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Given the constraints of schedule and the uncertainty of time, the practical needs of operators regarding transportation modes and routing selection are more effectively addressed through the utilization of multiple modes for freight transportation within a transportation network. A bi-objective integer planning model has been developed, focusing on the transportation demand of the operator. The model considers the variability in travel time between nodes and the transfer time associated with various modes of transportation. Its objective is to minimize both total cost and time while treating the freight arrival time as a constraint. The uncertainty planning model is further elucidated through three criteria: optimistic, expected, and pessimistic. The varying risk attitudes of different operators inform these criteria. The modal is subsequently resolved using the Gurobi solver. This study utilizes the transportation demand of Lanzhou-Ningbo Zhoushan Port to create a series of multimodal transportation routings based on various weight combinations. It further examines the impacts of arrival time, schedule constraints, risk attitude, and departing time on the selection outcomes of transportation routing. The findings of this paper may serve as a reference for decision-making by multimodal transportation operators in the development of transportation plans. [ABSTRACT FROM AUTHOR]

Purpose – This study aims to examine the impact of the decision makers’ risk preference on logistics routing problem, contributing to logistics behavior analysis and route integration optimization under uncertain environment. Due to the unexpected events and complex environment in modern logistics operations, the logistics process is full of uncertainty. Based on the chance function of satisfying the transportation time and cost requirements, this paper focuses on the fourth party logistics routing integrated optimization problem considering the chance preference of decision makers from the perspective of satisfaction. Design/methodology/approach – This study used the quantitative method to investigate the relationship between route decision making and human behavior. The cumulative prospect theory is used to describe the loss, gain and utility function based on confidence levels. A mathematical model and an improved ant colony algorithm are employed to solve the problems. Numerical examples show the effectiveness of the proposed model and algorithm. Findings – The study’s findings reveal that the dual-population improvement strategy enhances the algorithm’s global search capability and the improved algorithm can solve the risk model quickly, verifying the effectiveness of the improvement method. Moreover, the decision-maker is more sensitive to losses, and the utility obtained when considering decision-makers' risk attitudes is greater than that obtained when the decision-maker exhibits risk neutrality. Practical implications – In an uncertain environment, the logistics decision maker’s risk preference directly affects decision making. Different parameter combinations in the proposed model could be set for decision-makers with different risk attitudes to fit their needs more accurately. This could help managers design effective transportation plans and improve service levels. In addition, the improved algorithm can solve the proposed problem quickly, stably and effectively, so as to help the decision maker to make the logistics path decision quickly according to the required confidence level. Originality/value – Considering the uncertainty in logistics and the risk behavior of decision makers, this paper studies integrated routing problem from the perspective of opportunity preference. Based on the chance function of satisfying the transportation time and cost requirements, a fourth party logistics routing integrated optimization problem model considering the chance preference of decision makers is established. According to the characteristics of the problem, an improved dual-population ant colony algorithm is designed to solve the proposed model. Numerical examples show the effectiveness the proposed methods.

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. Traditional routing protocols, such as OSPF or the Dijkstra algorithm, often fall short in handling the complexity, scalability, and dynamic nature of modern network environments, including unmanned aerial vehicle (UAV), satellite, and 5G networks. By leveraging their ability to model network topologies and learn from complex interdependencies between nodes and links, GNNs offer a promising solution for distributed and scalable routing optimization. This paper provides a comprehensive review of the latest research on GNN-based routing methods, categorizing them into supervised learning for network modeling, supervised learning for routing optimization, and reinforcement learning for dynamic routing tasks. We also present a detailed analysis of existing datasets, tools, and benchmarking practices. Key challenges related to scalability, real-world deployment, explainability, and security are discussed, alongside future research directions that involve federated learning, self-supervised learning, and online learning techniques to further enhance GNN applicability. This study serves as the first comprehensive survey of GNNs for routing optimization, aiming to inspire further research and practical applications in future communication networks. [ABSTRACT FROM AUTHOR]

Transport and distribution management is a crucial aspect of supply chain logistics, as it allocates resources to optimize delivery times for finished products. This study proposes a vehicle routing system to minimize delivery times for milk to customers and retailers in the dairy supply chain in Magangué and Mompóx, Department of Bolívar, Colombia. The research involves using mathematical models to optimize routes and the utilization of the vehicle fleet. Two mathematical vehicle routing models were developed: the first addresses the Traveling Salesman Problem (TSP) for a single vehicle. In contrast, the second model deals with the capacitated vehicle routing problem (CVRP) for multiple vehicles. Both models were solved using the IBM ILOG CPLEX Optimization software. The study successfully identified viable CVRP and TSP mathematical models for milk distribution, resulting in minimized delivery times to meet the demands of the TSP model. [ABSTRACT FROM AUTHOR]

