ROUTE OPTIMIZING ALGORITHM OF AIRPORT SURFACE BASED ON GIS

The GIS technique is used for airport surface management to study the optimization of airplane taxiway for arrival and departure flights. The shortest paths are designed for just-arrived and ready-for-departing flights of the airport. Additionally, whether the flights could confront each other head-to-head on the taxiway is judged. In regard to the airport′s security and efficiency, airplanes must continuously taxi along the shortest route and the head-to-head confrontation should not occur. Two schemes are designed: One is to change the taxiing velocity of arrival flights, the other is to delay the starting time of departure flights. This algorithm is approved by a practical example.

Optimization of Planning Routes in Solid Waste Collection

This paper presents an optimization model for solving the planning problem of collection and transportation of solid waste in medium-sized cities. As final results, are expected to promote cost savings to the public coffers, as well as environmental benefits. The developed mathematical model is formulated as a problem of linear programming with mixed-integer variables and transcribed into software GAMS （general algebraic modeling system）. The practical application was tested using data collected in the central region of a Brazilian city with approximately 90,000 inhabitants. The deterministic model used allowed an optimal solution. It was found after inclusion of restrictions that eliminated the appearance of sub-routes. It was concluded that the optimal routes allow for a 38% reduction in total distance traveled, which can generate savings of $320.00 per day regarding maintenance and fuel trucks.

QoS Routing Optimization Based on Deep Reinforcement Learning in SDN

To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront thechallenge of managing the surging demand for data traffic. Within this realm, the network imposes stringentQuality of Service (QoS) requirements, revealing the inadequacies of traditional routing allocation mechanismsin accommodating such extensive data flows. In response to the imperative of handling a substantial influx of datarequests promptly and alleviating the constraints of existing technologies and network congestion, we present anarchitecture forQoS routing optimizationwith in SoftwareDefinedNetwork (SDN), leveraging deep reinforcementlearning. This innovative approach entails the separation of SDN control and transmission functionalities, centralizingcontrol over data forwardingwhile integrating deep reinforcement learning for informed routing decisions. Byfactoring in considerations such as delay, bandwidth, jitter rate, and packet loss rate, we design a reward function toguide theDeepDeterministic PolicyGradient (DDPG) algorithmin learning the optimal routing strategy to furnishsuperior QoS provision. In our empirical investigations, we juxtapose the performance of Deep ReinforcementLearning (DRL) against that of Shortest Path (SP) algorithms in terms of data packet transmission delay. Theexperimental simulation results show that our proposed algorithm has significant efficacy in reducing networkdelay and improving the overall transmission efficiency, which is superior to the traditional methods.

Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Risk-Balanced Routing Strategy for Service Function Chains of Cyber-Physical Power System Considering Cross-Space Cascading Failure

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.

ERCP for patients who have undergone Billroth Ⅱ gastroenterostomy and Braun anastomosis

Endoscopic retrograde cholangiopancreatography (ERCP) is efficacious in patients who have undergone Billroth II gastroenterostomies, but the success rate decreases in patients who also have experienced Braun anastomoses. There are currently no reports describing the preferred enterography route for cannulation in these patients. We first review the patient’s previous surgery records, which most often indicate that the efferent loop is at the greater curvature of the stomach. We recommend extending the duodenoscope along the greater curvature of the stomach and then advancing it through the “lower entrance” at the site of the gastrojejunal anastomosis, along the efferent loop, and through the “middle entrance” at the site of the Braun anastomosis to reach the papilla of Vater. Ten patients who had each undergone Billroth II gastroenterostomy and Braun anastomosis between January 2009 and December 2011 were included in our study. The overall success rate of enterography was 90% for the patients who had undergone Billroth II gastroenterostomy and Braun anastomosis, and the therapeutic success rate was 80%. We believe that this enterography route for ERCP is optimal for a patient who has had Billroth II gastroenterostomy and Braun anastomosis and helps to increase the success rate of the procedure.

