scholarly journals A Multilayer Genetic Algorithm for Automated Guided Vehicles and Dual Automated Yard Cranes Coordinated Scheduling

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qianru Zhao ◽  
Shouwen Ji ◽  
Wenpeng Zhao ◽  
Xinling De
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Operating Time ◽  
Operation Time ◽  
Complex Model ◽  
Automated Guided Vehicles ◽  
Total Energy Consumption ◽  
Equipment Operation ◽  
Storage Location ◽  
Calculation Results

At present, a lot of studies on automatic terminal scheduling are aimed at the shortest operating time. An effective way to reduce the operating time is to increase the amount of operating equipment. However, people often ignore the additional costs and energy consumption caused by increasing the amount of equipment. This paper comprehensively considers the two aspects of the equipment operation time and equipment quantity matching. With the minimum total energy consumption of the operating equipment as the objective function, a cooperative scheduling model of Automated Guided Vehicles (AGVs) and dual Automated Yard Cranes (AYCs) is established. In the modelling process, we also considered the interference problem between dual Automated Yard Cranes (AYCs). In order to solve this complex model, this paper designs an improved multilayer genetic algorithm. Finally, the calculation results from CPLEX and a multilayer genetic algorithm are compared, and the effectiveness of the model and algorithm is proved by experiments. In addition, at the same time, it is proved that it is necessary to consider the interference problem of dual Automated Yard Cranes (AYCs), and the optimal quantity matching scheme for the equipment and the optimal temporary storage location is given.

Effect of light switch illumination on total energy consumption

2021 ◽  
Vol 14 (2) ◽  
pp. 151-156
Author(s):  
M. A. Pronin ◽  
E. V. Churkina
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Operating Time ◽  
Operation Time ◽  
Luminous Flux ◽  
Total Energy Consumption ◽  
Network Operation ◽  
The Difference ◽  
Standby Mode ◽  
Effect Of Light

The question of the relevance of reducing energy consumption is considered. An assumption is made that in cases where indoor illuminated switches are used that control a group of lamps, currents of the order of microamperes flow in the lighting network in the switched-off mode.The regulatory documentation related to the normative indicators of illumination as well as the typical area of premises is analyzed, and on the basis of the listed data, the calculation of the minimum required luminous flux emitted by lamps is made. The normalized calculated luminous flux was divided by the luminous flux from one lamp, with the resulting ratio rounded up. This ratio is the approximate number of lamps. This number of lamps will enable to calculate the total current of the entire lighting network.The standard rates for the operating time of the lighting network are taken into account. The operating time of the lighting network in the "standby" mode is the difference between the total number of hours per day and the standard operating time of the lighting network.Knowing the power consumption and the network operation time in the "standby" mode, we can calculate the power consumption of the lighting network in the "standby" mode.

scholarly journals Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhuofan Liao ◽  
Jingsheng Peng ◽  
Bing Xiong ◽  
Jiawei Huang
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Cellular Networks ◽  
Physical Distance ◽  
Edge Computing ◽  
Base Stations ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Average Delay ◽  
Server Selection

AbstractWith the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

Lifetime Maximization Routing Based on Genetic Algorithm for Wireless Sensor Networks

2011 ◽  
Vol 230-232 ◽  
pp. 283-287
Author(s):  
You Rong Chen ◽  
Tiao Juan Ren ◽  
Zhang Quan Wang ◽  
Yi Feng Ping
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Gathering ◽  
Transmission Probability ◽  
Wireless Sensor ◽  
Total Energy Consumption ◽  
Lifetime Maximization

To prolong network lifetime, lifetime maximization routing based on genetic algorithm (GALMR) for wireless sensor networks is proposed. Energy consumption model and node transmission probability are used to calculate the total energy consumption of nodes in a data gathering cycle. Then, lifetime maximization routing is formulated as maximization optimization problem. The select, crosss, and mutation operations in genetic algorithm are used to find the optimal network lifetime and node transmission probability. Simulation results show that GALMR algorithm are convergence and can prolong network lifetime. Under certain conditions, GALMR outperforms PEDAP-PA, LET, Sum-w and Ratio-w algorithms.

scholarly journals Simulation-Based Genetic Algorithm towards an Energy-Efficient Railway Traffic Control

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Daniel Tuyttens ◽  
Hongying Fei ◽  
Mohand Mezmaz ◽  
Jad Jalwan
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Total Energy ◽  
Traffic Control ◽  
Energy Efficient ◽  
Energy Utilization ◽  
Utilization Rate ◽  
Total Energy Consumption ◽  
Railway Traffic ◽  
Mode Control

The real-time traffic control has an important impact on the efficiency of the energy utilization in the modern railway network. This study is aimed to develop an energy-efficient railway traffic control solution for any specified railway. In other words, it is expected to define suitable driving profiles for all the trains running within a specified period through the targeted network with an objective to minimize their total energy consumption. How to optimize the train synchronization so as to benefit from the energy regenerated by electronic braking is also considered in this study. A method based on genetic algorithm and empirical single train driving strategies is developed for this objective. Six monomode strategies and one multimode strategy are tested and compared with the four scenarios extracted from the Belgian railway system. The results obtained by simulation show that the multi-mode control strategy overcomes the mono-mode control strategies with regard to global energy consumption, while there is no firm relation between the utilization rate of energy regenerated by dynamic braking operations and the reduction of total energy consumption.

scholarly journals ENERGY EFFICIENCY OF GEARBOXES APPLICATION IN POWER UNITS OF ELECTRIC VEHICLES