The advancement of communication technologies has facilitated the deployment of numerous sensors, terminal human–machine interfaces, and smart devices in various complex environments for data collection and analysis, providing automated and intelligent services. The increasing urgency of monitoring demands in complex environments necessitates low-cost and efficient network deployment solutions to support various monitoring tasks. Distributed networks offer high stability, reliability, and economic feasibility. Among various Low-Power Wide-Area Network (LPWAN) technologies, Long Range (LoRa) has emerged as the preferred choice due to its openness and flexibility. However, traditional LoRa networks face challenges such as limited coverage range and poor scalability, emphasizing the need for research into distributed routing strategies tailored for LoRa networks. This paper proposes the Optimizing Link-State Routing Based on Load Balancing (LB-OLSR) protocol as an ideal approach for constructing LoRa distributed multi-hop networks. The protocol considers the selection of Multipoint Relay (MPR) nodes to reduce unnecessary network overhead. In addition, route planning integrates factors such as business communication latency, link reliability, node occupancy rate, and node load rate to construct an optimization model and optimize the route establishment decision criteria through a load-balancing approach. The simulation results demonstrate that the improved routing protocol exhibits superior performance in node load balancing, average node load duration, and average business latency. [ABSTRACT FROM AUTHOR]

In this work, a stochastic dynamic version of the container drayage problem is studied. The presented model incorporates uncertainty in the form of stochastic loading and unloading times at both terminals and customers, as well as stochastic travel times, conditionally dependent upon the departure time, allowing robust planning with respect to varying processing times. Moreover, the presented model is dynamic, allowing flexible orders and having the capability of re-solving the optimization problem in case of last-minute orders. Finally, the model also incorporates a truck appointment system operating at each terminal. First, a description of the general model is given, which amounts to a mixed integer non-linear program. In order to efficiently solve the optimization problem, and linearize both the objective and the conditional chance constraints, it is reformulated based on time window partitioning, yielding a purely integer linear program. As a test case, a large road carrier operating in the port of Antwerp is considered. We demonstrate that the model is efficiently solvable, even for instances of up to 300 orders. Moreover, the impact of incorporating stochastic information is clearly illustrated. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are challenged by the need for optimized Energy Consumption (EC), efficient Data Aggregation (DA), and reliable routing due to their dynamic topologies and limited resources. Existing solutions like TEAMR and DDQNDA address these concerns but face significant drawbacks--TEAMR lacks adaptability to rapidly changing topologies, while DDQNDA suffers from high computational overhead and delayed convergence, hindering its effectiveness in real-time scenarios. To overcome these limitations, this paper introduces the Adaptive Reinforcement Learning (RL)-Based DA and Routing Optimization (ARL-DARO) algorithm. The proposed methodology follows a systematic approach, beginning with cluster formation and Cluster Head (CH) selection (CHS) using the Grey Wolf Optimizer (GWO), which ensures Energy-Efficient (EE) clustering and optimal CH selection. In the next step, trust factors such as Node Connectivity (NC), Residual Trust (RT), and Cooperation Rate (CR) are integrated into Quality of Service (QoS) metrics as part of the Fitness Function(FF) to enhance route reliability and security. Finally, the ARL-DARO algorithm is employed to dynamically optimize both data aggregation and routing. It leverages Q-learning to select optimal routes based on energy efficiency, security, and link reliability, further reducing data redundancy and improving adaptability to realtime network changes. Performance is assessed using parameters such EC, packet delivery ratio (PDR), end-to-end latency (E2E delay), throughput, and network lifetime (NL) across networks with 100, 200, 300, 400, and 500 nodes. Results show that ARL-DARO significantly reduces energy consumption by up to 45%, increases throughput by 30%, and extends network lifetime, proving its effectiveness over existing methods. [ABSTRACT FROM AUTHOR]

Cyber-physical power system (CPPS) has significantly improved the operational efficiency of power systems. However, cross-space cascading failures may occur due to the coupling characteristics, which poses a great threat to the safety and reliability of CPPS, and there is an acute need to reduce the probability of these failures. Towards this end, this paper first proposes a cascading failure index to identify and quantify the importance of different information in the same class of communication services. On this basis, a joint improved risk-balanced service function chain routing strategy (SFC-RS) is proposed, which is modeled as a robust optimization problem and solved by column-and-constraint generation (C-CG) algorithm. Compared with the traditional shortest-path routing algorithm, the superiority of SFC-RS is verified in the IEEE 30-bus system. The results demonstrate that SFC-RS effectively mitigates the risk associated with information transmission in the network, enhances information transmission accessibility, and effectively limits communication disruption from becoming the cause of cross-space cascading failures. [ABSTRACT FROM AUTHOR]

