Aiming at the problem of automated guided vehicle path planning in storage system, this paper provided a new method which combines global path planning algorithm and local path planning algorithm to achieve the goal o...
详细信息
ISBN:
(纸本)9781510845541
Aiming at the problem of automated guided vehicle path planning in storage system, this paper provided a new method which combines global path planning algorithm and local path planning algorithm to achieve the goal of finding the optimal path. In this paper, the improved A* algorithm is used as the global path planning algorithm, and the improved APF algorithm is used as the local path planning algorithm. The hybridalgorithm not only makes full use of the known information to generate the global optimal path, but also can effectively avoid obstacles on the path. The advantages and effectiveness of the hybridalgorithm are proved by the results of simulations and applications.
Optimization scheduling plays a pivotal role in construction projects, significantly influencing both the overall project schedule and its efficiency. This study focuses on optimizing the scheduling of electric highwa...
详细信息
Optimization scheduling plays a pivotal role in construction projects, significantly influencing both the overall project schedule and its efficiency. This study focuses on optimizing the scheduling of electric highway engineering projects within roadbed construction. The research considers multiple earthmoving processes and optimizes the working time of each piece of equipment, taking into account its capacity and speed within a limited working week. The study is further contextualized by the use of regional time-of-use (TOU) electricity pricing. A sophisticated optimization model is developed to simulate optimal machinery operation, striking a balance between energy consumption and work efficiency. This paper introduces an innovative algorithm, the improved crested porcupine optimizer (ICPO), which incorporates Latin hypercube sampling for population initialization. To enhance algorithmic effectiveness, a combined strategy of parallel and compact processing is employed. This approach reduces the number of iterations required for optimization and consequently lowers energy consumption. Rigorous analysis and comparison with existing algorithms demonstrate that ICPO significantly reduces both iteration count and financial expenditure. Simulation results validate the accuracy and practicality of the proposed model and algorithm, showing a reduction of over 7% in both engineering time and energy consumption.
The optimum design of distributed timed mass dampers (DTMDs) is normally based on predefined restrictions, such as the location and/or mass ratio of the tuned mass dampers (TMDs). To further improve the control perfor...
详细信息
The optimum design of distributed timed mass dampers (DTMDs) is normally based on predefined restrictions, such as the location and/or mass ratio of the tuned mass dampers (TMDs). To further improve the control performance, a free parameter optimization method (FPOM) is proposed. This method only restricts the total mass of the DTMDs system and takes the installation position, mass ratio, stiffness and damping of each TMD as parameters to be optimized. An improvedhybrid genetic-simulated annealing algorithm (IHGSA) is adopted to find the optimum values of the design parameters. This algorithm can solve the non-convexity and multimodality problems of the objective function and is quite effective in dealing with the large amount of computations in the free parameter optimization. A numerical benchmark model is adopted to compare the control efficiency of FPOM with conventional control scenarios, such as single TMD, multiple TMDs and DTMDs optimized through conventional methods. The results show that the DTMDs system optimized by using FPOM is superior to the other control scenarios for the same value of mass ratio.
Shared electric vehicle relocation (SEVR) is essential to the shared mobility and cost-benefit of a one-way, floating-station vehicle-sharing system. This study investigates the crowdsourced task dispatching problem f...
详细信息
Shared electric vehicle relocation (SEVR) is essential to the shared mobility and cost-benefit of a one-way, floating-station vehicle-sharing system. This study investigates the crowdsourced task dispatching problem for SEVR to rebalance the spatial variation in supply and demand under random demand. The objective is to minimize total costs by dynamic matching among relocation tasks, stations, and crowdsourced dispatchers. Single-task crowdsourcing (STC) and multitask crowdsourcing (MTC) dispatching models are presented. A hybridalgorithm, combining an improved genetic algorithm with variable neighbourhood search, is devised to solve the problem. Scenario analysis, using trajectory data of electric taxis in Changchun City, shows that compared with the benchmark algorithm, the hybridalgorithm improves the solution quality by 5.09% and reduces the running time by 31.20%. STC is preferred over MTC in the low supply-to-demand density (R) scenario, though MTC is not rejected. However, MTC performs more effectively in the medium and high R scenarios.
With the frequent occurrence of cyber attacks in recent years, cyber attacks have become a major factor affecting the security and reliability of power SCADA. We urgently need an effective SCADA risk assessment algori...
详细信息
ISBN:
(纸本)9783031243851;9783031243868
With the frequent occurrence of cyber attacks in recent years, cyber attacks have become a major factor affecting the security and reliability of power SCADA. We urgently need an effective SCADA risk assessment algorithm to quantify the value at risk. However, traditional algorithms have the shortcomings of excessive parsing variables and inefficient sampling. Existing improvedalgorithms are far from the optimal distribution of the sampling density function. In this paper, we propose an optimal sampling algorithm and a selective parsing algorithm and combine them into an improved hybrid algorithm to solve the problems. The experimental results show that the improved hybrid algorithm not only improves the parsing and sampling efficiency, but also realizes the optimal distribution of the sampling density function and improves the accuracy of the assessment index. The assessment indexs accurately quantify the risk values of three widely used cyber attacks.
In this paper, we propose a bi-level optimization model (BLOM) with improvedhybrid metaheuristics. The hybrid GA and PSO algorithm is applied in both upper-level and lower-level model. This improved hybrid algorithm ...
详细信息
In this paper, we propose a bi-level optimization model (BLOM) with improvedhybrid metaheuristics. The hybrid GA and PSO algorithm is applied in both upper-level and lower-level model. This improved hybrid algorithm differs from the general hybridalgorithm which GA or PSO is applied in the single upper-level or lower-level model. BLOM is intended to schedule the phases of each isolated traffic signal and eco-driving environmentally. The upper-level optimization model (ULOM) considers the real-time traffic characteristics of the traffic flows near the signalized road intersection. At the same time, vehicles in the lower-level optimization model (LLOM) retrieve the real-time traffic signals using vehicular networks. Then, the traffic signals update the schedule and the vehicles are optimized motion states for greener environment factor respectively. We evaluate the performance of BLOM in a single road intersection using OMNET++ and SUMO. From the simulation results, we conclude that the BLOM with improved hybrid algorithm reduce fuel consumption and CO2 emissions compared with Maximize Throughput Model (MaxTM). Moreover, compared with the ordinary single algorithm, the proposed improved hybrid algorithm decreases the average operation cycle.
暂无评论