Retrieval-balloon-assisted enterography for ERCP after Billroth Ⅱ gastroenterostomy and Braun anastomosis

AIM: To describe an optimal route to the Braun anastomosis including the use of retrieval-balloon-assisted enterography.METHODS: Patients who received a Billroth Ⅱ gastroenterostomy(n = 109) and a Billroth Ⅱ gastroenterostomy with Braun anastomosis(n = 20) between January 2009 and May 2013 were analyzed in this study. Endoscopic ret-rograde cholangiopancreatography(ERCP) was performed under fluoroscopic control using a total length of 120 cm oblique-viewing duodenoscope with a 3.7-mm diameter working channel. For this procedure, we used a triplelumen retrieval balloon catheter in which a 0.035-inch guidewire could be inserted into the "open-channel" guidewire lumen while the balloon could be simultaneously injected and inflated through the other 2 lumens.RESULTS: For the patients with Billroth Ⅱ gastroenterostomy and Braun anastomosis, successful access to the papilla was gained in 17 patients(85%) and there was therapeutic success in 16 patients(80%). One patient had afferent loop perforation, but postoperative bleeding did not occur. For Billroth Ⅱ gastroenterostomy, there was failure in accessing the papilla in 15 patients(13.8%). ERCP was unsuccessful because of tumor infiltration(6 patients), a long afferent loop(9 patients), and cannulation failure(4 patients). The papilla was successfully accessed in 94 patients(86.2%), and there was therapeutic success in 90 patients(82.6%). Afferent loop perforation did not occur in any of these patients. One patient had hemorrhage 2 h after ERCP, which was successfully managed with conservative treatment.CONCLUSION: Retrieval-balloon-assisted enterography along an optimal route may improve the ERCP success rate after Billroth Ⅱ gastroenterostomy and Braun anastomosis.

Load Balancing-Based Routing Optimization Mechanism for Power Communication Networks

In power communication networks, it is a challenge to decrease the risk of different services efficiently to improve operation reliability. One of the important factor in reflecting communication risk is service route distribution. However, existing routing algorithms do not take into account the degree of importance of services, thereby leading to load unbalancing and increasing the risks of services and networks. A routing optimization mechanism based on load balancing for power communication networks is proposed to address the abovementioned problems. First, the mechanism constructs an evaluation model to evaluate the service and network risk degree using combination of devices, service load, and service characteristics. Second, service weights are determined with modified relative entropy TOPSIS method, and a balanced service routing determination algorithm is proposed. Results of simulations on practical network topology show that the mechanism can optimize the network risk degree and load balancing degree efficiently.

Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

An analytical method to calculate station evacuation capacity

The major objective of this work was to calculate evacuation capacity and solve the optimal routing problem in a given station topology from a network optimization perspective where station facilities were modelled as open finite queueing networks with a multi-objective set of performance measures. The optimal routing problem was determined so that the number of evacuation passengers was maximized while the service level was higher than a certain criterion. An analytical technique for modelling open finite queueing networks, called the iteration generalized expansion method(IGEM), was utilized to calculate the desired outputs. A differential evolution algorithm was presented for determining the optimal routes. As demonstrated, the design methodology which combines the optimization and analytical queueing network models provides a very effective procedure for simultaneously determining the service level and the maximum number of evacuation passengers in the best evacuation routes.

Research on the Optimization and Simulation of the Shortest Path Based on Algorithm of Dijkstra

Dijkstra algorithm is a theoretical basis to solve transportation network problems of the shortest path, which has a wide range of application in path optimization. Through analyzing traditional Dijkstra algorithm,on account of the insufficiency of this algorithm in path optimization,this paper uses adjacency list and circular linked list with combination to store date,and through the improved quick sorting algorithm for weight sorting, accomplish a quick search to the adjacent node,and so an improved Dijkstra algorithm is got.Then apply it to the optimal path search,and make simulation analysis for this algorithm through the example,also verify the effectiveness of the proposed algorithm.

Energy optimal routing for long chain-type wireless sensor networks in underground mines

Wireless sensor networks are useful complements to existing monitoring systems in underground mines. They play an important role of enhancing and improving coverage and flexibility of safety monitoring systems.Regions prone to danger and environments after disasters in underground mines require saving and balancing energy consumption of nodes to prolong the lifespan of networks.Based on the structure of a tunnel,we present a Long Chain-type Wireless Sensor Network（LC-WSN）to monitor the safety of underground mine tunnels.We define the optimal transmission distance and the range of the key region and present an Energy Optimal Routing（EOR）algorithm for LC-WSN to balance the energy consumption of nodes and maximize the lifespan of networks.EOR constructs routing paths based on an optimal transmission distance and uses an energy balancing strategy in the key region.Simulation results show that the EOR algorithm extends the lifespan of a network,balances the energy consumption of nodes in the key region and effectively limits the length of routing paths,compared with similar algorithms.