Abstract The COVID-19 pandemic accelerated the shift towards online shopping, reshaping consumer habits and intensifying the impact on urban freight distribution. This disruption exacerbated traffic congestion and parking shortages in cities, underscoring the need for sustainable distribution models. The European Union's common transport policy advocates for innovative UFD approaches that promote intermodal transportation, reduce traffic, and optimize cargo loads. Our study addresses these challenges by proposing an agile routing algorithm for an alternative UFD model in Barcelona. This model suggests strategically located micro-hubs selected from a set of railway facilities, markets, shopping centers, district buildings, pickup points, post offices, and parking lots (1057 points in total). It also promotes intermodality through cargo bikes and electric vans. The study has two main objectives: (i) to identify a network of intermodal micro-hubs for the efficient delivery of parcels in Barcelona and (ii) to develop an agile routing algorithm to optimize their location. The algorithm generates adaptive distribution plans considering micro-hub operating costs and vehicle routing costs, and using heuristic and machine learning methods enhanced by parallelization techniques. It swiftly produces high-quality routing plans based on transportation infrastructure, transportation modes, and delivery locations. The algorithm adapts dynamically and employs multi-objective techniques to establish the Pareto frontier for each plan. Real-world testing in Barcelona, using actual data has shown promising results, providing potential scenarios to reduce CO2 emissions and improve delivery times. As such, this research offers an innovative and sustainable approach to UFD, that will contribute significantly to a greener future for cities.

The COVID-19 pandemic accelerated the shift towards online shopping, reshaping consumer habits and intensifying the impact on urban freight distribution. This disruption exacerbated traffic congestion and parking shortages in cities, underscoring the need for sustainable distribution models. The European Union's common transport policy advocates for innovative UFD approaches that promote intermodal transportation, reduce traffic, and optimize cargo loads. Our study addresses these challenges by proposing an agile routing algorithm for an alternative UFD model in Barcelona. This model suggests strategically located micro-hubs selected from a set of railway facilities, markets, shopping centers, district buildings, pickup points, post offices, and parking lots (1057 points in total). It also promotes intermodality through cargo bikes and electric vans. The study has two main objectives: (i) to identify a network of intermodal micro-hubs for the efficient delivery of parcels in Barcelona and (ii) to develop an agile routing algorithm to optimize their location. The algorithm generates adaptive distribution plans considering micro-hub operating costs and vehicle routing costs, and using heuristic and machine learning methods enhanced by parallelization techniques. It swiftly produces high-quality routing plans based on transportation infrastructure, transportation modes, and delivery locations. The algorithm adapts dynamically and employs multi-objective techniques to establish the Pareto frontier for each plan. Real-world testing in Barcelona, using actual data has shown promising results, providing potential scenarios to reduce CO2 emissions and improve delivery times. As such, this research offers an innovative and sustainable approach to UFD, that will contribute significantly to a greener future for cities. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of logistics distribution in urban environment, taking delivery time and delivery cost as optimization objectives, a mathematical model of “vehicle-drone”route optimization was established, and a third-order algorithm based on clustering-Floyd-genetic algorithm was proposed. The experimental results show that the algorithm can effectively reduce the computational load through multi-order data processing, and overcome the slow convergence rate of genetic algorithm, easy to fall into the local optimal problem. The sensitivity analysis of drone capacity shows that the delivery capability of drone increases significantly with the increase of load, and the simultaneous increase of load and maximum range can bring the delivery capability of drone into full play. Compared with the vehicle-only mode, the total cost and time of “ vehicle-drone” mode decrease by 36. 1% and 34. 5% respectively. It is proved that the algorithm has some practical value in city logistics distribution. [ABSTRACT FROM AUTHOR]

This paper investigates how artificial intelligence (AI) is making systems way better. It centers on making strides things like how information voyages, overseeing activity, finding programmers, and spotting bizarre exercises. By looking at later thinks about and real-world illustrations, we see that AI is much way better than more seasoned ways at making systems quicker, more secure, and more effective. AI models, particularly those utilizing profound learning and fortification learning, are extraordinary at making beyond any doubt information voyages perfect way" the most perfect way conceivable, taking care of active times on systems, and securing against cyber dangers. Real-life tests appear AI can decrease holding up times, speed up information, and make systems more secure totally different circumstances. These discoveries appear that AI is changing networks to be more intelligent and harder, which is able lead to more advancements in how we oversee and secure systems. [ABSTRACT FROM AUTHOR]

To effectively address the real-time updates of passenger demand orders on online carpooling platforms, this paper proposed an algorithm for continuously assigning orders to drivers with unfinished orders. The proposed algorithm ensured an increase in driver earnings while enhancing the dispatch efficiency of the carpooling platform. Specifically, this paper developed a double-layer scheduling model which maximized the total driver revenue on the basis of the service quality and operational costs of the carpooling system, and proposed a double-layer algorithm for this model. The bottom layer involved the construction of a model for the carpooling routing problem, which was solved by using an improved genetic algorithm. The upper layer determined the order of task assignments, which adopted the greedy algorithm to call the model in the botton layer and compared revenue changes to obtain the final scheduling result. The model demonstrated its effectiveness and feasibility through a specific example, validating the prompt determination of order matching results and travel routes. The calculated results faithfully reflect real-world scenarios. Comparative experiments shows that the model not only achieves the goal of increasing driver earnings, but also effectively reduces delay time and travel distances. This has positive reference significance for related studies on carpooling scheduling problems in the context of real-time order updates. [ABSTRACT FROM AUTHOR]

