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From the view of comprehensive consideration, this considers the uncertainty of transportation time and cost due to seasonality and human factors, and a multi-objective chance constrained programming model with minimum transportation time and cost was established. Due to the complexity of the problem, traditional algorithms are not enough, and more efficient optimization algorithms are still needed. According to the characteristics of the problem, the beetle antennae search (BAS) with one beetle is changed into multiple beetles, and Dijkstra algorithm is embedded, a hybrid beetle swarm optimization algorithm (HBSO) is designed to solve the problem, and the case analysis and algorithm analysis are made for three different examples; the datasets are referenced in literature (Lu et al. in Computer Integrated Manufacturing Systems 5:10–14, 2020). Under the three examples, the influence of the customer on the time and cost is different. The confidence level and weight of cost and time have great influence on the decision results. 4PL provider will not affect the choice of transportation route, but will change the choice of 3PL supplier and transportation mode. HBSO is an effective method to solve this problem. It is proved on efficiency, effectiveness and reliability in experiments by comparing with GA, PSO and BAS.

Path finding models attempt to provide efficient approaches for finding shortest paths in networks. A well-known shortest path algorithm is the Dijkstra algorithm. This paper redesigns it in order to tackle situations in which the parameters of the networks may be uncertain. To be precise, we allow that the parameters take the form of special picture fuzzy numbers. We use this concept so that it can flexibly fit the vague character of subjective decisions. The main contributions of this article are fourfold: ( i ) The trapezoidal picture fuzzy number along with its graphical representation and operational laws is defined. ( ii ) The comparison of trapezoidal picture fuzzy numbers on the basis of their expected values is proposed in terms of their score and accuracy functions. ( iii ) Based on these elements, we put forward an adapted form of the Dijkstra algorithm that works out a picture fuzzy shortest path problem, where the costs associated with the arcs are captured by trapezoidal picture fuzzy numbers. Also, a pseudocode for the application of our solution is provided. ( iv ) The proposed algorithm is numerically evaluated on a transmission network to prove its practicality and efficiency. Finally, a comparative analysis of our proposed method with the fuzzy Dijkstra algorithm is presented to support its cogency.

Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively describes the principles and steps of four types of path planning algorithms: the Dijkstra algorithm (including its optimized derivatives), the A* algorithm (including its optimized derivatives), the RRT (Rapidly exploring Random Trees) algorithm (including its optimized derivatives), and the BFS (Breadth First Search) algorithm. Secondly, the Dijkstra algorithm, the A* algorithm, the BFS algorithm, and the Dynamic Weighted A* algorithm were utilized to plan the paths required for the process of smart parking. During the analysis, it was found that the Dijkstra algorithm had the drawbacks of planning circuitous paths and taking too much time in the path planning for smart parking. Although the traditional A* algorithm based on the Dijkstra algorithm had greatly reduced the planning time, the effect of path planning was still unsatisfactory. The BFS (Breadth First Search) algorithm had the shortest planning time among the four algorithms, but the paths it plans were unstable and not optimal. The Dynamic Weighted A* algorithm could achieve better path planning results, and with adjustments to the weight values, this algorithm had excellent adaptability. This review provides a reference for further research on path planning algorithms in the process of smart parking.

This paper proposes a real-time path-planning algorithm for unmanned aerial vehicles (UAVs) operating in hostile environments, such as disaster areas and battlefields. The key idea of the algorithm is to compensate Voronoi diagram to quickly yield the paths with maximum survivability against abrupt environmental changes or pop-up threats. We suggest a compensated Voronoi diagram which considers the weighted strengths of various threats, whereas the original Voronoi diagram is constructed via Delaunay triangulation with the same strength of whole threats. Then, Dijkstra algorithm is adopted to find the path connecting the vertices to maximize survivability. This algorithm is simple to implement, requires a short computation time, and can effectively consider the strengths of various threats. Numerical simulations are performed to compare the performance of the proposed method with that of the original Voronoi diagram, the improved Voronoi diagram, and the original Voronoi diagram with optimization. Results show that the proposed path-planning method provides suboptimal solutions in survivability and works in a real-time manner.