Route optimization model for pedestrian evacuation in metro hubs

The route optimization problem for road networks was applied to pedestrian flow.Evacuation path networks with nodes and arcs considering the traffic capacities of facilities were built in metro hubs,and a path impedance function for metro hubs which used the relationships among circulation speed,density and flow rate for pedestrians was defined.Then,a route optimization model which minimizes the movement time of the last evacuee was constructed to optimize evacuation performance.Solutions to the proposed mathematical model were obtained through an iterative optimization process.The route optimization model was applied to Xidan Station of Beijing Metro Line 4 based on the actual situations,and the calculation results of the model were tested using buildingExodus microscopic evacuation simulation software.The simulation result shows that the proposed model shortens the evacuation time by 16.05%,3.15% and 2.78% compared with all or none method,equally split method and Logit model,respectively.Furthermore,when the population gets larger,evacuation efficiency in the proposed model has a greater advantage.

Traffic Engineering in Dynamic Hybrid Segment Routing Networks

The emergence of Segment Routing(SR)provides a novel routing paradigm that uses a routing technique called source packet routing.In SR architecture,the paths that the packets choose to route on are indicated at the ingress router.Compared with shortest-path-based routing in traditional distributed routing protocols,SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress router.Despite the advantages of SR,it may be difficult to update the existing IP network to a full SR deployed network,for economical and technical reasons.Updating partial of the traditional IP network to the SR network,thus forming a hybrid SR network,is a preferable choice.For the traffic is dynamically changing in a daily time,in this paper,we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR network.WASAR algorithm can be divided into three steps:firstly,representative Traffic Matrices(TMs)and the expected TM are obtained from the historical TMs through ultrascalable spectral clustering algorithm.Secondly,given the network topology,the initial network weight setting and the expected TM,we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning(DRL)algorithm.Thirdly,we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands,in order to improve the network performance.In the evaluation,we exploit historical TMs to test the performance of the obtained routing configuration in WASAR.The extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization(MLU)under the dynamic traffic.

The Information Modeling and Intelligent Optimization Method for Logistics Vehicle Routing and Scheduling with Multi-objective and Multi-constraint

The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.

DERNNet:Dual Encoding Recurrent Neural Network Based Secure Optimal Routing in WSN

A Wireless Sensor Network(WSN)is constructed with numerous sensors over geographical regions.The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy.As sensor nodes are resource constrained in nature,novel techniques are essential to improve lifetime of nodes in WSN.Nodes energy is considered as an important resource for sensor node which are battery powered based.In WSN,energy is consumed mainly while data is being transferred among nodes in the network.Several research works are carried out focusing on preserving energy of nodes in the network and made network to live longer.Moreover,this network is threatened by attacks like vampire attack where the network is loaded by fake traffic.Here,Dual Encoding Recurrent Neural network(DERNNet)is proposed for classifying the vampire nodes s node in the network.Moreover,the Grey Wolf Optimization(GWO)algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points;thereby maximizing battery/lifetime of the network nodes.The proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing(KIDSASR),Risk-aware Reputation-based Trust(RaRTrust)model and Activation Function-based Trusted Neighbor Selection(AF-TNS)in terms of various parameters.These existing methods may lead to wastage of energy due to vampire attack,which further reduce the lifetime and increase average energy consumed in the network.Hence,the proposed DERNNet method achieves 31.4%of routing overhead,23%of end-to-end delay,78.6%of energy efficiency,94.8%of throughput,28.2%of average latency,92.4%of packet delivery ratio,85.2%of network lifetime,and 94.3%of classification accuracy.