Order distribution and routing optimization of takeout delivery is a challenging research topic in the field of e-commerce. In this paper, we propose a drone–rider joint delivery mode with multi-distribution center collaboration for the problems of limited-service range, unreasonable distribution, high delivery cost, and tight time windows in the takeout delivery process. The model is constructed with the minimum delivery cost and the overall maximum customer satisfaction as the objective function, and a two-stage heuristic algorithm is designed to solve the model. In the first stage, Euclidean distance is used to classify customers into the regions belonging to different distribution centers, and the affinity propagation (AP) clustering algorithm is applied to allocate orders from different distribution centers. The second stage uses an improved tabu search algorithm for route optimization based on specifying the number of rider and drone calls. This paper takes China's Ele.me and Meituan takeout as the reference object and uses the Solomon data set for research. The experimental results show that compared with the traditional rider delivery mode, the drone–rider joint delivery mode with multiple distribution center collaboration can effectively reduce the number of riders used, lower the delivery cost, and improve the overall customer satisfaction. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Order distribution and routing optimization of takeout delivery is a challenging research topic in the field of e-commerce. In this paper, we propose a drone–rider joint delivery mode with multi-distribution center collaboration for the problems of limited-service range, unreasonable distribution, high delivery cost, and tight time windows in the takeout delivery process. The model is constructed with the minimum delivery cost and the overall maximum customer satisfaction as the objective function, and a two-stage heuristic algorithm is designed to solve the model. In the first stage, Euclidean distance is used to classify customers into the regions belonging to different distribution centers, and the affinity propagation (AP) clustering algorithm is applied to allocate orders from different distribution centers. The second stage uses an improved tabu search algorithm for route optimization based on specifying the number of rider and drone calls. This paper takes China’s Ele.me and Meituan takeout as the reference object and uses the Solomon data set for research. The experimental results show that compared with the traditional rider delivery mode, the drone–rider joint delivery mode with multiple distribution center collaboration can effectively reduce the number of riders used, lower the delivery cost, and improve the overall customer satisfaction.

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The integration of power grids and communication networks in smart grids enhances system safety and reliability but also exposes vulnerabilities to network attacks, such as Denial-of-Service (DoS) attacks targeting communication networks. A multi-index evaluation approach is proposed to optimize routing modes in integrated energy cyber-physical systems (IECPS) considering potential failures from attacks. Security and economic service evaluation indexes are incorporated to quantify the significance of information flow routing. An optimization model for electric, heat, and gas routing in worst-case scenarios is formulated and solved using a column and constraint generation algorithm. The optimized routing method effectively circumvents specified attack areas, reducing the correlation degree of communication links within the attack area. Comparison with single-service optimization methods demonstrates the superiority of the proposed approach in mitigating the impact of network attacks on IECPS. The study highlights the importance of considering security and economic factors in optimizing routing modes to enhance the resilience of integrated energy cyber-physical systems against network attacks, particularly DoS attacks on communication networks. The evaluation index approach presented in this study provides a comprehensive method for assessing the importance of communication links in IECPS and optimizing routing modes to improve system robustness and reliability in the face of network attacks. [ABSTRACT FROM AUTHOR]

Connected and autonomous vehicles (CAVs), with their potential to enhance the interactive perception of vehicle behavior, are expected to benefit traffic congestion and travel efficiency. However, the research scenarios in most current literature are oversimplified and limited, such as a road section or an intersection. To address this issue, this paper proposes a congestion avoidance routing strategy for CAVs to reduce the occurrence and propagation of congestion at the network level. Unlike rerouting after detecting the congestion downstream, the floating-car data are utilized to extract the network congestion patterns, based on which the routes of CAVs are optimized and updated. A simulation framework was built to model the network consisting of CAVs and human-driven vehicles (HDVs). Cooperative adaptive cruise control (CACC) and intelligent driver model (IDM) car-following models were set to characterize the driving behavior of CAVs and HDVs. Simulation experiments were conducted to examine the performance of the proposed routing strategy. The results indicate that the proposed CAV routing strategy can significantly improve the overall congestion state of the network. Compared with the full HDV environment, the vehicles' average delay can be reduced by up to 46.7% and the travel time by up to 28.2% if all vehicles are switched to CAVs. The sensitivity analysis on CAV penetration rate and vehicle inflow rate shows that the vehicles' average delay and travel time decreases with the CAV penetration rate increase, and the travel efficiency of CAVs outperforms HDV users sufficiently. Moreover, the benefits of CAVs would be weakened with the increase in vehicle inflow rates. Finally, the findings also provide a reference for CAVs' centralized control strategy in urban intelligent transportation construction. [ABSTRACT FROM AUTHOR]