Recently, the unmanned aerial vehicle (UAV)-assisted edge computing is proposed to improve the quality of service in some scenarios within sparse or unavailable base stations (BSs). Meanwhile, the caching technology is adopted to reduce the wireless traffic load and the data transmission delay. However, due to the limited storage capacity of edge nodes, the edge nodes cannot store all of the contents required by user equipment (UE). So, how to select the reasonable contents for caching on edge nodes to reduce the content delivery delay becomes a challenge in the UAV-assisted edge computing environment. In this paper, the Dijkstra algorithm based cooperative caching strategy for UAV-assisted edge computing system is proposed. Specially, the content transmission delay between two nodes is computed. Then, for each requested content, the weighted edge-undirected graph (WEUG), in which one vertex represents one node, is built. Furthermore, Dijkstra algorithm is adopted to achieve the minimal content transmission delay from the edge node caching the requested content to UE. Finally, the optimization problem of content caching is built, and the corresponding cache strategy is achieved by solving the optimization problem. The experimental results imply that the proposed cooperative caching algorithm can achieve better performance on the average content transmission delay, the average cache hit rate, and the total of hops, respectively, comparing with the benchmark algorithms.

The Dijkstra algorithm, also termed the shortest-route algorithm, is a model that is categorized within the search algorithms. Its purpose is to discover the shortest-route, from the beginning node (origin node) to any node on the tracks, and is applied to both directional and undirected graphs. However, all edges must have non-negative values. The problem of organizing inter-city flights is one of the most important challenges facing airplanes and how to transport passengers and commercial goods between large cities in less time and at a lower cost. In this paper, the authors implement the Dijkstra algorithm to solve this complex problem and also to update it to see the shortest-route from the origin node (city) to the destination node (other cities) in less time and cost for flights using simulation environment. Such as, when graph nodes describe cities and edge route costs represent driving distances between cities that are linked with the direct road. The experimental results show the ability of the simulation to locate the most cost-effective route in the shortest possible time (seconds), as the test achieved 95% to find the suitable route for flights in the shortest possible time and whatever the number of cities on the tracks application.

During the process of sending a signal across a transmission channel that has a broad bandwidth, the Multiple-Input Multiple-Output, or MIMO, system will frequently require a larger quantity of energy and power than it would under normal circumstances. This is because the transmission channel will have a greater capacity to carry more data. When there are so many different sets of signals that have been found, it is necessary to be able to differentiate between them using one of the many different methods that have been developed. One of the methods that is now one of the most extensively employed in the process of discovery is called maximum likelihood detection. This method is also frequently referred to as machine learning (ML) detection. This is as a result of the very high throughput that ML detection has. On the other hand, the exponential growth in ideal throughput is far smaller than the complexity of the framework and the amount of energy that it consumes. In order to better simplify communication between the transmitter and the receiver, the major goal of this endeavor is to locate the shortest viable routing route for the physical data transmission layer and use that information to design the routing path. Because of this, both the quantity of energy that is used and the level of complexity that the system has will both drop. Improved Iterative Based Dijkstra Algorithm (IIBDA) to Gauge the Most Limited Course of the Channel with a Restricted Maximum Likelihood-Detection (RMLD) Design was a method that was presented to solve the problem. This was done in order to address the concerns that had been raised and resolved in the prior discussion. This was done in an effort to discover answers to problems such as these. The Execution Examination illustrates how the Complexity and Control Utilization in the Proposed Strategy may be Decreased by showing how these factors might be Addressed. An FPGA Virtex-6 was used for putting the suggested plan into action in order to accomplish this. When the recommended IIBDA-RMLD were put through their paces in terms of power consumption, area, time delay, and complexity, all of these characteristics exhibited improvements of up to 95.4%, 84.23%, 84.21%, 87.23%,90.14% respectively. These percentages represent the maximum levels of improvement that were observed. MIMO Transmission System is able to achieve a significantly higher Signal to Noise Ratio (Snr) demonstrating with reduced power usage of 0.538mw and range optimization accomplished 11093.13 m for Quadrature Phase Shift Keying (QPSK) balancing with the ML Location System using Feed Forward Neural Network (FFNN). This is in addition to the fact that the MIMO Transmission System can reduce the amount of power it uses by 0.538mw. This is accomplished with a reduced quantity of usage of electrical power. In conclusion, RMLD was employed on MIMO networks in order to enhance the transmission of information in military and other applications by making it more transportable and safer. This was done for a variety of reasons. This action was taken for a number of different reasons.

