scholarly journals Energy Saving and Low-Cost-Oriented Design Processes of Blank’s Dimensions Based on Multi-Objective Optimization Model

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 811 ◽  
Author(s):  
Yongmao Xiao ◽  
Qingshan Gong ◽  
Xiaowu Chen
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Optimization Model ◽  
Low Cost ◽  
Minimum Cost ◽  
Processing Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Set Up

The blank’s dimensions are an important focus of blank design as they largely determine the energy consumption and cost of manufacturing and further processing the blank. To achieve energy saving and low cost during the optimization of blank dimensions design, we established energy consumption and cost objectives in the manufacturing and further processing of blanks by optimizing the parameters. As objectives, we selected the blank’s production and further processing parameters as optimization variables to minimize energy consumption and cost, then set up a multi-objective optimization model. The optimal blank dimension was back calculated using the parameters of the minimum processing energy consumption and minimum cost state, and the model was optimized using the non-dominated genetic algorithm-II (NSGA-II). The effect of designing blank dimension in saving energy and costs is obvious compared with the existing methods.

scholarly journals Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

2021 ◽  
Vol 13 (4) ◽  
pp. 1929
Author(s):  
Yongmao Xiao ◽  
Wei Yan ◽  
Ruping Wang ◽  
Zhigang Jiang ◽  
Ying Liu
Keyword(s):  
Optimization Model ◽  
Optimization Design ◽  
Design Method ◽  
Low Cost ◽  
Route Optimization ◽  
Low Carbon ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Blank Design ◽  
Process Route

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

Virtual Machines Scheduling Algorithm Based on Multi-Objective Optimization in Cloud Computing

2014 ◽  
Vol 1046 ◽  
pp. 508-511
Author(s):  
Jian Rong Zhu ◽  
Yi Zhuang ◽  
Jing Li ◽  
Wei Zhu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Machine Scheduling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Virtual Machine Scheduling ◽  
Set Up

How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.

Consumption-based multi-objective optimization model for minimizing energy consumption: A case study of China

Energy ◽  
2020 ◽  
Vol 208 ◽  
pp. 118384
Author(s):  
Meihui Jiang ◽  
Haizhong An ◽  
Xiangyun Gao ◽  
Donghui Liu ◽  
Nanfei Jia ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Optimization Model ◽  
Multi Objective Optimization ◽  
Multi Objective

Two-Sided Matching with Ordinal Numbers and Costs

2014 ◽  
Vol 587-589 ◽  
pp. 2299-2302
Author(s):  
Qi Yue
Keyword(s):  
Optimization Model ◽  
Ordinal Number ◽  
Weighted Sums ◽  
Multi Objective Optimization ◽  
Matching Problem ◽  
Multi Objective ◽  
Ordinal Numbers ◽  
Objective Model ◽  
Set Up ◽  
The Cost

A novel decision method for solving the two-sided matching problem is proposed in this paper, in which the preferences provided by agents are in the format of ordinal numbers and the preference provided by intermediary is in the format of costs. The concept of two-sided matching is firstly introduced, and the two-sided matching problem with ordinal numbers and costs is discribed. Then the related concepts on costs are given. Considering the ordinal number of each agent and the cost of intermediary, a multi-objective optimization model is set up. The method of weighted sums based on membership function is used to convert the multi-objective optimization model into a single-objective model. By solving the model, the matching alternative can be obtained.

Manufacturer Order Fulfillment Based on Multi-Objective Optimization NSGA II Model

2013 ◽  
Vol 340 ◽  
pp. 136-140
Author(s):  
Liang You Shu ◽  
Ling Xiao Yang
Keyword(s):  
Optimization Model ◽  
Optimization Problems ◽  
Population Based ◽  
Pareto Optimal ◽  
Order Fulfillment ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Local Search Procedure

The aim of this paper is to study the production and delivery decision problem in the Manufacturer Order Fulfillment. Owing to the order fulfillment optimization condition of the manufacturer, the multi-objective optimization model of manufacturers' production and delivery has been founded. The solution of the multi-objective optimization model is also very difficult. Fast and Elitist Non-dominated Sorting Genetic Algorithm (NSGA II) have been applied successfully to various test and real-world optimization problems. These population based the algorithm provide a diverse set of non-dominated solutions. The obtained non-dominated set is close to the true Pareto-optimal front. But its convergence to the true Pareto-optimal front is not guaranteed. Hence SBX is used as a local search procedure. The proposed procedure is successfully applied to a special case. The results validate that the algorithm is effective to the multi-objective optimization model.

scholarly journals Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1474 ◽  
Author(s):  
Leehter Yao ◽  
Jin-Hao Huang
Keyword(s):  
Energy Saving ◽  
Air Temperature ◽  
Data Center ◽  
Air Conditioning ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Air Conditioning System ◽  
Multi Objective ◽  
Control Scheme ◽  
Energy Saving Control