As an innovative public transport, the customized bus has rapidly grown. To improve the efficiency of customized bus and satisfy customers' personal requirement, a mixed-integer programming model is proposed to optimize the stop assignment for customers and route scheduling for buses with walking distance constraints and travel time constraints. As an variants of vehicle routing problem, the formulated model on large-scale instances is intractable to solve for commercial solvers. Therefore, an exact algorithm based on branch and price is developed to solve the model, in which a labeling algorithm is designed for the pricing problems. Numerical experiments and a real-world case in Dalian are conducted to validate the effectiveness of the proposed model. The computational results show that the tailored algorithm can yield an optimal solution within a significantly shorter time than that of CPLEX. Some managerial implications are also obtained based on sensitivity analysis, which may be potentially useful for bus companies. [ABSTRACT FROM AUTHOR]

The effective disposal of healthcare waste is a highly concerned issue. Healthcare waste collection poses transport risks and temporary storing risks, and the deliberation undertaken by decision-makers concerning waste collection entails potential scenario wherein some hospitals have not yet submitted their service requests and may submit their requests during the healthcare waste collection procedure. A routing optimisation problem for healthcare waste collection with temporary storing risks and sequential uncertain service requests is introduced. A two-stage decision-making is proposed and mathematical models corresponding to each stage are developed. Different solution algorithms are developed for different stages or different scales of instances, including the improved $\varepsilon $ -constraint method and Non-dominated Sorting Genetic Algorithm-II for the solution procedure in Stage 1, and the Compare-choose-move algorithm for the solution procedure in Stage 2. Finally, the models and algorithms are tested by numerical instances and several suggestions for healthcare waste collection have been proposed based on sensitivity analysis.

The Internet of Things (IoT) is an emerging technology that allow smart devices to communicate through various heterogeneous channels (wired or wireless). However, for conventional networks, it has become a challenging task to efficiently control and manage the data flows of a huge number of devices. Software-defined networking (SDN) is a new way of thinking about networking. Because it is programmable, flexible, agile, and gives you a big picture of the network, it has tried to solve some IoT problems, like scalability, heterogeneity, and complexity. In large-scale SDN-IoT networks, there is a requirement for routing protocols that are both efficient and secure in order to ensure a superior level of quality of service (QoS) and quality of experience (QoE). To address the above stated challenges, a novel deep reinforcement learning (DRL) known as DQQS model is proposed. The aim is to achieve QoS and QoE while also ensuring the security of the SDN-IoT network. The proposed DQQS model dynamically extracts patterns from the past network history by interacting with the underlying network and generating optimized routing policies. This article employs three network metrics—throughput, latency, and the probability of avoiding malicious nodes—to measure the performance of DQQS. Simulations reveal that the proposed framework outperforms four state-of-the-art routing algorithms: OSPF, L-L Routing, Sailfish Routing, and RL-Routing in terms of both throughput and latency. For instance, in an attacked environment, the proposed DQQS model achieved the highest throughput value of 14.5 Mbps, surpassing OSPF at 8 Mbps, L-L at 8.2 Mbps, Sailfish at 9 Mbps, and RL at 9.5 Mbps. Similarly, this model exhibited superior performance in latency, recording the lowest latency value of 52 ms, compared to OSPF 88 ms, L-L 85 ms, Sailfish 72 ms, and RL 75 ms routing algorithms. The experimental results demonstrate that this new DQQS model is a pioneering deep reinforcement learning-based technique that optimally addresses secure routing in the SDN-IoT environment, ensuring enhanced quality of service and experience, and outperforming state-of-the art DL methodologies in both security and network performance metrics.

Electric vehicle (EV) charging spots require frequent inspections to identify potential safety hazards, and charging piles in charging spots exhibit a significant failure rate, necessitating maintenance. The efficiency and quality of inspection-maintenance are crucial components in the operation and management of charging spots, directly affecting the operation cost, service level, and user experience. The inspection-maintenance of EV charging spots requires two different types of personnel, and the impact on customer satisfaction cannot be ignored. In addition, the large-scale charging spots have further increased the difficulty of routing. A novel routing optimization problem for large-scale inspection-maintenance of EV charging spots is proposed in this paper, considering multiple types of personnel and composite customer satisfaction. While inspections can be carried out by general personnel, maintenance requires professional engineers. Composite customer satisfaction is with respect to the waiting time for maintenance of faulty charging spots and the duration of absence of professional engineers in the working station. A bi-objective routing optimization model is proposed, minimizing the cost of arranging personnel and traveling between charging spots, and the loss of composite customer satisfaction simultaneously. Due to the large scale of the problem caused by the enormous number of charging spots, an effective multi-stage solution approach is proposed. Finally, numerical experiments, managerial insights, and a case study are provided. The purpose of this research is to help the operators to decide the routes for inspection-maintenance of EV charging spots and offer valuable guidance in balancing operational efficiency with customer satisfaction.

