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Copyright of Telecommunication Engineering is the property of Telecommunication Engineering 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.)

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 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]

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]

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.)

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]

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]

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%.

When organizing the intermodal transport of hazmat by road-rail, choosing route is crucial to reduce operational risks and transport costs. Based on the Silk Road Economic Zone, the hazmat route optimization problem was studied by road-rail intermodal transport. The transportation costs and risks of both the highway and the railway was analyzed systematically, and a multi-objective nonlinear optimization model that consider both costs (transportation costs, carbon emissions costs, and soft time window penalty costs) and risks (transportation risks) was established. Through linear weighting the optimal model was transformed into a single-objective one, and an improved non-dominated sorting genetic algorithm-Ⅱ was designed to solve it. The process of population initialization was improved, the elite strategy of cooperative selection was introduced, and the crossover operator was enhanced as well. Finally, a numerical example was solved. The result shows that the improved NSGA-Ⅱ algorithm is more uniform in spatial distribution, and the front span is larger. The optimized transportation scheme owns fewer transportation nodes and shorter transportation path, simultaneously. Compared with NSGA-Ⅱalgorithm, the total costs and risks obtained by this algorithm decreased dramatically, Besides, both the searching ability and the solving accuracy are significantly improved. [ABSTRACT FROM AUTHOR]

Copyright of Space-Integrated-Ground Information Networks is the property of Beijing Xintong Media Co., Ltd. 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.)

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering 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.)

Take-out delivery is an important part of takeout business, and the delivery cost and on-time delivery rate are the key factors to determine the quality of take-out delivery, so the research on the optimization of take-out delivery routing is particularly important. Based on an emerging drones and riders joint take-out delivery mode, a temporal-spatial distance measurement method is introduced to establish a routing optimization model for drone rider joint take-out delivery to minimize the cost of food delivery, and a two-stage heuristic algorithm is designed to solve the problem. In the first stage, the genetic algorithm combined with K-means is used to cluster customers and form the initial path of riders. In the second stage, the improved variable neighborhood search algorithm and A* algorithm are respectively used to optimize the path of riders and the flight path of drone food delivery. Finally, a case is designed and solved. The experimental results show that, compared with the traditional rider delivery mode, the drones and riders delivery mode considering the temporal-spatial distance can reduce the cost of food delivery and improve the on-time delivery rate. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

Agricultural wireless sensor networks plays a key role in obtaining multi-source heterogeneous big data of farmland soil, environment and crop growth. The increasing network scale brings challenges to the application of agricultural Internet of Things. In order to solve the problem that sensors are not uniformly distributed in farmland and constrained by energy, a collaborative optimization hybrid multi hop routing algorithm, particle simulated multipath routing (PSMR) based on particle swarm optimization and simulated annealing was proposed. Firstly, cluster heads were selected by node residual energy and node degree weighting, and cluster structure was used to realize efficient dynamic networking of heterogeneous networks. Then, the multi hop data structure between cluster heads was used to solve the problem of high energy consumption in long-distance transmission of cluster heads. Particle swarm optimization and simulated annealing were used to improve the convergence speed, and sink nodes could accelerate the collection of aggregated data in cluster heads. The simulation results showed that compared with the energy-efficient load balancing multipath routing scheme (EMR), the network lifetime of PSMR algorithm was increased by 57%. EMR selected the data transmission link with low energy consumption and small delay by calculating the weight of total link hops and transmission energy consumption. Compared with greedy perimeter stateless routing-algorithm (GPSR-A algorithm), which could ensure the shortest data transmission distance and lower network transmission delay, the first dead sensor node was delayed for two rounds in the same network life cycle, and the residual energy standard deviation was reduced by 0.04 J, which had good network energy consumption balance. PSMR algorithm could reduce the extra energy consumption of remote cluster heads by multi hop between cluster heads, and improved the energy balance performance of cluster heads with different distances. It can provide technical basis for long-term, efficient and stable data acquisition and monitoring of large-scale farmland complex environment, and improve the resource utilization efficiency of agricultural Internet of Things.

