementing innovative technologies and sustainable practices, paving the way for City Logistics 5.0.

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.

As consumer awareness grows and regulations regarding the quality and safety of perishable goods become stricter, careful management of environmental conditions throughout the supply chain is becoming essential. Among these factors, storage temperature plays a crucial role in preserving the physicochemical characteristics of products. Therefore, an effective approach to ensure quality and safety up to the final customer is to continuously monitor the temperature within warehouses, using specific location-mapping techniques and stocking optimization methods. This study proposes a dynamic optimization model for the storage location assignment problem, integrating both temperature and humidity constraints into the placement of stock-keeping units. The model operates under a multi-period, multi-product framework and leverages real-time sensor data to account for spatial temperature stratification and environmental variability within the warehouse, contributing to the reduction in the energy consumption. Two alternative optimization strategies are explored: one focused on minimizing thermal and humidity stress, and another targeting the reduction in average storage cycle time. A detailed what-if analysis is conducted across three scenarios, varying warehouse fill rates and incoming load volumes, in order to prove the effectiveness of the proposed model in a real-data context. The results show that the approach minimizing environmental stress consistently outperforms traditional methods in quality-related metrics, maintaining superior objective function values.

Cultural and archaeological tourism is increasingly acknowledged as a catalyst for regional development, particularly in areas beyond mainstream travel circuits. While itinerary planning has attracted academic attention, the optimization of multi-period cultural routes across dispersed archaeological landscapes remains underexplored. This paper proposes a novel bi-objective optimization model—belonging to the class of Vacation Planning Problems and formulated as a variant of the Team Orienteering Problem—for constructing multi-period archaeological itineraries. The model maximizes cultural value and minimizes travel distance, subject to some logistics constraints. A real-world application in Calabria, Italy—a region rich in archaeological heritage but limited in tourism infrastructure—demonstrates the capability of the model to generate efficient, thematically coherent routes. The findings underscore the potential of operations research methods to enhance sustainable tourism planning in marginal cultural landscapes.

As consumer awareness grows and regulations regarding the quality and safety of perishable goods become stricter, careful management of environmental conditions throughout the supply chain is becoming essential. Among these factors, storage temperature plays a crucial role in preserving the physicochemical characteristics of products.Therefore, an effective approach to ensure quality and safety up to the final customer is to continuously monitor the temperature within warehouses, using specific location-mapping techniques and stocking optimisation methods. This study proposes a dynamic optimization model for the Storage Location Assignment Problem (SLAP), integrating both temperature and humidity constraints into the placement of stock-keeping units (SKUs). The model operates under a multi-period, multi-product framework and leverages real-time sensor data to account for spatial temperature stratification and environmental variability within the warehouse. Two alternative optimization strategies are explored: one focused on minimizing thermal and humidity stress, and another targeting the reduction of average storage cycle time. A detailed what-if analysis is conducted across three scenarios, varying warehouse fill rates and incoming load volumes, in order to prove the effectiveness of the proposed model in real-data context.

To meet the increasing demands of home delivery resulting from the proliferation of internet shopping and compounded by the rising expectation of fast fulfillment (often within hours of request), companies seek new delivery methods supported by information and communication technologies. In this study, we consider a dispatching platform with delivery capacity consisting of a dedicated fleet of vehicles complemented by crowdsourced couriers. We consider the crowdsourced couriers to be in-store customers who, upon checking out of the store, declare themselves available to deliver one or more requests from e-shoppers. The role of the collaborative platform is to aggregate e-shopper orders from the participating businesses and then manage the routing for the pickup of the corresponding products at the physical stores and the subsequent deliveries to the e-shoppers’ locations. We model this dynamic stochastic pickup-and-delivery problem as a Markov decision process to represent the uncertainty in the e-shopper requests and in-store crowdshipper appearances. We adapt a real-time insertion method enhanced with a cost function approximation to account for differences in the temporal availability of the dedicated vehicles and in-store crowdshippers. We conduct computational experiments to demonstrate the conditions under which in-store crowdshippers provide a cost benefit.

This paper introduces a two-echelon optimization model for the integrated routing of an electric vehicle (EV) and a traditional internal combustion engine vehicle (ICEV) in an urban environment. The scientific context of this study is sustainable urban logistics. The case study focuses on the distribution of pharmaceuticals in the metropolitan area of Rome. Distributing pharmaceuticals in large cities presents significant challenges, including heavy traffic congestion, the need for strict temperature control, and the maintenance of the integrity and timely delivery of sensitive medications. Furthermore, the complexity of urban logistics and adherence to regulatory requirements introduce additional layers of difficulty. Therefore, the implementation of fast and sustainable distribution mechanisms is crucial in this context. Specifically, the model seeks to minimize both total CO₂ emissions and transportation costs while optimizing the use of an EV and an ICEV, all while ensuring that service level requirements are met. Computational results demonstrate the effectiveness of the proposed method in improving the sustainability of pharmaceutical distribution.