Wireless sensor network (WSN) technology is becoming increasingly crucial in the context of the Fourth Industrial Revolution. This has led to the development of various protocols aimed at enhancing energy efficiency, with LEACH, PEGASIS, and EEACP being key among them. Despite these advancements, the EEACP protocol exhibits significant limitations in its current form, particularly regarding energy expenditure during data transmission phases. A primary inefficiency arises from suboptimal path configurations during data reception, which can severely impact the network’s longevity and effectiveness. To address these challenges, the newly proposed Advanced EEACP (O3EACP) protocol introduces strategic optimizations in data routing toward the sink node. This refinement aims to minimize energy drain during transmissions, thereby extending the operational lifespan of the network and bolstering its overall resilience. This enhancement not only promises a reduction in energy consumption but also a notable improvement in network sustainability, essential for supporting the robust, interconnected frameworks envisioned in the latest industrial revolution.

In the process of long-distance and large-volume transportation of hazardous materials (HAZMAT), multimodal trans-portation plays a crucial role with its unique advantages. In order to effectively reduce the transportation risk and improve the reliability of transportation, it is particularly important to choose a suitable transportation plan for multimodal transport of HAZMAT. In this paper, we study the transportation of HAZMAT in multimodal transport networks. Considering the fluctuation in demand for HAZMAT during the actual transportation process, it is difficult for decision makers to obtain the accurate demand for HAZMAT orders in advance, leading to uncertainty in the final transportation plan. Therefore, in this paper, the uncertain demand of HAZMAT is set as a triangular fuzzy random number, and a multi-objective mixed integer linear programming model is established with the objective of minimizing the total risk exposure population and the total cost in the transportation process of HAZMAT. In order to facilitate the solution of the model, we combined the fuzzy random expected value method with the fuzzy random chance constraint method based on credibility measures to reconstruct the uncertain model clearly and equivalently, and designed a non-dominated sorting genetic algorithm (NSGA-) to obtain the Pareto boundary of the multi-objective optimization problem. Finally, we conducted a numerical example experiment to verify the rationality of the model proposed in this paper. The experimental results indicate that uncertain demand can affect the path decision-making of multimodal transportation of HAZMAT. In addition, the confidence level of fuzzy random opportunity constraints will have an impact on the risk and economic objectives of optimizing the multimodal transportation path of HAZMAT. When the confidence level is higher than 0.7, it will lead to a significant increase in transportation risks and costs. Through sensitivity analysis, it can provide useful decision-making references for relevant departments to formulate HAZMAT transportation plans.

The integration of power grids and communication networks in smart grids enhances system safety and reliability but also exposes vulnerabilities to network attacks, such as Denial-of-Service (DoS) attacks targeting communication networks. A multi-index evaluation approach is proposed to optimize routing modes in integrated energy cyber-physical systems (IECPS) considering potential failures from attacks. Security and economic service evaluation indexes are incorporated to quantify the significance of information flow routing. An optimization model for electric, heat, and gas routing in worst-case scenarios is formulated and solved using a column and constraint generation algorithm. The optimized routing method effectively circumvents specified attack areas, reducing the correlation degree of communication links within the attack area. Comparison with single-service optimization methods demonstrates the superiority of the proposed approach in mitigating the impact of network attacks on IECPS. The study highlights the importance of considering security and economic factors in optimizing routing modes to enhance the resilience of integrated energy cyber-physical systems against network attacks, particularly DoS attacks on communication networks. The evaluation index approach presented in this study provides a comprehensive method for assessing the importance of communication links in IECPS and optimizing routing modes to improve system robustness and reliability in the face of network attacks.

With the increase in global wireless traffic, the use of large-scale satellite networking to provide ubiquitous access is one of the essential trends of future 6G network development. Elastic optical satellite networks (EOSNs) are widely considered a flexible solution for future satellite communication. However, with the continuous proliferation of network devices and users, the growing disparity between user demands and the limited bandwidth and capacity of the network is becoming increasingly noticeable. This has led to issues such as constrained network resource utilization and resource fragmentation. Therefore, EOSNs must efficiently address the challenge of allocating scarce bandwidth resources. Effective traffic grooming methods will be applied to EOSNs to solve the problem of bandwidth shortage. This paper proposed a dynamic traffic grooming algorithm based on virtualization-plane-aided optimization (DTG-VPO) to facilitate the bandwidth allocation for EOSNs. Firstly, the nodes of the alternative paths were graded, and the weights of the subsequent hop links were modified. Then, the path was evaluated using link weights, alternative paths were selected in the virtual and physical topologies, respectively, and a path set was constructed. Finally, a resource block evaluation parameter was designed to quantify the quality of candidate resource blocks and rank them. A series of simulations have evaluated the traffic-blocking probability and wavelength utilization under different traffic loads. The link resource was more fully utilized compared with other traffic grooming algorithms. The blocking probability can be reduced by 75%, while wavelength utilization can be improved by 8.1%. [ABSTRACT FROM AUTHOR]