2021 ◽  
Vol 3 (56) ◽  
pp. 5-12
Author(s):  
Sergey N. PODDUBKO ◽  
◽  
Nikolay N. ISHIN ◽  
Arkadiy M. GOMAN ◽  
Andrey S. SKOROKHODOV ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Power Unit ◽  
Electric Vehicles ◽  
Electric Motor ◽  
Hydrocarbon Fuel ◽  
High Energy ◽  
Operating Conditions ◽  
Total Energy Consumption ◽  
Calculation Results

With all advantages, electric vehicles have a significant disadvantage — a relatively small driving range on a single charge of the traction battery compared to cars using hydrocarbon fuel. The solution to the issue is to choose a rational structural scheme of an electromechanical power unit to obtain its high energy efficiency regardless of the operating conditions. A significant number of electric vehicles produced today either do not contain gearboxes or contain single-speed reducers. The use of a multi-speed gearbox solves the problem of adapting the working processes of a traction electric motor to the loading conditions, bringing its efficiency as close as possible to the range of highly efficient values. Calculated estimation of energy consumption of the MAZ-4381EE electric delivery truck is carried out in the paper for various versions of the mechanical part of power unit: without a reducer, with the use of a reducer and two types of two-speed gearboxes (shaft and shaft-planetary). The evaluation is made based on consideration of the European test driving cycle NEDC, taking into account the use of efficiency maps of the traction induction electric motor. The calculation results showed that the use of two-speed gearboxes can reduce the total energy consumption by more than 1.8 times compared to a power unit with a high-torque motor and without a gearbox.

scholarly journals Energy consumption of agitators in activated sludge tanks – actual state and optimization potential

2017 ◽  
Vol 77 (3) ◽  
pp. 800-808 ◽  
Author(s):  
K. Füreder ◽  
K. Svardal ◽  
W. Frey ◽  
H. Kroiss ◽  
J. Krampe
Keyword(s):  
Energy Consumption ◽  
Power Density ◽  
Specific Energy ◽  
Operating Time ◽  
Specific Energy Consumption ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Actual State ◽  
Total Energy Consumption ◽  
Continuous Mixing

Abstract Depending on design capacity, agitators consume about 5 to 20% of the total energy consumption of a wastewater treatment plant. Based on inhabitant-specific energy consumption (kWh PE120−1 a−1; PE120 is population equivalent, assuming 120 g chemical oxygen demand per PE per day), power density (W m−3) and volume-specific energy consumption (Wh m−3 d−1) as evaluation indicators, this paper provides a sound contribution to understanding energy consumption and energy optimization potentials of agitators. Basically, there are two ways to optimize agitator operation: the reduction of the power density and the reduction of the daily operating time. Energy saving options range from continuous mixing with low power densities of 1 W m−3 to mixing by means of short, intense energy pulses (impulse aeration, impulse stirring). However, the following correlation applies: the shorter the duration of energy input, the higher the power density on the respective volume-specific energy consumption isoline. Under favourable conditions with respect to tank volume, tank geometry, aeration and agitator position, mixing energy can be reduced to 24 Wh m−3 d−1 and below. Additionally, it could be verified that power density of agitators stands in inverse relation to tank volume.

New Deadline-Aware Energy-Consumption Optimization Model and Genetic Algorithm Under Cloud Computing

2016 ◽  
Vol 30 (03) ◽  
pp. 1659006 ◽  
Author(s):  
Hai Zhu ◽  
Hongfeng Wang
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Optimization Model ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
Time Constraints ◽  
Primary Concern ◽  
Total Energy Consumption ◽  
Energy Consumption Optimization

With large-scale data centers widely deployed around the world, their huge energy consumption becomes a primary concern. Effective resource allocation and scheduling is one of the key to solve this problem. However, existing studies on this topic are relatively rare. In this paper, a new deadline-aware energy-consumption optimization model is designed, which optimizes both the idle and execution energy consumption of servers. To save the idle energy consumption, we propose a new virtual machine deployment algorithm for mapping virtual machines to a constrained packing problem with multidimensional variables. In the proposed genetic algorithm, in order to improve the diversity of the population, we select some of the individuals which do not satisfy time constraints but have low energy consumption into the next generation. To save the execution energy consumption, we adopt the technique of dynamic voltage and frequency scaling. Finally, experimental results show that compared with the existing algorithms, the proposed one greatly reduces the total energy consumption of data centers under the time constraints of tasks.

scholarly journals An Optimization Model of Integrated AGVs Scheduling and Container Storage Problems for Automated Container Terminal Considering Uncertainty

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1904
Author(s):  
Wentao Jian ◽  
Jishuang Zhu ◽  
Qingcheng Zeng
Keyword(s):  
Optimization Model ◽  
Container Terminal ◽  
Operation Time ◽  
Triangular Fuzzy Number ◽  
Automated Guided Vehicles ◽  
Improved Genetic Algorithm ◽  
Running Time ◽  
Location Allocation ◽  
Storage Location ◽  
Maximum Completion Time

The running path of automated guided vehicles (AGVs) in the automated terminal is affected by the storage location of containers and the running time caused by congestion, deadlock and other problems during the driving process is uncertain. In this paper, considering the different AGVs congestion conditions along the path, a symmetric triangular fuzzy number is used to describe the AGVs operation time distribution and a multi-objective scheduling optimization model is established to minimize the risk of quay cranes (QCs) delay and the shortest AGVs operation time. An improved genetic algorithm was designed to verify the effectiveness of the model and algorithm by comparing the results of the AGVs scheduling and container storage optimization model based on fixed congestion coefficient under different example sizes. The results show that considering the AGVs task allocation and container storage location allocation optimization scheme with uncertain running time can reduce the delay risk of QCs, reduce the maximum completion time and have important significance for improving the loading and unloading efficiency of the automated terminal.

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