This work presents a steady-state genetic algorithm enhanced by a complete trie-based solution archive for solving the generalized vehicle routing problem with stochastic demands using a preventive restocking strategy. As the necessary dynamic programming algorithm for the solution evaluation is very time consuming, considered candidate solutions are stored in the solution archive. It acts as complete memory of the search history, avoids reevaluations of duplicate solution candidates, and is able to efficiently transform them into guaranteed new ones. This increases the diversity of the population and reduces the risk of premature convergence. Similar to a branch-and-bound algorithm, the tree structure of the solution archive is further exploited to compute lower bounds on the nodes to cut off parts of the solution space that evidently do not contain good solutions. Since in each iteration a not yet considered solution candidate is generated and completeness can be efficiently checked, the overall method is in principle an exact enumeration algorithm, which leads to guaranteed optimal solutions for smaller instances. Computational results of this algorithm show the superiority over the so far state-of-the-art metaheuristic and also prove its effectiveness on the nongeneralized version of this problem.

Population growth in cities and expanding city territory as well as population decline in rural areas pose a challenge for the existing transport network. Consequently, we observe a rapid change in transport infrastructure and transportation technology within the last few years. Due to novelty or differentness, it will initially be challenging to integrate them into the existing network in Western European cities and to identify suitable corridors leading to especially beneficial effects on the overall transport network. The effects of new technologies and (high-performance) infrastructures are hardly examined. It remains unclear how these novel transportation technologies will change society, our understanding of spatial proximity, mobility, and consequently the logistics sector. In this work, we give an overview regarding first considerations and reflections on the impacts of the changes and developments in the field of freight transportation. Our work mainly focusses on the estimation of the impacts of high-performance transport technologies on the society, spatial proximity, and the logistics sector while extending the European transportation network accordingly. In our understanding, we refer to high-performance transportation technologies as mobility systems with either high throughput (fast and/or high utilization loads) or very flexible application. To be more specific, we focus on Hyperloop technologies, Cargo-Sous-Terrain, freight airships, and drones.

This paper explores the feasibility and potential implications of automated air-taxis as a new mode of urban transport. By applying flight simulation and operations research, we investigate different questions regarding travel time, travel costs, and transport sustainability. As practical application, we consider the transportation service between the city and the airport, as well as on-demand air-taxi services within the city of Vienna. We compare the air-taxis with gasoline taxis and e-taxis, as they serve analogous transportation needs and cater to a similar clientele. In our effort to assess the feasibility of air-taxis, we aim to answer crucial questions that will influence the future of urban transportation. Our study examines how efficient air-taxis are in reducing travel time and explores their cost dynamics, addressing the affordability for passengers and their value of travel time savings. Additionally, we investigate their environmental impact by looking into energy consumption and CO₂ emissions. Through a rigorous analysis of empirical data and simulation outcomes, we aim to provide a comprehensive perspective that informs policy decisions and guides the evolution of urban transportation networks in the years to come.

Small cities and towns often struggle to provide high-quality public transport services to daily commuters. This is reflected in the modal split, where the share of car users dominates. Such a problem requires a modern solution, where transport planners can verify the impact of potential transport network improvements on the travel behavior of the residents before the changes are actually deployed. This study aims to demonstrate the usefulness of employing an agent-based simulation tool in the decision process for redesigning an express service regional bus route connecting a network of small cities and towns. The model was initially developed as a Mobility as a Service simulation solution for suburban areas of European metropolises. The model is adapted and applied to a case study for the region of Agder, Norway, simulating the impact of nine different scenarios on the patronage of a specific bus route. The simulation model proposes to upgrade the classic agent structure to a persona profile designed specifically for the case study. The main objective of this research is to identify the scenario that maximizes patronage while minimizing total route travel time and additional costs. The results suggest that the proposed model can be successfully adapted from suburban metropolitan areas to the realities of the considered case study, and potentially other similar regions. Specifically, out of the nine proposed scenarios, the model identified four promising ones. One of the four scenarios also fits the cost constraints imposed by the transport provider. The model provides a solid approach for analyzing complex transport systems that are practically impossible to consider in detail if the analysis is done without computer support. Thus, the results can be used as a decision support system for public transport planning and operations in networks of small cities and towns.