This study proposes a roadside unit (RSU)-enabled autonomous driving framework addressing occlusion challenges through global state awareness and dynamic resource co-optimization. The methodology resolves two interconnected problems: (1) navigation reliability degradation caused by occluded road users, and (2) communication resource allocation inefficiency in high-mobility vehicle-to-everything (V2X) networks. A three-tier technical architecture is developed: (a) RSU-assisted environmental state estimation that fuses infrastructure-side perception with vehicular status to mitigate occlusion-induced uncertainty, (b) joint communication-planning optimization via a differentiable end-to-end framework, unifying motion control and resource allocation decisions, and (c) distributed motion planning with latency-aware safety verification, ensuring real-time feasibility through edge-assisted pipeline optimization. Experimental results demonstrate superior performance in occlusion-rich scenarios compared to existing V2X-dependent approaches, achieving balanced improvement in task completion rates and communication overhead reduction. The framework maintains operational robustness under varying traffic densities while ensuring real-time computational feasibility through edge-assisted pipeline optimization.

Directing patients with both minor and serious illnesses to top-tier hospitals undermines equitable resource allocation within a tiered system due to the limited capacity of these facilities. Treating minor ailments in top-tier hospitals can delay care for serious conditions and undermine the efficiency of resource use. The interplay between personal opinions and behaviors shapes patient choices within the tiered healthcare system. This raises equity concerns, as entrenched misconceptions about care options hinder effective resource allocation. However, the influence of nonrational factors on patient preference for tertiary care remains insufficiently quantified. Therefore, this study models the influence of irrational factors on patient selection within a two-tiered healthcare system. The model assumes that individuals initially hold small, readily influenced preferences for tertiary care, and that both primary and tertiary facilities possess sufficient capacity to meet patient demand. These preferences undergo nonlinear updates based on individual healthcare experiences, which are subsequently disseminated throughout the community. Both theoretical analysis and simulations demonstrate that these initially small preferences, through community interaction, can converge towards an overwhelming preference for tertiary care, ultimately destabilizing the effectiveness of the two-tiered system. Ideally, primary care facilities should proactively build community trust before individuals seek treatment. Accordingly, this study develops a quantitative model to explore how such proactive community engagement by primary care facilities, emphasizing preventative care, can mitigate this imbalance.

This article addresses the challenges of computation offloading and resource allocation in dynamic vehicular platoons, where high-speed mobility and sparse roadside unit (RSU) deployment lead to intermittent connectivity and complex task scheduling delays. This article proposes a rule-based reinforcement learning (RL) framework that jointly optimizes time-slotted task offloading and vehicle-to-everything (V2X) resource allocation while strictly adhering to energy consumption constraints, which reduces computational complexity while ensuring optimization accuracy. The framework integrates domain-specific rules—such as leader vehicle platoon leader (PL) capacity limits, RSU computation thresholds, and energy budgets—into the RL decision-making process to ensure feasible actions across four offloading scenarios: local computation, direct RSU offloading, relay-based RSU offloading, and PL offloading. The problem is formulated as a mixed-integer nonlinear programming (MINLP) model and decomposed into vehicle-level mode selection and computing unit-level resource allocation. Extensive simulations demonstrate the algorithm’s robustness in dynamic environments.

In recent years, there has been increased emphasis on the resilience of healthcare systems, particularly in light of the global COVID-19 pandemic. This article seeks to provide a comprehensive understanding of resilience in healthcare systems when faced with various disruptions. An exhaustive review of the existing literature is conducted to clarify how resilience is defined within healthcare systems and the variety of methods used to assess it, integrating both qualitative and quantitative approaches. Furthermore, the article introduces a novel perspective by categorizing resilience into three result-oriented disaster types. The review also critically examines several challenges and outlines future directions, aiming to promote more unbiased, unified, and practical approaches in the field of resilient healthcare systems.

