Web3 technologies, representing the next generation of a decentralised and user-centric Internet, offer innovative solutions to enhance adaptability, sustainability, and resilience in logistics systems aligned with the principles of Logistics 5.0. This study conducts a Systematic Literature Review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, analysing peer-reviewed journal articles published between 2018 and 2024 and retrieved from Scopus, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The review specifically focuses on trucking logistics, a sector characterised by high fossil-fuel dependency, operational fragmentation, and significant environmental impact. The findings reveal that Artificial Intelligence and Internet of Things technologies dominate current implementations, mainly supporting fleet management, route optimisation, accident prevention, and risk assessment. In contrast, blockchain applications remain limited, and metaverse-based solutions are largely exploratory and confined to training scenarios. Key research gaps include the scarcity of integrated Web3 solutions, the limited consideration of human-centric Logistics 5.0 dimensions, and the lack of large-scale empirical validation in real-world trucking operations. Based on the analysis, this paper proposes a conceptual framework that maps Web3 technologies to trucking logistics areas, investment priorities, and Logistics 5.0 objectives, offering actionable guidance for Logistics Service Providers transitioning from Logistics 4.0 to Logistics 5.0.
The Hamiltonian cycle problem is a well-known NP-complete problem in graph theory. This problem relates to lots of practical problems such as designing very large scale integration (VLSI) and travel-ling salesman problem (TSP). Since it is NP-complete, there is no efficient algorithm to solve the Hamiltonian cycle problem, and hence, its solution is valuable. In this paper, we propose new physical zero-knowledge proof protocols for the Hamiltonian cycle problem, whereby an entity can prove its knowledge of a solution to another entity without leaking any information about the valuable solution. Our protocols are more efficient than the previous protocols. We also propose a physical zero-knowledge proof protocol for TSP, one of whose building blocks is a new representation of an integer commitment with a secure addition protocol.
Objective. To develop, formalize, and evaluate a decentralized multi-agent system for realâtime traffic management in autonomous openâpit mining operations, aimed at minimizing truck idle time, preventing congestion and deadlocks, and increasing overall haulage efficiency. Methodology. The system is designed using a multiâagent paradigm where haul trucks, excavators,dumping points, and key infrastructure elements are modeled as autonomous agents. Task allocation is performed via a marketâbased Contract Net Protocol with bidding based on estimated time of arrival. Path planning employs an A* algorithm on a dynamic graph, while conflict resolution is achieved through a reservation mechanism managed by infrastructure agents. Reinforcement learning (Qâlearning) is integrated to allow truck agents to adapt their bidding strategies over time. The approach is validated through agentâbased simulations under normal, highâintensity, and disruption scenarios, and compared against a centralized firstâcome, firstâserved dispatch system. Results. Under normal operation, the multiâagent system reduced average truck idle time by 44%, cycle time by 17.5%, increased throughput by 21%, and lowered traffic conflict frequency by 66.5% compared to the baseline. In highâintensity traffic, it prevented congestion and deadlocks, maintaining smooth flow. During unexpected disruptions (excavator breakdown, road blockage), the system autonomously reâplanned routes and reassigned tasks within minutes, effectively isolating the impact and ensuring operational continuity. Scientific novelty. The novelty lies in the holistic integration of decentralized coordination, marketâbased task allocation, dynamic path reservation, and reinforcement learning within a unified multiâagent framework for realâtime traffic management in openâpit mining. This approach enables emergent selfâorganization, robust adaptation to dynamic conditions, and scalability beyond the capabilities of traditional centralized fleet management systems. Practical significance. The proposed system offers a scalable and resilient solution for autonomous haulage fleets, directly reducing operational costs through lower idle times and higher throughput, improving safety by preventing traffic conflicts, and enhancing resilience to equipment failures or route blockages. It provides a clear pathway for transitioning existing mines to fully autonomous, efficient, and safe operations. Keywords: multi-agent system, traffic management, open-pit mining, autonomous haulage, fleet management, reinforcement learning, Contract Net Protocol, path planning, conflict resolution, simulation.
