Vehicles

The purpose of this study was to investigate how environmental judgments and psychological factors jointly influence self-reported violation riding behaviors among e-bike riders in China, with attention to sociodemographic heterogeneity. To achieve this, the e-bike violation riding behavior questionnaire was designed. Additionally, a hybrid approach integrating the Structural Equation Model (SEM) with the Random Parameters Logit (RP-Logit) model was constructed to reveal the impact factors of e-bike riders’ violation riding behaviors, in which demographic information and latent variables were comprehensively considered. This methodology simultaneously analyzed the complex relationships among latent variables (measured by SEM) and captured the heterogeneous effects of demographic factors on discrete violation tendencies (modeled by RP-Logit). The following two main findings emerged: (1) Experienced riders and those who use e-bikes as operating tools tend to exhibit a higher tendency to engage in violation riding. (2) Perceived Risk has the greatest impact on the performance of high-violation tendencies. Specifically, the probability of choosing high-violation riding behaviors decreases by 0.18 for each unit increase in the rider’s Perceived Risk. (3) Similarly, for each unit increase in riders’ Perceived Law Enforcement, the probability of choosing high-violation riding behaviors decreases by 0.15. The findings suggest that relevant authorities should address e-bike violation behaviors through enhanced safety education and strengthened enforcement measures, particularly targeting high-risk rider groups.

The importance of Last-Mile Delivery (LMD) in the current economy cannot be overstated, as it is the final and most crucial step in the supply chain between retailers and consumers. In major cities, absent intervention, urban LMD emissions are projected to rise by >30% by 2030 as e-commerce grows (top-100-city “do-nothing” baseline). Sustainable, innovative ground-based solutions for LMD, such as Electric Vehicles, autonomous delivery robots, parcel lockers, pick-up points, crowdsourcing, and freight-on-transit, can revolutionize urban logistics by reducing congestion and pollution while improving efficiency. However, developing these solutions presents challenges in Operations Research (OR), including problem modeling, optimization, and computations. This systematic review aims to provide an OR-centric synthesis of sustainable, ground-based LMD by (i) classifying these innovative solutions across problem types and methods, (ii) linking technique classes to sustainability goals (cost, emissions/energy, service, resilience, and equity), and (iii) identifying research gaps and promising hybrid designs. We support this synthesis by systematically screening 283 records (2010–2025) and analyzing 265 eligible studies. After the gap analysis, the researchers and practitioners are recommended to explore new combinations of innovative solutions for ground-based LMD. While they offer benefits, their complexity requires advanced solution algorithms and decision-making frameworks.

This work addresses the challenges of airline planning, which requires the integration of flight scheduling, aircraft availability, and maintenance to ensure both airworthiness and profitability. Current solutions, often developed by human experts, are susceptible to bias and may yield suboptimal results due to the inherent complexity of the problem. Furthermore, existing state-of-the-art approaches often inadequately address critical factors, such as maintenance, variable flight numbers, discrete time slots, and potential flight repetition. This paper presents a novel approach to aircraft routing optimization using a model that incorporates critical constraints, including path connectivity, flight duration, maintenance requirements, turnaround times, and closed routes. The proposed solution employs a simulated annealing algorithm enhanced with specialized perturbation operators and constraint-handling techniques. The main contributions are twofold: the development of an optimization model tailored to small airlines and the design of operators capable of efficiently solving large-scale, realistic scenarios. The method is validated using established benchmarks from the literature and a real case study from a Mexican commercial airline, demonstrating its ability to generate feasible and competitive routing configurations.