A multi-objective optimization scheme is proposed to save energy for a data center air conditioning system (ACS). Since the air handling units (AHU) and chillers are the most energy consuming facilities, the proposed energy saving control scheme aims to maximize the saved energy for these two facilities. However, the rack intake air temperature tends to increase if the energy saving control scheme applied to AHU and chillers is conducted inappropriately. Both ACS energy consumption and rack intake air temperature stabilization are set as two objectives for multi-objective optimization. The non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimization problem. In order for the NSGA-II to evaluate fitness functions that are both the ACS total power consumption and AHU outlet cold air temperature deviations from a specified range, neural network models are utilized. Feedforward neural networks are utilized to learn the power consumption models for both chillers and AHUs as well as the AHU outlet cold air temperature based on the recorded data collected in the field. The effectiveness and efficiency of the proposed energy saving control scheme is verified through practical experiments conducted on a campus data center ACS.

Parameter Optimization of Classroom Energy-Saving Controller Based on Fuzzy Multi-Objective Optimization Algorithm

2011 ◽  
Vol 393-395 ◽  
pp. 867-870
Author(s):  
Yi Zhong Wang ◽  
Jun Chen ◽  
Li Li Wang ◽  
Qing Xin Meng
Keyword(s):  
Fuzzy Logic ◽  
Energy Saving ◽  
Parameter Optimization ◽  
Optimization Algorithm ◽  
Optimization Model ◽  
Electric Energy ◽  
Illumination Intensity ◽  
Multi Objective Optimization ◽  
Multi Objective

In order to save electric energy for classroom lighting in universities, energy-saving controllers are used to control turning-on and turning-off of lights in a classroom based on the illumination intensity and the usage conditions of the classroom. Three parameters for an energy-saving controller, namely turning-on threshold, turning-off threshold, and delaying time, should be set before usage, which is usually done based on a user’s experience. In this paper, for optimizing the parameters, a multi-objective optimization model for an energy-saving controller is established. Then, fuzzy logic is used to optimize the parameters. In this way, an energy-saving controller can be used well.

Using NSGA-II to solve multi-objective competitive location problem with cooperative coverage for distance-based attractiveness

2021 ◽  
pp. 1-12
Author(s):  
Sheng-Chuan Wang ◽  
Ta-Cheng Chen
Keyword(s):  
Genetic Algorithms ◽  
Optimization Model ◽  
Location Problem ◽  
Solution Procedure ◽  
Competitive Location ◽  
Multi Objective Optimization ◽  
Demand Point ◽  
Nsga Ii ◽  
Solution Sets ◽  
Multi Objective

Multi-objective competitive location problem with cooperative coverage for distance-based attractiveness is introduced in this paper. The potential facilities compete to be selected to serve all demand points which are determined by maximizing total collective attractiveness of all demand points from assigned facilities and minimizing the fixed and distance costs between all demand points and selected facilities. Facility attractiveness is represented as a coverage of the facility with full, partial and none coverage corresponding to maximum full and partial coverage radii. Cooperative coverage, which the demand point is covered by at least one facility, is also considered. The problem is formulated as a multi-objective optimization model and solution procedure based on elitist non-dominated sorting genetic algorithms (NSGA-II) is developed. Experimental example demonstrates the best non-dominated solution sets obtained by developed solution procedure. Contributions of this paper include introducing competitive location problem with facility attractiveness as a distance-based coverage of the facility, re-categorizing facility coverage classification and developing solution procedure base upon NSGA-II.

scholarly journals Multi-objective Optimization of Grid Computing for Performance, Energy and Cost

2017 ◽  
Vol 2 (3) ◽  
pp. 74-79
Author(s):  
Ahmed Badri Muslim Fanfakhri ◽  
Ali Yakoob Yousif ◽  
Esraa Alwan
Keyword(s):  
Energy Consumption ◽  
Message Passing ◽  
Minimum Energy ◽  
Minimum Cost ◽  
Parallel Applications ◽  
Jacobi Method ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Minimum Energy Consumption ◽  
Parallel Iterative

In this paper, new multi-objective optimization algorithm is proposed. It optimizes the execution time, the energy consumption and the cost of booked nodes in the grid architecture at the same time. The proposed algorithm selects the best frequencies depends on a new optimization function that optimized these three objectives, while giving equivalent trade-off for each one. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption of the message passing parallel iterative method executed over grid. DVFS is also reduced the computing power of each processor executing the parallel applications. Therefore, the performance of these applications is decreased and so on the payed cost for the booking nodes is increased.  However, the proposed multi-objective algorithm gives the minimum energy consumption and minimum cost with maximum performance at the same time. The proposed algorithm is evaluated on the SimGrid/SMPI simulator while running the parallel iterative Jacobi method. The experiments show that it reduces on average the energy consumption by up to 19.7 %, while limiting the performance and cost degradations to 3.2 % and 5.2 % respectively.

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