The application of artificial intelligence (AI) is poised to bring about a revolutionary shift in the field of supply chain management. The potential for artificial intelligence to transform supply chain operations and improve sustainability and efficiency is examined in this article. We start by analyzing the complicated and challenging present supply chain environment before delving into the crucial part artificial intelligence (AI) plays in solving these problems. We demonstrate AI-driven advancements in demand forecasting, inventory optimization, logistics management, and sustainability programs using real-world examples. In addition, we provide case studies that illustrate effective AI applications and their quantifiable results. As the piece goes on, we also go over the moral issues and legal difficulties that AI in supply chains raises. We suggest a thorough framework for incorporating AI into supply chain management that includes automation, predictive modeling, data integration, and sustainability principles in order to direct this transition. In this final look ahead, we investigate new developments in artificial intelligence and the possibility of completely self-sufficient supply networks. In addition to highlighting sustainability and ethical AI adoption, this essay highlights the significant impact of AI in changing supply chain dynamics and provides a forward-thinking prognosis for supply chains powered by AI.

Disaster logistics presents a highly complex decision-making challenge under conditions of uncertainty, where the timely and efficient allocation of scarce resources is essential to minimize human suffering. In this context, we propose a novel Quantum-Inspired Hyperheuristic Framework (QHHF) designed to solve Dynamic Multi-Objective Combinatorial Optimization Problems (DMOCOPs) arising in disaster relief operations. The proposed framework integrates Quantum-Inspired Evolutionary Algorithms (QIEAs), which facilitate diverse and explorative solution generation, with a Reinforcement Learning (RL)-based hyperheuristic capable of dynamically selecting the most suitable low-level heuristic in response to evolving disaster conditions. A dynamic multi-objective mathematical model is formulated to simultaneously minimize total travel cost and risk exposure, while maximizing priority-weighted demand satisfaction. The model captures real-world complexity through time-dependent variables, stochastic demand variations, and fluctuating transportation risks. Extensive simulations using real-world disaster scenarios demonstrate the effectiveness of the proposed approach in generating high-quality solutions within stringent response time constraints. Comparative evaluations reveal that QHHF consistently outperforms traditional heuristics and metaheuristics in terms of adaptability, scalability, and solution quality across multiple objective trade-offs. Notably, our method achieves a 9.6% reduction in total travel cost, a 6.5% decrease in cumulative risk exposure, and a 4.7% increase in priority-weighted demand satisfaction when benchmarked against existing techniques. This work contributes both to the advancement of hyperheuristic theory and to the development of practical, AI-enabled decision-support tools for emergency logistics management.

This paper proposes a novel Hybrid Metaheuristic Framework (HMF) that integrates multiple optimization strategies within a self-adaptive mechanism for solving large-scale combinatorial problems. Unlike traditional metaheuristics, which rely on fixed algorithmic parameters and predefined strategies, HMF dynamically selects and adjusts heuristic operators using a reinforcement learning-based adaptive mechanism. Specifically, the framework employs Deep Q-Learning to guide real-time parameter tuning and metaheuristic selection based on performance feedback from the optimization process. The study applies this framework to three benchmark problems: the Vehicle Routing Problem (VRP), the Facility Location Problem (FLP), and the Job Scheduling Problem (JSP), demonstrating consistent gains in solution quality and convergence speed. Experimental results reveal that HMF achieves noticeable improvements over conventional methods in both runtime efficiency and optimization effectiveness, without requiring manual parameter tuning. The findings establish the proposed framework as a promising tool for real-world large scale combinatorial optimization challenges.

The Internet of Things (IoT) is expanding rapidly, but the security of IoT devices remains a noteworthy concern due to resource limitations and existing security conventions. This research investigates and proposes the use of a Light certificate with the Constrained Application Protocol (CoAP) instead of the X509 certificate based on traditional PKI/CA. We start by analyzing the impediments of current CoAP security over DTLS with the certificate mode based on CA root in the constrained IoT device and suggest the implementation of LightCert4IoT for CoAP over DTLS. The paper also describes a new modified handshake protocol in DTLS applied for IoT devices and Application server certificate authentication verification by relying on a blockchain without the complication of the signed certificate and certificate chain. This approach streamlines the DTLS handshake process and reduces cryptographic overhead, making it particularly suitable for resource-constrained environments. Our proposed solution leverages blockchain to reinforce IoT gadget security through immutable device characters, secure device registration, and data integrity. The LightCert4IoT is smaller in size and requires less power consumption. Continuous research and advancement are pivotal to balancing security and effectiveness. This paper examines security challenges and demonstrates the effectiveness of giving potential solutions, guaranteeing the security of IoT networks by applying LightCert4IoT and using the CoAP over DTLS with a new security mode based on blockchain.

This study addresses the optimization of the Vehicle Routing Problem (VRP) with prioritized customers by introducing and comparing two advanced solution approaches: a metaheuristic-based hyperheuristic framework and a Variational Autoencoder (VAE)-based hyperheuristic. The VRP with prioritized customers introduces additional complexity by requiring efficient routing while ensuring high-priority customers receive service within strict constraints. To tackle this challenge, the proposed metaheuristic-based hyperheuristic dynamically selects and adapts low-level heuristics using Simulated Annealing (SA) and Ant Colony Optimization (ACO), enhancing search efficiency and solution quality. In contrast, the VAE-based approach leverages deep learning to model historical routing patterns and autonomously generate new heuristics tailored to problem-specific characteristics. Through extensive computational experiments on benchmark VRP instances, our results reveal that both approaches significantly enhance routing efficiency, with the VAE-based method demonstrating superior generalization across varying problem structures. Specifically, the VAE-based approach reduces total travel costs by an average of $8\%$ and improves customer priority satisfaction by $95\%$ compared to traditional hyperheuristic methods. Moreover, a comparative analysis with recent state-of-the-art algorithms highlights the competitive performance of our approaches in balancing computational efficiency and solution quality. These findings underscore the potential of integrating metaheuristics with machine learning in complex routing problems and provide valuable insights for real-world logistics and transportation planning. Future research will explore the generalization of these methodologies to dynamic and large-scale routing scenarios.