Aiming at the multi-constraint routing problem,a mathematical model was designed,and an improved immune clonal shuffled frog leaping algorithm (IICSFLA) was proposed,which combined immune operator with traditional SFLA.Under the constraints of bandwidth,delay,packet loss rate,delay jitter and energy cost,total energy cost from the source node to the terminal node was computed.The proposed algorithm was used to find an optimal route with minimum energy cost.In the simulation,the performance of IICSFLA with adaptive genetic algorithm and adaptive ant colony optimization algorithm was compared.Experimental results show that IICSFLA solves the problem of multi-constraints QoS unicast routing optimization.The proposed algorithm avoids local optimum and effectively reduces energy loss of data on the transmission path in comparison with adaptive genetic algorithm and adaptive ant colony optimization algorithm.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 distribution practices, the delivery task may be delayed or completed in advance, both of which will increase the distribution cost and decrease the revenue, resulting the earliness/tardiness risk. Here, the routing optimization is studied for the fourth-party logistics with consideration of earliness/tardiness risk. A mathematical model trying to minimize the distribution costs and constrain the earliness/tardiness risk is set up, then a deletion algorithm embedded harmony search is proposed. The effectiveness of the proposed model and algorithm is verified through solving cases at different scales. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications 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.)

Copyright of Journal of Fuzhou University is the property of Journal of Fuzhou University, 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.)

In order to improve the performances and adaptabilities of wireless sensor networks the architecture of soft-ware-defined sensor network (SDSN) was proposed and the studies were focused on the network reconfiguration algo-rithm of SDSN. In the algorithm, the theory of Voronoi diagram was first used to search the optimal allocation of sensing radius to achieve K-coverage on the target region. Then, based on the theory of simplicial complex, a centralized control mechanism based on the minimal generator of boundary chain group and the node degree was proposed to simplify the architecture of network topology and to ensure the connectivity of the whole system and the robustness of the emergency region. Considering the adaptability in dynamic environment of routing protocols in SDSN, a routing optimization algo-rithm for SDSN was proposed, which was based on quality of service (QoS) of multi-service. Simulation results show that the proposed routing algorithm can efficiently allocate resources to satisfy the requirements of multi-service's QoS and to prolong the lifetime of network.

Smart identifier network (SINET)has an advantage of being aware of the accurate traffic matrix. Traffic ma-trix was use as the constraint, model on load balancing routing problem, and transform the primal problem into dual problem by using Lagrange function. Therefore,the optimization goal of the dual problem can be easily achieved. In or-der to achieve the dual problem optimization goal,a traffic matrix based load balancing routing algorithm (TM-LB)was proposed. By performing the TM-LB,the control plane can plan paths for subsequent traffic according to network fluctu-ation. OMNET++ was used to run the experiment simulation based on NSFnet topology. The results show that TM-LB can better reduce congestion and realize load balancing, compared with traditional routing mechanism. Finally, a proof-of-concept was built implementation and carry out experiments for testing the overhead of TM-LB algorithm.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Reasonable emergency rescue vehicle scheduling is an effective way to reduce casualties and property losses in the event of sudden disaster. This paper reviewed vehicle scheduling model from two different perspectives, one was the routing optimization of distribution vehicle of emergency material and the other was reliability of travel time of rescue vehicle. Combined the characteristics of emergency rescue, it analyzed the defects of rescue model and algorithm. It proposed the application value of learning effect in the optmization of emergency rescue vehicle routing problem. Finally, it prospected the trend of emergency rescue vehicle scheduling’s development. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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.)

To solve the logistics distribution routing problem under the carbon emission trading mechanism, we introduce a carbon emission calculation method considering the vehicle load and speed. Based on TSP model, a logistics distribution routing model under carbon emission trading mechanism is proposed. It is shown that the routing arrangement under carbon emission trading mechanism is better to reduce carbon emissions over the traditional routing arrangement only considering economic cost. We analytically and numerically examine the impacts of carbon trade, carbon price and carbon cap on routing decisions, carbon emissions, and total cost. We make some interesting observations for logistics companies about how to control cost and reduce carbon emissions from the numerical analytical results. [ABSTRACT FROM AUTHOR]