As Internet technology undergoes rapid development and widespread adoption, e-commerce emerges as a pivotal component of the platform economy, permeating various facets of daily life. However, due to the influence of time, space, and other factors, the problem of integrity becomes severe in the real trading environment. As the platforms, sellers, and consumers are the main participants and their decision-making is restricted by historical experiences and contextual conditions, they exhibit constrained rationality. Utilizing evolutionary game theory, the study constructs a tripartite game model that analyses the influence of relevant parameters on the behavior of the participants. To deal with the behaviors of the participants, we built a simulation system on MATLAB to demonstrate the effects of beginning circumstances and associated parameter adjustments on the evolution outcomes for participants. Through theoretical analysis and numerical simulation analysis, we identify that the e-commerce platforms should standardize the good faith behavior of sellers by increasing the punishment, which can reduce the malicious return behavior of consumers. Sellers can mitigate the probability of fraud by improving production technology. Consumers can improve their learning to avoid returning products. This research provides a theoretical framework and decision support for e-commerce platforms, and it also promotes the long-term growth of online transactions.

Customers’ price comparison behavior and search cost evaluation play a pivotal role in shaping purchasing utility and enterprise pricing strategies. This study employs the multinomial logit (MNL) model to integrate the reference price and search cost into product line design. We probe into pure bundle and mixed bundle pricing decisions for the product line and value-added services in monopolistic settings and delve into the impacts of the reference price and search cost on pricing, profit, product variety, and strategy. Based on model analysis and numerical simulations, the results show that the reference price effect benefits low-priced offerings but undermines high-priced products, overall market share, and profit. As the reference price effect or search cost increases, product variety diminishes. In scenarios where the reference price effect and the proportion of bundled purchases are minimal, the pure bundling strategy is preferable; otherwise, the mixed bundling strategy is more advantageous. When enterprises overlook the reference price effect, there is an imbalance in the pricing of various products and services within the product line, which results in the actual market share and profit consistently falling below the ideal expectations.

The healthcare system is a complex organization designed to provide medical services, exhibiting heterogeneity due to economic, legal, and cultural differences. To effectively manage the resilience of healthcare systems, it is essential to model their working domains. Furthermore, healthcare systems at different levels have distinct structures and substance flow characteristics. In particular, at the departmental level, the substance flow is primarily patient-centric, while at the hospital level, it includes orders, bills, and report flows. Interhospital scenarios may involve case flows and healthcare personnel movements. Therefore, this article proposes a healthcare system domain modeling framework based on the function-context-behavior-principle-state-structure (FCBPSS) tool, outlining its application in three hierarchical healthcare system levels. Unlike FCBPSS applications in other systems engineering fields, this framework not only captures the system's semantics but also encompasses ethical rules and operational standards within the healthcare system, making it an ideal tool for healthcare system domain modeling. Finally, the model's effectiveness is validated through the optimization of a case study at Sheng-Jing Hospital in Shenyang.

The production-inventory system is a fundamental component of the supply chain, and inconsistencies in production rates arise from the flow of products from suppliers to retailers within the supply chain network. This article investigates the problem of dynamic control of inventory levels in a multitime-scale production-inventory system, the basic unit in a supply chain. First, the dynamic behavior of the production-inventory quantity of two different level factories is modeled by the ordinary differential equations. Second, the issue of inconsistent production rates among different factories is considered, and the singular perturbation theory is introduced to describe the phenomenon. In addition, to deal with the bullwhip effect caused by the variation of order demand and the phenomenon of inaccurate inventory levels caused by misplacing and theft, the$\mathscr{H}_{\infty}$control strategy is considered to improve the system's robustness. Then, the finite-time boundedness of the production-inventory system is analyzed by combining it with the Lyapunov control theory, and the theorem is given to ensure that the dynamic change of the factory inventory is bounded and controllable. Finally, the effectiveness of the proposed method is verified by a simulation example of the potassium carbonate production process in a chemical plant.