The development of multicore hardware has provided many new development opportunities for many application software algorithms. Especially, the algorithm with large calculation volume has gained a lot of room for improvement. Through the research and analysis, this paper has presented a parallel PO-Dijkstra algorithm for multicore platform which has split and parallelized the classical Dijkstra algorithm by the multi-threaded programming tool OpenMP. Experiments have shown that the speed of PO-Dijkstra algorithm has been significantly improved. According to the number of nodes, the completion time can be increased by 20–40%. Based on the improved heterogeneous dual-core simulator, the Dijkstra algorithm in Mi Bench is divided into tasks. For the G.72 encoding process, the number of running cycles using “by function” is 34% less than using “divided by data,” while the power consumption is only 83% of the latter in the same situation. Using “divide by data” will reduce the cost and management difficulty of real-time temperature. Using “divide by function” is a good choice for streaming media data. For the Dijkstra algorithm, the data is data without correlation, so using a simpler partitioning method according to the data partitioning can achieve good results. Through the simulation results and the analysis of the results of real-time power consumption, we conclude that for data such as strong data correlation of streaming media types, using “divide by function” will have better performance results; for data types where data correlation is not very strong, the effect of using “divide by data” is even better.

Tailings dam is the place where tailings are accumulated, once a failure occurs, it will cause irreversible huge harm to downstream residents and the environment. However, in the field of environment and safety, few scholars have conducted comprehensive studies on the inundation area of tailings dam break and the evacuation of personnel after dam break. In this paper, we adopt the Finite Volume Method and Turbulence Model to simulate the debris flow evolution after the tailings dam break in Fuchonggou, Anhui province, China. Then we calculate the extent of dam break inundation and put forward the evacuation route of people downstream. Considering the road factors, the 2D evacuation is improved to the 3D-environment resident evacuation for reaching the optimal evacuation route method and the best shelters under tailings dam break. The results show that once the tailings dam was to fail, it would cause damage to the downstream area of about 3.21 km 2 . And this improved 3D Dijkstra optimization algorithm can save a maximum of 11.83% of evacuation time. Through the analysis of the inundation extent and the speed of pedestrians, all residents can be guaranteed to survive if the alert time is 434 s ahead of. Considering the actual evacuation effects, the study can also assess the best locations for shelters in the inundated areas and provides corresponding suggestions for urban road reconstruction. This work makes a pioneering comprehensive study on the inundation area of tailings dam break and the personnel evacuation, and proves that under the premise of advance prediction and accurate early warning, the people in the inundation area can be evacuated to the shelter in the shortest time, thus reducing the loss of residents’ life in emergency situations.

To increase data availability and accuracy in the coastal zone, especially in the last 5 km to the coast, we present a SCMR (Seamless Combination of Multiple Retrackers) processing strategy to combine sea surface height (SSH) estimates from waveform retrackers of SGDR MLE4, ALES, WLS3 and MB4 for Jason-3 and Saral missions, and of SAMOSA and SAMOSA+ for Sentinel-3A mission in the Australian coastal zone. The SCMR does not require the waveform classification result. It includes two steps: (1) estimating and removing the SSH bias due mainly to the significant wave height (SWH) difference-dependent height differences, and (2) determining the optimal along-track SSH profile by using the Dijkstra algorithm. In the study region, the results show that the SCMR increases the data availability by up to 15% in the last 5 km to the coast and reduces the noise level by 28–34% at the spatial scales < 2.5 km. The validation results against tide gauges show that SCMR-derived SSH estimates achieve a better accuracy than that from any single retracker, with the improvement percentage of 6.26% and 4.94% over 0–10 km and 20–100 km distance bands, respectively.