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The aim of the work is to indicate the direction of development of postal services by means of various analyses. According to the trend of recent years, the number of letters in domestic and international traffic keeps decreasing, while the number of parcels keeps increasing. The Universal Postal Union and the postal organizations of individual countries must respond to this trend. Of course, the Covid-19 pandemic, the complex geopolitical situation in Eastern Europe and the current energy crisis have had a significant impact on postal services. The analysis includes the EU member states, Schengen countries and Balkan countries. Indicators like average area covered by a permanent office and average number of inhabitants served by a permanent are analyzed. The comparative analysis of postal services in Serbia and Slovakia includes the number of letter, parcel and express services, revenues from these services and data on the coverage of the postal network in these countries. With the help of the analysis performed in this paper, a good example can be found and a way for further development in the functioning of the postal market and the provision of postal services. For example: new parcel services must be introduced, delivery times must be shortened, and the sending and receiving of parcels must be facilitated.

Unmanned aerial vehicles, commonly referred to as drones , have recently seen an increased level of interest as their potential use in same-day home delivery has been promoted and advocated by large retailers and courier delivery companies. We introduce a novel way to exploit drones in same-day home delivery settings: drone resupply. We consider a home delivery system in which delivery trucks are regularly resupplied by drones. Resupply can take place whenever a delivery truck is stationary and a drone can land on the truck’s roof. We introduce the vehicle routing problem with drone resupply to capture and investigate this setting. We develop different algorithms and compare their performance. Finally, we quantify the potential benefits of drone resupply and generate valuable insights for advancing this concept.

This paper studies the economic desirability of unmanned aerial vehicle (UAV) parcel delivery and its effect on e-retailer distribution networks while taking into account technological limitations, government regulations, and customer behavior. We consider an e-retailer offering multiple same-day delivery services including a fast UAV service and develop a distribution network design formulation under service-based competition where the services offered by the e-retailer not only compete with the stores (convenience, grocery, etc.) but also with each other. Competition is incorporated using the multinomial logit market share model. To solve the resulting nonlinear mathematical formulation, we develop a novel logic-based Benders decomposition approach. We build a case based on New York City, carry out extensive numerical testing, and perform sensitivity analyses over delivery charge, delivery time, government regulations, technological limitations, customer behavior, and market size. The results show that government regulations, technological limitations, and service charge decisions each play a vital role in the future of UAV delivery.

With rapid population growth and increasing demand for urban mobility, metropolitan areas such as Singapore, Tokyo, and Shanghai are increasingly dependent on public transport systems. Various strategies are proposed to improve the service quality and capacity of bus and subway systems. Express trains—i.e., trains that skip certain stations—are commonly used because they can travel at higher speeds, potentially reduce travel time, and serve more passengers. In this paper, we study cyclic express subway service (CESS), in which express trains provide routine transport service with cyclic (periodic) station-skip patterns that can be used in daily service. We propose an exact Mixed Integer Programming (MIP) model to optimize cyclic station-skip patterns for express trains operating in a single-track subway system. The objective is to reduce passengers’ total travel time—i.e., the sum of waiting time and riding time—while considering demand intensity and distribution and train headway, frequency, and capacity. We implement the model in a set of numerical experiments using real data from Singapore. To solve the optimization problem more efficiently, we also propose a heuristic to solve large-scale problems. We observe that the exact MIP model for CESS provides optimal cyclic express service patterns within a reasonable computational time, and the heuristic method can significantly reduce the computational time and provide a good solution. The case study demonstrates that passengers’ average travel time could be significantly reduced compared to local train service. We also discuss the potential transfer of passengers between express trains and evaluate its effects using numerical experiments.

The increasing frequency and complexity of cyberattacks pose a significant threat to the express logistics sector, where digital coordination and data integrity are essential for operational continuity. This study examines how specific cyber-attack features influence team-based cybersecurity readiness, focusing on the mediating role of project team skills. The research addresses a practical problem faced by logistics firms in Jordan, many lacking standardized cybersecurity protocols despite rising digital adoption. Using a quantitative, cross-sectional design, data were collected from 310 employees in express delivery service companies. Structural equation modeling (SEM) via SmartPLS was employed to test the proposed relationships. The results demonstrate that cyber-attack features significantly affect cybersecurity readiness, both directly and through the partial mediation of project team skills. Teams with stronger technical and collaborative capabilities were more effective in translating threat exposure into organizational preparedness. The model accounted for 69% of the variance in cybersecurity readiness. This study contributes a novel integration of technical threat dimensions and human-centered readiness within logistics operations. It offers actionable insights for logistics managers aiming to strengthen cyber resilience through targeted skill development and team-based interventions.

Long in-vehicle travel times resulting from frequent stops make bus service an unattractive choice for many commuters. Limited-stop bus services however have the advantage of shorter in-vehicle times experienced by passengers. In this work, we seek to modify a given bus service by optimally reassigning some number of bus trips, as opposed to providing additional trips, to operate a limited-stop service. We propose an optimization model to determine a limited-stop service route to be operated in parallel with the local service and its associated frequency to maximize total user welfare. A few theoretical properties of the model are established and used to develop a solution approach. As a proof of concept, we present numerical results obtained using real-world data together with comprehensive discussions of solution quality, computational times and the model’s sensitivity to different parameters. Finally, we solve the optimization model for 178 real-world bus routes with different characteristics in order to demonstrate the impacts of some key attributes on potential benefits of limited-stop services.

The rapid development of e-commerce has led to increased pressure on the express delivery industry to transport products to customers in a timely manner. The problem of how to deliver an increasing volume of express orders to customer clusters in a timely manner and at low cost with the joint distribution mode is becoming urgent. In this study, an express terminal node optimization and integration model is presented with an option to detach single customer clusters. In addition, the simulated annealing algorithm (SAA) based on neighborhood search that includes four rules is proposed to solve the problem. Contrast experiments are performed with SAA, the immune genetic algorithm (IGA), and the CPLEX solver. The experimental results indicate that IGA is less effective than SAA, and the running time of the IGA is longer. The CPLEX solver is less effective than the SAA, too. Additionally, the experimental results also show that every neighborhood rule proposed in this study plays a role in the optimization process.