A platoon is a group of vehicles traveling in the same direction at a reasonable speed while maintaining a reasonable and safe distance. Robust model predictive control addresses vehicle stability, but evolving communication topology is a significant challenge for high-speed autonomous vehicles. A multi-hop communication model is proposed to improve communication quality within power and channel constraints, considering the Doppler effect and changes in vehicle spacing due to high speeds. This paper uses a Markov decision process to model communication and designs a reward function in reinforcement learning to enhance communication quality while reducing power usage. The efficiency of the learning process is enhanced by narrowing the search range for the signal transmission power of vehicles, informed by the characteristics of the communication model. The proposed model and algorithm’s effectiveness and feasibility are confirmed through thorough simulations.

The outpatient services in large-scale hospitals consist of multiple specialty departments, servers, and multiple stages. Single-server and small clinics have been extensively studied, but little attention has been paid to the modeling and evaluation of large-scale outpatient care systems. In this study, complex patient routings in large-scale outpatient systems are specified, and three patient flow variability factors (e.g., return rate, first-lab-visit rate, and second-lab-visit rate) are discussed. The measures, namely, patient satisfaction, resource utilization, and system efficiency, are based on the ratios of “effective time” over “holistic time.” The purpose of this study is to find the effect of patient flow variability and service time variability on the efficiency of a multistage outpatient system. A discrete-event simulation (DES) model is built, and a real case is presented. A novel method to generate the transition matrix based on patient flow variability is proposed. Our results show that the patient flow variability has a negative impact on patient satisfaction and a positive effect on resource utilization and system efficiency. Service time variability has a negative impact on resource utilization but has a combined effect on patient satisfaction and system efficiency with patient flow variability. The interactions between the two factors are thoroughly discussed.

Identifying critical department(s) to enhance the supply in large-scale systems is beneficial to support the system efficiency. Large-scale outpatient systems (LSOSs) are usually faced with crowdedness and, hence, require critical departmental improvement to maintain patient satisfaction under a limited budget. Besides, when demand surges (DSs) or physicians are absent [supply loss (SL)], critical department identification becomes one of the key steps to resilient clinical management. To improve the clinical services and mitigate the risk of disruptions efficiently, we conduct critical department analysis under three scenarios: one clinical improvement scenario [supply enhancement (SE)] and two clinical disruption scenarios (DS and SL). These scenarios can happen in different departments in varying time sessions (e.g., am or pm). We define the criticality of a department as the change of patient satisfaction with respect to the change of departmental supply and demand. We accordingly propose a simulation-based ranking method and implement a case study in an LSOS. The simulation results show that the criticality of the department highly depends on the time session. Surprisingly, SE may reduce patient satisfaction when the supply increases in several specific departments. Key findings and managerial insights are further discussed.

The road network serves as one of the most fundamental infrastructure systems for the society but is at risk from different types of disruptions. Due to the variety and unpredictability of disruptions, the road network has stochastic behavior, which refers to the state change of the network. In this paper, we propose an approach based on percolation theory to study the stochastic behavior of road networks impacted by disruptions. Two performance metrics, network connectivity and network efficiency, are defined to quantify the behavior of road networks. Instead of relying on detailed network structure, the proposed percolation theory-based approach uses only basic topology information, such as the distribution of node degrees and link capacities, which makes it suitable for large-scale networks. It is found that the efficiency of a disruption-impacted road network is the product of global connectivity, local connectivity, and connectivity strength. Validation by a real-world case shows that this new approach provides detailed and accurate evaluation of the behavior of disruption-impacted road networks.

Sequential strategies and integrated design and control (IDC) strategies have been developed to optimize engineering systems. Nevertheless, neither of them considers the impact of uncertainty when optimizing system performance. To improve the robustness of the system performance and ensure the systematic optimality, this article proposes an integrated robust design and robust control (IRDRC) strategy for a general engineering system using the genetic algorithm. The proposed IRDRC strategy is applied to a rocket flight attitude control system to test its effectiveness. The results show that the IRDRC strategy outperforms existing sequential strategies and IDC strategies, concerning system-level optimization and system robustness.