Cooperative Channel and Optimized Route Selection in Adhoc Network

Over the last decade,mobile Adhoc networks have expanded dramati-cally in popularity,and their impact on the communication sector on a variety of levels is enormous.Its uses have expanded in lockstep with its growth.Due to its instability in usage and the fact that numerous nodes communicate data concur-rently,adequate channel and forwarder selection is essential.In this proposed design for a Cognitive Radio Cognitive Network(CRCN),we gain the confidence of each forwarding node by contacting one-hop and second level nodes,obtaining reports from them,and selecting the forwarder appropriately with the use of an optimization technique.At that point,we concentrate our efforts on their channel,selection,and lastly,the transmission of data packets via the designated forwarder.The simulation work is validated in this section using the MATLAB program.Additionally,steps show how the node acts as a confident forwarder and shares the channel in a compatible method to communicate,allowing for more packet bits to be transmitted by conveniently picking the channel between them.We cal-culate the confidence of the node at the start of the network by combining the reliability report for thefirst hop and the reliability report for the secondary hop.We then refer to the same node as the confident node in order to operate as a forwarder.As a result,we witness an increase in the leftover energy in the output.The percentage of data packets delivered has also increased.

Evaluation of the submarine debris-flow hazard risks to planned subsea pipeline systems: a case study in the Qiongdongnan Basin, South China Sea

The ever-increasing deepwater oil and gas development in the Qiongdongnan Basin,South China Sea has initiated the need to evaluate submarine debris-flow hazard risks to seafloor infrastructures.This paper presents a case study on evaluating the debris-flow hazard risks to the planned pipeline systems in this region.We used a numerical model to perform simulations to support this quantitative evaluation.First,one relict failure interpreted across the development site was simulated.The back-analysis modeling was used to validate the applicability of the rheological parameters.Then,this model was applied to forecast the runout behaviors of future debris flows originating from the unstable upslope regions considered to be the most critical to the pipeline systems surrounding the Manifolds A and B.The model results showed that the potential debris-flow hazard risks rely on the location of structures and the selection of rheological parameters.For the Manifold B and connected pipeline systems,because of their remote distances away from unstable canyon flanks,the potential debris flows impose few risks.However,the pipeline systems around the Manifold A are exposed to significant hazard risks from future debris flows with selected rheological parameters.These results are beneficial for the design of a more resilient pipeline route in consideration of future debris-flow hazard risks.

An Ant Colony Algorithm Based on Cross-Layer Design for Routing and Wavelength Assignment in Optical Satellite Networks

This paper introduces an ant colony routing and wavelength assignment algorithm based on cross-layer design(CL-ACRWA),which can overcome the adverse effects of Doppler wavelength shift on data transmission in optical satellite networks. Firstly, a cross-layer optimization model is built, which considers the Doppler wavelength shift, the transmission delay as well as wavelength-continuity constraint. Then an ant colony algorithm is utilized to solve the cross-layer optimization model, resulting in finding an optimal light path satisfying the above constraints for every connection request. The performance of CL-ACRWA is measured by the communication success probability, the convergence property and the transmission delay. Simulation results show that CL-ACRWA performs well in communication success probability and has good global search ability as well as fast convergence speed. Meanwhile, the transmission delay can meet the basic requirement of real-time transmission of business.

Analysis of Short Sea Shipping-Based Logistics Corridors in the Baltic Sea Region

Despite recent turbulences in global economy, the growth of global trade volumes is expected to continue in the future, leading to increased demands on the performance of logistics networks. The political framework for EU （European Union） Transport Policy Development is presented in the EU White Paper on Transport 2011 in order to build a competitive European transport system. One significant aspect is the promotion of multimodal transport in order to decrease terrestrial transport services （road and rail） and to increase services in the maritime transport sector, especially considering the relief of road and railway infrastructure. Looking at the present situation, SSS （Short Sea Shipping） is already used in many different transport fields all around the world. However, there still exists a great potential which currently is not used or not sufficiently exploited for many different reasons. In order to identify the potential use of SSS in multimodal transport, different scenarios in the Baltic Sea Region and the adjoining hinterland have been developed pointing out alternative solutions for routing. These options are analyzed in detail and evaluated from different perspectives （i.e. transport and handling costs, time consumption and transport-related emissions）. Afterwards, advantages and disadvantages of each alternative will be examined by taking into account economic and ecological aspects in making decision.