The complexity of modern supply chain networks requires sophisticated approaches to inventory management that can effectively handle demand uncertainty and coordinate decisions across multiple organizational levels. This paper proposes a novel hierarchical multi-agent reinforcement learning framework for dynamic inventory allocation in multi-echelon supply chains facing stochastic demand patterns. The hierarchical architecture decomposes the inventory control problem into strategic and operational decision layers, where high-level agents coordinate allocation policies across distribution networks while low-level agents optimize local replenishment decisions. The framework integrates Centralized Training with Decentralized Execution paradigm, enabling autonomous agents to learn coordinated policies through shared experience while maintaining operational independence during deployment. Experimental results demonstrate that the proposed approach achieves significant reductions in total system costs compared to traditional base-stock policies and single-agent reinforcement learning methods, while effectively mitigating the bullwhip effect in supply chains with high demand variability.
This research proposes the development of a next generation airline reservation system that incorporates the Cloud microservices, distributed artificial intelligence modules and the blockchain technology to improve on the efficiency, safety and customer satisfaction. The traditional reservation systems encounter issues related to the expansion of the systems, the integrity of the data provided and the level of service offered to the customers, which is the main focus of this architecture through the modular and data centric design approaches. This will allow different operations such as reservations, payments, and customer data management among others to be performed separately thereby facilitating high availability of the system by 30% and enhancing performance of the system by 40% on its scalability. Such systems contain AI driven modules that utilize the past booking patterns along with the profile of the customer to estimate the demand and make recommendations, which increases to 25 % of customer engagement. Moreover, blockchain is effective in engaging an incorruptible ledger system for the all transactions therefore mitigating fraud incidences and increasing the clarity by 20%. The system was subjected to analysis using a simulator and using machine learning evaluations that rated it against other conventional systems. The results show that there were clear enhancements in the speed of transactions where the rates of secure data processing rose by 35%, and the system response time by 15 %. The system can also be used for other high transaction industries like logistics and hospitality. This structural design is indicative of how the use of advanced technologies will revolutionize the airline reservation sector. The implications are growing effectiveness, improvement in security and greater customer contentment.
Abstract We address two interrelated issues affecting the hinterland portion of the maritime container supply chain: reducing the movement of empty containers and reducing empty trips by trucks carrying these containers. In this paper, we show that empty container flow optimization can be implemented via a blockchain based on the proofâofâusefulâwork concept where the proof of work requires the solution of an âhard optimization problem whose solution benefits the blockchain participants. Accordingly, we propose that anonymous miners compete to solve the container truck routing problem, which seeks to find the most efficient routes for trucks. We show that this problem is âhard. Miners must also solve the problem of optimally matching consignees and shippers, which will reduce transportation and storage costs for empty containers. In essence, the proposed framework turns blockchain into a massive optimization engine that directly benefits the hinterland container supply chain ecosystem.
Abstract Alongside the rise of âlast-mileâ delivery in contemporary urban logistics, drones have demonstrate commercial potential, given their outstanding triple-bottom-line performance. However, as a lithium-ion battery-powered device, dronesâ social and environmental merits can be overturned by battery recycling and disposal. To maintain economic performance, yet minimise environmental negatives, fleet sharing is widely applied in the transportation field, with the aim of creating synergies within industry and increasing overall fleet use. However, if a sharing platformâs transparency is doubted, the sharing ability of the platform will be discounted. Known for its transparent and secure merits, blockchain technology provides new opportunities to improve existing sharing solutions. In particular, the decentralised structure and data encryption algorithm offered by blockchain allow every participant equal access to shared resources without undermining security issues. Therefore, this study explores the implementation of a blockchain-enabled fleet sharing solution to optimise drone operations, with consideration of battery wear and disposal effects. Unlike classical vehicle routing with fleet sharing problems, this research is more challenging, with multiple objectives (i.e., shortest path and fewest charging times), and considers different levels of sharing abilities. In this study, we propose a mixed-integer programming model to formulate the intended problem and solve the problem with a tailored branch-and-price algorithm. Through extensive experiments, the computational performance of our proposed solution is first articulated, and then the effectiveness of using blockchain to improve overall optimisation is reflected, and a series of critical influential factors with managerial significance are demonstrated.