In order to analyze the effect of pipeline-soil action on the gas pipeline crossing under the soil landslide, the pipeline route selection in the landslide affected area was discussed. Based on smooth particle hydrodynamics and finite element coupling method (SPH-FEM), the force analysis model of pipeline under landslide was established. From the design point of view, the position of pipeline laying was comprehensively considered, the influence of pipeline wall thickness, buried depth and other factors was discussed, and the force deformation characteristics of pipeline under large soil deformation were analyzed. Based on the results, some suggestions are provided for pipeline routing optimization in landslide area. Results show that for the thrust-type landslide, when the pipeline is laid in the sliding mass, the stress and displacement of the pipeline at the leading edge of the sliding mass are small, and the damage of the pipeline at the posterior border is more likely. When the thickness of the pipeline wall is increased, the bearing capacity of the pipeline can be effectively improved, but with the increase of the buried depth, the effective yield strain of the pipeline increases first and then decreases. When the pipeline is laid outside the landslide, the stress of the pipeline located at the leading edge of the landslide is less than that at the posterior border, and it presents different stress states, which meet the safety of operation. At the same time, the increase of wall thickness can reduce the stress of the pipeline. The conclusions can provide suggestions for pipeline routing optimization in landslide area in the design stage, which has certain engineering significance and application value. [ABSTRACT FROM AUTHOR]

The routing optimization of yard trailers in container terminal is an important issue for vehicle dispatching. Whether the vehicle dispatch is reasonable will determine the circulation efficiency of the container terminal. In order to better optimize the yard trailers routing under stochastic demand, a two-stage optimization model is established in this paper considered the constraint of vehicle capacity and travel distance, which aims at planning and determining the travel routing of the trailers and the operating sequence. To solve the two-stage optimization model, a particle swarm optimization algorithms (PSO) algorithm with exponentially decreasing weight strategy is introduced to search for a satisfied solution to ensure the feasibility of distribution plan and vehicle routing. And the optimal results of the simulation experimental reveal that satisfied solutions can be obtained by employing the two-stage optimization model constructed in this paper, which further verified the feasibility and validity of the optimization model and algorithm. The two-stage optimization model provides a practical and effective method to solve the trailers routing dispatching in container terminals under stochastic demand. [ABSTRACT FROM AUTHOR]

Traffic Engineering (TE) enables management of traffic in a manner that optimizes utilization of network resources in an efficient and balanced manner. However, existing TE solutions face issues relating to scalability and complexity. In recent years, Segment Routing (SR) has emerged as a promising source routing paradigm. As one of the most important applications of SR, Segment Routing Traffic Engineering (SR-TE), which enables a headend to steer traffic along specific paths represented as ordered lists of instructions called segment lists, has the capability to overcome the above challenges due to its flexibility and scalability. In this paper, we conduct a comprehensive survey on SR-TE. A thorough review of SR-TE architecture is provided in the first place, reviewing the core components and implementation of SR-TE such as SR Policy, Flexible Algorithm and SR-native algorithm. Strengths of SR-TE are also discussed, as well as its major challenges. Next, we dwell on the recent SR-TE researches on routing optimization with various intents, e.g., optimization on link utilization, throughput, QoE (Quality of Experience) and energy consumption. Afterwards, node management for SR-TE are investigated, including SR node deployment and candidate node selection. Finally, we discuss the existing challenges of current research activities and propose several research directions worth of future exploration.

This research paper presents an integrated emarketplace solution for the coconut industry, aiming to match crop supply efficiently. The system combines a coconut quality grading system using image processing, registration of farmers and collectors, algorithmic matching of buyers and sellers, supply visualization on a map, vehicle routing optimization, and a machine learning-based pricing and trend analysis dashboard. By integrating these functionalities, the solution enhances efficiency, transparency, and profitability in the coconut industry. This research contributes to advancing the industry in Sri Lanka by providing a comprehensive platform for seamless transactions, optimized transportation, and data-driven decision making. The proposed e-marketplace offers a holistic approach to connect stakeholders, streamline operations, and enable informed decision-making for sustainable growth in the coconut sector. [ABSTRACT FROM AUTHOR]

For the low-carbon multimodal transportation planning problem with fuzzy demands and carbon emission limit, this paper proposed a multi-objective multimodal transportation mathematical model to minimize the path cost and carbon emission. Firstly, it used the chance constrained programming to deal with the uncertain demand represented by trapezoidal fuzzy number according to the characteristics of the model. Secondly, it designed the path relinking algorithm, multiple crossover operators, and mutation operators to replace the search process of original Harris hawks optimizer algorithm. It successfully applied the algorithm to the discrete optimization problems on the premise of preserving the original characteristics of this algorithm. Finally, it studied the multimodal transportation from Nanning city to Harbin city as a case, which gave multiple reasonable route scheme. HHHO was compared with other algorithms. The results show that HHHO, NSGA-Ⅱ, GA, SA and PSO all obtain a set of near-optimal solutions with five solutions in an ideal time, and the solution set of HHHO is closer to the optimal solution set. The running time of HHHO and the other four algorithms are 86.50 s, 118.26 s, 101.67 s, 81.22 s and 68.40 s, respectively. HHHO is faster than GA and NSGA-Ⅱ in running time. Therefore, these results verify the feasibility of the model and the effectiveness of hybrid Harris hawks optimizer algorithm. [ABSTRACT FROM AUTHOR]

