Neale Ricardo Smith

The rise of autonomous vehicles (AVs) in transportation holds promise for revolutionizing emergency healthcare logistics. Ambulance allocation and routing is a real-time, dynamic, NP-hard problem complicated further by cyber-physical vulnerabilities that may disrupt communication and operational reliability. This study formulates the cyber risk-aware Autonomous Ambulance Allocation and Routing Problem (AARP) as a weighted multi-objective optimization problem, minimizing a composite of patient service completion times and delay penalties across priority tiers, while accounting for cyber-induced service interruptions through a robust disruption-budget model grounded in reliability engineering parameters. To solve this problem, we propose Q2GA, a reinforcement learning-assisted quantum-inspired genetic algorithm that dynamically tunes rotation-gate parameters via an ε-greedy Q-learning controller, enhancing exploration and exploitation without manual parameter scheduling. Unlike fixed-angle quantum genetic algorithms, Q2GA adapts its rotation policy based on population diversity and fitness signals, enabling more effective navigation of complex solution spaces. Benchmarked against a standard GA, Simulated Annealing, and a baseline QGA across three instance scales, Q2GA achieves the best mean solution quality on the small and medium instances; on the large instance its mean is statistically indistinguishable from that of simulated annealing, and its advantage shifts from mean fitness to run-to-run consistency. Sensitivity analysis reveals that disruption scope has a larger system-wide impact than recovery duration, a finding with direct implications for fleet cyber-resilience strategy. A simulation case study based on the 2025 Los Angeles Palisades Fire, combining real road-network geometry and reported infrastructure outages with simulated patients, triage classes and disruptions, evaluates the framework under geographically heterogeneous disruption conditions, where Q2GA exhibits the least performance degradation among all algorithms. The source code and simulation metadata are openly available; see the Data Availability statement.

This technical note aims to present a theoretical analysis for cybertronics engineering (CE) for a class of dynamic supply chains for artificial intelligence (AI)-based products, services or hybrid solutions. The cybertronics-based analysis encompasses three classes of supply chains: (1) energy-based dynamic supply chains (DSC); (2) semiconductor manufacturing and quantum supply chains; and (3) retailing of the AI-based DSC solutions generated. Considering the nonlinear nature of DSC, to provide solutions for products and services based on AI-chained supply chains, novel robust control is addressed via sliding mode control (SMC) with proper stability analysis for the DSC.

This research communication presents an analysis of dynamic supply chains (DSCs). The main goal of model-based cybertronics is to approximate, via a mathematical model from a dynamical system, the dynamics and behavior of dynamic supply chains. This considers that is at the operational level, where automation and control theory approaches take an insight —in this case, via Lyapunov stability—as a way to extend the use of mechatronic systems. Three case studies are presented: Firstly, the mathematical modeling and stability analysis of the ball-and-beam problem, as an approximation of a two echelon supply chain. Secondly, the mathematical modeling and stability analysis of a cold chain with temperature monitoring, and its relationship to inventory levels, are presented. From a theoretical perspective, applying model-based cybertronics in DSCs has direct practical implications: it improves operational control, enhances decision-making, and optimizes inventory management, particularly in cold chains. By treating high-volume supply chains as dynamical systems, managers can anticipate fluctuations and quantify efficiency. Finally, Lyapunov stability analysis ensures that models reliably reflect real-world behavior, enabling automation and predictable performance at an operational level in DSCs.

This technical note presents a perspective on the creation of value in time-delayed agri-food supply chains (AFSCs) and examines its impact on dynamic pricing, human resources, and sustainability. In general, analyzing AFSCs may seem straightforward; however, the complexity involved in managing dynamic pricing, sustainability trade-offs, and establishing ethnographic measures to improve worker productivity makes these challenges far from simple from an operational perspective. This study presents a mathematical model incorporating time delays in a two-tier supply chain consisting of producers and consumers, based on certain assumptions. In addition, a finite-time optimal control (OC) problem is addressed analytically using Pontryagin's maximum principle (PMP). Finally, stability and sensitivity analyses are conducted for a system that experiences losses in the production and consumption stages of the dynamic supply chain (DSC).

A critical decision in pre-disaster humanitarian logistics planning is determining the amount of aid to preposition to ensure timely and effective emergency response. To support managers in this process, we propose four mathematical formulations designed to optimize food prepositioning and subsequent distribution while minimizing unmet demand under supply uncertainty. Two formulations adopt the cardinality-constrained approach: one focuses on minimizing unmet demand, and the other incorporates equity in meeting demand. The remaining two formulations are scenario-based, addressing the same objectives with and without equity considerations. To compare the variations in the solutions generated by the proposed formulations and gain a deeper understanding of their behavior and performance, the formulations are applied to synthetic instances. To assist managers in selecting the model that best aligns with their objectives, we provide a summary of the advantages and disadvantages of each formulation. Our results show that considering supply uncertainty has important implications for the total costs, and that having adequate storage capacity may help mitigate the problems caused by this uncertainty.

At the end of the 20th century, inventory problems involving defective items have been relevant to production systems research. This paper provides an economic lot scheduling problem (ELSP) for a defective manufacturing system with scrap and reworked items involving multiple products, and two separate policies to handle the defective items. The defective items will be reworked and converted into useful items. In the first policy, all defective items are deposited in a warehouse until all production periods are completed, and then, after n production cycles, a separate rework process is implemented. In the second policy, all reworkable items are repaired in the same production cycle, i.e ., as soon as the production phase concludes, the rework process starts immediately. Finally, each problem is solved using a closed-form approach, employing the derivative method for solution, and a sensitivity analysis is performed. Both policies are compared, and some managerial insights are presented to identify which one is better and why.

Agri-food supply chains have experienced notable changes in recent decades, with tomatoes (Solanum lycopersicum) maintaining their status as a key global crop in terms of both production and consumption. These supply chains comprise a complex network of stakeholders—including producers, processors, distributors, and retailers—who collectively ensure the delivery of tomatoes from farms to consumers. This study develops mathematical models of agri-food tomato supply chains (AFTSCs) and examines their behavior through stability analysis and dynamic simulations based on a compartmental approach. Furthermore, the environmental impact is evaluated using a sustainability index, to which the waste diversion rate is introduced. This metric is defined as the proportion of diverted waste (i.e., materials recycled, reused, or composted) relative to the total waste generated, thereby enabling the quantification of sustainability performance within the system. Finally, a sensitivity analysis is conducted on the proposed dynamical models to validate and reinforce the findings.