The use of Unmanned Aerial Vehicles (UAVs) in delivery logistics has become an efficient solution with the advancement of autonomous robotics. This paper proposes a novel mechanism that synchronizes drones and delivery trucks; particularly the case where trucks can work as mobile launching and retrieval sites. The problem is a Vehicle Routing Problem with Time Windows and Synchronized Drones. A multi-objective optimization model is developed with two conflicting objectives, minimizing the travel costs and maximizing the customer service level in terms of timely deliveries. A novel Collaborative Pareto Ant Colony Optimization algorithm is proposed to solve the model and Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to compare and validate the proposed algorithm. The experimental results indicate that the proposed mechanism is an efficient solution to parcel delivery logistics.

This paper presents a so-called multi-trailer drop-and-pull container drayage (MTDPCD) problem. The MTDPCD problem allows a tractor to carry multiple trailers and a tractor can leave the customer while the container is being packed / unpacked. This mode has been applied in real life and shown its great potential to improve transportation efficiency but not been reported in literature. We model the MTDPCD problem based on node decomposition as a nonlinear program and then linearize it. A backtracking adaptive threshold accepting algorithm is proposed to solve the problem. A named 3-vector procedure is designed to calculate the number of trailers and to check feasibility of solutions. A decoding algorithm including three stages is presented to adjust starting times of trucks given sequences of drayage orders. Experiments based on randomly generated instances validate the aforementioned methodology and indicate that pulling 2 trailers can save about 30% of operating costs than pulling 1 trailer.

This paper studies a novel sustainable ship routing problem considering a time window concept and bunker fuel management. Ship routing involves the decisions corresponding to the deployment of vessels to multiple ports and time window concept helps to maintain the service level of the port. Reducing carbon emissions within the maritime transportation domain remains one of the most significant challenges as it addresses the sustainability aspect. Bunker fuel management deals with the fuel bunkering issues faced by different ships, such as selection of bunkering ports and total bunkered amount at a port. A novel mathematical model is developed capturing the intricacies of the problem. A hybrid particle swarm optimization with a basic variable neighborhood search algorithm is proposed to solve the model and compared with the exact solutions obtained using Cplex and other popular algorithms for several problem instances. The proposed algorithm outperforms other popular algorithms in all the instances in terms of the solution quality and provides good quality solutions with an average cost deviation of 5.99% from the optimal solution.

The resilience of transportation systems has been extensively studied in the past decade. This paper aims to provide a synthesis of the up-to-date literature on resilient transportation, focusing on concepts and methodologies. A systematic literature search method integrating database search, related journal search, and citation supplement is proposed to select all the appropriate articles. Based on the selected core papers, the definition of resilience is examined, and some related concepts are compared. The main body of the paper is devoted to the review of metrics and mathematical models used to measure resilience and the strategies used to enhance resilience. Several popular subtopics are identified and discussed. Finally, current research gaps and challenges are addressed, and some potential research directions are presented.

Emergency fleet management has become one of the determinant success factors for post-disaster responses in urban flood control. However, it is challenging as multiple types of emergency vehicles are involved, and its performance is frequently threatened by the fluctuation of rescue demands and fleet capacity. Aiming at coping with the imbalances between rescue demands and vehicle supplies, and maintaining required service level of fleet management after flood occurs, this paper proposes a data-driven resilient fleet management solution under the context of cloud asset-enabled urban flood control. First, the problem of resilient fleet management is quantitatively defined, and then a data-driven dynamic management mechanism is proposed, which is highly effective on realizing resilient fleet management. Furthermore, considering the cooperation among different types of emergency vehicles, a greedy-based algorithm is proposed for resilient vehicle dispatching based on real-time scenarios. Finally, a simulation case is also conducted to verify the effectiveness and performance of the proposed solution.