Currently, existing research on deploying deep reinforcement learning on software-defined networks (SDN) to achieve route optimization does not consider the network's spatial–temporal correlation globally and has yet to reach the ultimate in performance. Given the above issues, this study proposes a Proximal Policy Optimization algorithm based on the Attention mechanism and Spatio–Temporal correlation (ASTPPO) to optimize the SDN routing issue. First, we extract temporal and spatial correlation features in state information using Gated Recurrent Units (GRU) and Graph Attention Networks (GAT), providing implicit information containing more environments for reinforcement learning decisions. Second, we use the skip-connect method to connect implicit and directly related information into a multi-layer perceptron, improving the model's learning efficiency and perceptual ability. Finally, we demonstrate the effectiveness of ASTPPO through static and dynamic traffic experiments. Benefitting from Spatio–Temporal correlation learning with a global view, ASTPPO performs better load balancing and congestion control under different traffic intensity requirements and network topologies than other reinforcement learning baseline algorithms. The simulation results show that the ASTPPO algorithm improved by 9.02% and 15.07%, respectively, compared with the second-best algorithm in static and dynamic traffic scenarios. [ABSTRACT FROM AUTHOR]

With the advancement of space technology and satellite communications, low-Earth-orbit (LEO) satellite networks have experienced rapid development in the past decade. In the vision of 6G, LEO satellite networks play an important role in future 6G networks. On the other hand, a variety of applications, including many delay-sensitive applications, are continuously emerging. Due to the highly dynamic nature of LEO satellite networks, supporting time-deterministic services in such networks is challenging. However, we can provide latency guarantees for most delay-sensitive applications through data plane traffic shaping and control plane routing optimization. This paper addresses the routing optimization problem for time-sensitive (TS) flows in software-defined low-Earth-orbit (LEO) satellite networks. We model the problem as an integer linear programming (ILP) model aiming to minimize path handovers and maximum link utilization while meeting TS flow latency constraints. Since this problem is NP-hard, we design an efficient longest continuous path (LCP) approximation algorithm. LCP selects the longest valid path in each topology snapshot that satisfies delay constraints. An auxiliary graph then determines the routing sequence with minimized handovers. We implement an LEO satellite network testbed with Open vSwitch (OVS) and an open-network operating system (ONOS) controller to evaluate LCP. The results show that LCP reduces the number of path handovers by up to 31.7% and keeps the maximum link utilization lowest for more than 75% of the time compared to benchmark algorithms. In summary, LCP achieves excellent path handover optimization and load balancing performance under TS flow latency constraints. [ABSTRACT FROM AUTHOR]

Routing systems mechanisms have piqued researchers' interest in heuristic components for routing problems that could alter problem complexity. As a result, myriad routing strategies were proposed that minimize deployment costs while maximizing traversal coverage. Several constraints were considered, including deployment times, load capacities, and projected coverage. Research into routing systems has focused on heuristics to optimize complex routing problems. Multiple strategies have been proposed to optimize deployment costs and maximize route coverage, focusing on deployment times, load capacities, and coverage. This literature study examines data interpolation for cost optimization features coupled with relative scheduling systems, with the primary purpose of supporting heterogeneity subjugation towards cost interoperability based on varied goals and objective functions. A total of 250 papers were analyzed for relevance regarding routing scheduling from relevant academic-based user-accessed scientific journal databases such as Scopus, Web of Science, Hindawi, ACM, and Google Scholar to perform a concise analysis of the relative cost interoperability measures in routing strategies, including single objective purposes undertakings. The research evaluated the application, niche problem-solving methodologies, and viability for future refinement or integration with comparable solutions. This qualitative study aims to present an information synthesis based on the PRISMA (Systematic Literature Review) framework on various recognized developments and trends for routing heuristic research works that will serve as a benchmark for refining improvisation on current solution strategies. Ultimately, this study presents a comprehensive review of the applicable field, an analysis of existing problem-solving strategies, and a comprehensive overview of the possibilities for incorporating them into further research. [ABSTRACT FROM AUTHOR]

With the rapid development of satellite communication, the satellite internet is one of the core technologies of 6G network to realize global coverage, full-time access and full scene service.The high dynamics and limited capacity of satellite network lead to a series of management and control challenges such as heterogeneous network management, dynamic resource allocation and so on.Since the machine learning-based technologies have strength in network design, the intelligent architecture of software-defi ned satellite internet was put forward.In view of the intelligent routing in satellite internet, and leverages the deep reinforcement algorithm based on double delayed deep deterministic policy gradient (TD3) to solve the network routing optimization problem.The experimental results showed that compared with DDPG algorithm, the TD3 algorithm reduced the delay by 19.19%.