Multi-objective optimization of machining parameters considering energy consumption

2013 ◽  
Vol 71 (5-8) ◽  
pp. 1133-1142 ◽  
Author(s):  
Qiulian Wang ◽  
Fei Liu ◽  
Xianglian Wang
Keyword(s):  
Energy Consumption ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals A Dynamic Multi-Swarm Particle Swarm Optimizer for Multi-Objective Optimization of Machining Operations Considering Efficiency and Energy Consumption

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2616
Author(s):  
Lijun Song ◽  
Jing Shi ◽  
Anda Pan ◽  
Jie Yang ◽  
Jun Xie
Keyword(s):  
Energy Consumption ◽  
Particle Swarm ◽  
Economic Benefits ◽  
Machining Parameters ◽  
Manufacturing Companies ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Multi Objective ◽  
Use Of Resources ◽  
Machining Operations

Facing energy shortage and severe environmental pollution, manufacturing companies need to urgently energy consumption, make rational use of resources and improve economic benefits. This paper formulates a multi-objective optimization model for lathe turning operations which aims to simultaneously minimize energy consumption, machining cost and cutting time. A dynamic multi-swarm particle swarm optimizer (DMS-PSO) is proposed to solve the formulation. A case study is provided to illustrate the effectiveness of the proposed algorithm. The results show that the DMS-PSO approach can ensure good convergence and diversity of the solution set. Additionally, the optimal machining parameters are identified by fuzzy comprehensive evaluation (FCE) and compared with empirical parameters. It is discovered that the optimal parameters obtained from the proposed algorithm outperform the empirical parameters in all three objectives. The research findings shed new light on energy conservation of machining operations.

scholarly journals Fuzzy-TOPSIS based Multi-objective Optimization of Machining Parameters for Improving Energy Consumption and Productivity

Procedia CIRP ◽  
2021 ◽  
Vol 102 ◽  
pp. 192-197
Author(s):  
Shailendra Pawanr ◽  
Tanmay Tanishk ◽  
Anuj Gulati ◽  
Girish Kant Garg ◽  
Srikanta Routroy
Keyword(s):  
Energy Consumption ◽  
Fuzzy Topsis ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective

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.

Towards A Virtual Machine Migration Algorithm Based On Multi-Objective Optimization

2017 ◽  
Vol 8 (3) ◽  
pp. 79-89 ◽  
Author(s):  
Xiang Chen ◽  
Jun-rong Tang ◽  
Yong Zhang
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Resource Competition ◽  
Service Level Agreement ◽  
Service Level ◽  
Multi Objective Optimization ◽  
Virtual Machine Migration ◽  
Multi Objective ◽  
Time Migration ◽  
Management Method

In the cloud computing, the virtual machine (VM) dynamical management method needs to consider VM resource re-configuration caused by system computation resource status changing and load fluctuation. Based on migration objectives as QoS (Quality of Service), resource competition and energy consumption, the VM migration time, migration objective node selection and VM placement strategies are designed in this work. The Multi-Criteria Decision-Making (MCDM) method is also introduced for migration destination host selection. Experiment results show that the multi-objective optimization management method with TOPSIS can achieve lower service-level agreement (SLA) violation rate, less energy consumption and better balance among different objectives.

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