A fundamental and important step for safety analysis for an urban transportation system is to find its critical links. However, most approaches in the current literature focused on highway or intercity transportation systems. The key characteristics of urban transportation systems were not considered and the concept of criticality of links was mixed up with the concept of vulnerability. This paper defines the criticality of links from two perspectives, i.e., the vulnerability and potential, based on which a novel methodology for identifying critical links in an urban transportation network is proposed. This novel methodology includes a ranking method and a novel mesoscopic model to examine the urban transportation network performance. The mesoscopic model is a novel cell transmission model, which grasps key characteristics of an urban transportation network, such as dynamic demand generated on links, different link lengths, and intersection flow assignment. The method is validated by a real world case from Hong Kong. The simulation results indicate that the ranking of critical links depends on particular scenarios, available resources, and both supply and demand of the system; furthermore, two paradoxes are discovered and discussed.

Contemporary information modeling of enterprise systems only focuses on the technical aspect of the systems, though it is known that they are social-technical (socio-tech) systems in essence. In fact, there are many lessons that can be learned from failures in the management of enterprise systems, which range from a small one (e.g., failure to install a printer driver) to a large one (e.g., nuclear power plant post-accident management). This paper, therefore, proposes that the enterprise system should be viewed as a socio-tech system. The paper presents a novel integrated approach to information modeling of socio-tech enterprise systems. In particular, the approach integrates object-orientation, systems-dynamics (as a means to represent high-level dynamics), and aspect-programming. The paper discusses an example to illustrate how the proposed approach works.

Evacuation planning with the contraflow technique is a complex planning problem. The problem is further complicated when more realistic situations such as evacuation priorities and the setup time for the contraflow operation are considered. Such a complex problem has yet to be discussed in the present literature. In this paper, we present a multiple-objective optimization model for this problem and a two-layer algorithm to solve this model. Experiments on three transportation networks with different network scales are presented to show the excellent performance of the proposed model and algorithm.

This paper presents our study on the emergency resource-planning problem, particularly on the development of a new approach to resource planning through contraflow techniques with consideration of the repair of damaged infrastructures. The contraflow technique is aimed at reversing traffic flows in one or more inbound lanes of a divided highway for the outbound direction. As opposed to the current literature, our approach has the following salient points: (1) simultaneous consideration of contraflow and repair of repair of roads; (2) classification of victims in terms of their problems and urgency in sending them to a safe place or place to be treated; and (3) consideration of multiple destinations for victims. A simulated experiment is also described by comparing our approach with some variations of our approach. The experimental results show that our approach can lead to a reduction in evacuation time by more than 50%, as opposed to the original resource operation on the damaged transportation network, and by about 20%, as opposed to the approach with resource replanning (only) on the damaged network. In addition, the multiobjective optimization algorithm to solve our model can be generalized to other network resource-planning problems under infrastructure damage.

Taking the characteristics of difficulty on exact measurement and complete expression of NGI (next generation Internet) into account, a flexible QoS multicast routing algorithm based on DE (differential evolution) is presented with introduction of principle of fuzzy mathematics. The corresponding model and its mathematical description are introduced. Under inaccurate information of QoS parameters and users' flexible QoS constraints, the proposed algorithm tries to find the multicast tree with maximum reliability degree and user's QoS satisfaction degree. Simulation results have shown that the proposed algorithm is both feasible and effective

Taking the characteristics of multi-constrained QoS (Quality of Service) routing in NGI (Next Generation Internet) into account, a hybrid intelligent multicast QoS routing algorithm based on PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) is presented. In this paper, the corresponding model and its mathematical description are introduced. Combining fast searching ability of PSO and global optimization ability of GA, the multi-constrained QoS (such as bandwidth, delay, delay jitter and error rate) multicast routing problem is solved. Simulation research and performance evaluation have been done over some actual and virtual network topologies. It has been shown that the proposed algorithm is both feasible and effective.