Peach Harvest System Optimization Model

1978 ◽  
Vol 21 (5) ◽  
pp. 0817-0821
Author(s):  
I. Amir ◽  
R. A. Genge ◽  
W. K. Bilanski ◽  
D. R. Menzies
Keyword(s):  
Optimization Model ◽  
System Optimization

Optimization design of drive system for industrial robots based on dynamic performance

2017 ◽  
Vol 44 (6) ◽  
pp. 765-775 ◽  
Author(s):  
LianZheng Ge ◽  
Jian Chen ◽  
Ruifeng Li ◽  
Peidong Liang
Keyword(s):  
Optimization Model ◽  
Optimization Problem ◽  
Industrial Robot ◽  
Dynamic Performance ◽  
System Optimization ◽  
Drive System ◽  
Industrial Robots ◽  
Mixed Integer ◽  
Light Weight ◽  
Content Type

Purpose The global performance of industrial robots partly depends on the properties of drive system consisting of motor inertia, gearbox inertia, etc. This paper aims to deal with the problem of optimization of global dynamic performance for robotic drive system selected from available components. Design/methodology/approach Considering the performance specifications of drive system, an optimization model whose objective function is composed of working efficiency and natural frequency of robots is proposed. Meanwhile, constraints including the rated and peak torque of motor, lifetime of gearbox and light-weight were taken into account. Furthermore, the mapping relationship between discrete optimal design variables and component properties of drive system were presented. The optimization problem with mixed integer variables was solved by a mixed integer-laplace crossover power mutation algorithm. Findings The optimization results show that our optimization model and methods are applicable, and the performances are also greatly promoted without sacrificing any constraints of drive system. Besides, the model fits the overall performance well with respect to light-weight ratio, safety, cost reduction and others. Practical implications The proposed drive system optimization method has been used for a 4-DOF palletizing robot, which has been largely manufactured in a factory. Originality/value This paper focuses on how the simulation-based optimization can be used for the purpose of generating trade-offs between cost, performance and lifetime when designing robotic drive system. An applicable optimization model and method are proposed to handle the dynamic performance optimization problem of a drive system for industrial robot.

Factorial Two-Stage Irrigation System Optimization Model

2016 ◽  
Vol 142 (2) ◽  
pp. 04015056 ◽  
Author(s):  
Xiaying Xin ◽  
Guohe Huang ◽  
Wei Sun ◽  
Yang Zhou ◽  
Yurui Fan
Keyword(s):  
Optimization Model ◽  
Irrigation System ◽  
System Optimization ◽  
Two Stage

Multi-criterion system optimization model for urban multimodal traffic network

2011 ◽  
Vol 54 (4) ◽  
pp. 947-954 ◽  
Author(s):  
BingFeng Si ◽  
HongYun Zhang ◽  
Ming Zhong ◽  
XiaoBao Yang
Keyword(s):  
Optimization Model ◽  
System Optimization ◽  
Traffic Network ◽  
Criterion System

Automatic Integration of System-Level Design and System Optimization Based on SysML

2012 ◽  
Vol 249-250 ◽  
pp. 1154-1159
Author(s):  
Yu Sheng Liu ◽  
Wen Qiang Yuan
Keyword(s):  
System Design ◽  
Systems Engineering ◽  
Optimization Model ◽  
System Modeling ◽  
System Optimization ◽  
System Level ◽  
Design Parameters ◽  
System Level Design ◽  
Model Based ◽  
Level Design

Model based systems engineering (MBSE) is becoming a promising approach for the system-level design of complex mechatronics. And several MBSE tools are developed to conduct system modeling. However, the system design cannot be optimized in current MBSE tools. In this study, an approach is presented to conduct the task. A set of optimization stereotype is defined at first which is used to formalize the optimization model based on the system design model. Then the design parameters and their relationships applied optimization stereotypes are extracted and transferred to construct the tool-dependent optimization model. Finally, the optimization model is solved and the results are given back and then modify the corresponding system model automatically. In this paper, MagicDraw is used to model the whole system whereas Matlab optimizer is used for optimization. The combustion engine is chosen as the example to illustrate the proposed approach.

Robust Optimization of Steam Power System

2013 ◽  
Vol 860-863 ◽  
pp. 2606-2609
Author(s):  
Ran Ding ◽  
Guo Xiang Li
Keyword(s):  
Power System ◽  
Robust Optimization ◽  
Optimization Model ◽  
System Optimization ◽  
Uncertain Parameters ◽  
Steam Power ◽  
Power System Optimization ◽  
Steam Power System ◽  
System Optimal ◽  
Simulation Results

In steam power system optimal problems, uncertain parameters should be considered unless the solution will be infeasible. The uncertain parameters and constraints in steam power system optimization model are analyzed. Then the related constraints with uncertain parameters which used to be expressed by joint chance constraints are approximated, and a robust optimization model of steam power system is proposed. The simulation results illustrate the validity of the model.

scholarly journals Hierarchical Decomposition Synthesis in Optimal Systems Design

1997 ◽  
Vol 119 (4) ◽  
pp. 448-457 ◽  
Author(s):  
R. S. Krishnamachari ◽  
P. Y. Papalambros
Keyword(s):  
Optimal Design ◽  
Optimization Model ◽  
Multicriteria Optimization ◽  
Systems Design ◽  
System Optimization ◽  
Hierarchical Decomposition ◽  
Objective Functions ◽  
Formal Process ◽  
Large Systems ◽  
System Objective

Optimal design of large systems is easier if the optimization model can be decomposed and solved as a set of smaller, coordinated subproblems. Casting a given design problem into a particular optimization model by selecting objectives and constraints is generally a subjective task. In system models where hierarchical decomposition is possible, a formal process for selecting objective functions can be made, so that the resulting optimal design model has an appropriate decomposed form and also possesses desirable properties for the scalar substitute functions used in multicriteria optimization. Such a process is often followed intuitively during the development of a system optimization model by summing selected objectives from each subsystem into a single overall system objective. The more formal process presented in this article is simple to implement and amenable to automation.

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