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.

scholarly journals Hierarchical Decomposition Synthesis in Optimal Systems Design

1996 ◽  
Author(s):  
Ramprasad S. Krishnamachari ◽  
Panos Y. Papalambros
Keyword(s):  
Optimal Design ◽  
Optimization Model ◽  
Design Problem ◽  
Systems Design ◽  
Design Model ◽  
Hierarchical Decomposition ◽  
Design Models ◽  
Large Systems

Abstract 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. This article describes how such a subjective selection can be made so that the resulting optimal design model can be directly partitioned into an appropriate decomposed form. This process is termed decomposition synthesis. A particular methodology for synthesizing hierarchically decomposed optimal design models is presented together with examples.

scholarly journals Interval Optimization-Based Optimal Design of Distributed Energy Resource Systems under Uncertainties

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3465
Author(s):  
Da Li ◽  
Shijie Zhang ◽  
Yunhan Xiao
Keyword(s):  
Optimal Design ◽  
Optimization Model ◽  
Energy Resource ◽  
System Optimization ◽  
Optimal Operation ◽  
Weighting Coefficient ◽  
Interval Optimization ◽  
Distributed Energy ◽  
Interval Numbers ◽  
Distributed Energy Resource

Distributed energy resource (DER) systems have elicited increasing attention and applications because of their excellent economic and environmental performance. However, uncertainties exist in such systems, preventing their potential advantages to be realized. In this study, an interval optimization-based model for the optimal design of DER systems is proposed, considering uncertainties in energy prices, renewable energy intensity, and load demands. Uncertainties are described as interval numbers, and the uncertain optimization model is transformed into a deterministic optimization problem using the order relationship and probability degree of interval numbers. The proposed model is applied to a typical hospital in Lianyungang, China, and its effectiveness is verified. One deterministic case and three uncertain cases are designed. The effects of uncertainties on system configuration and economic performance are also analyzed, and the optimal operation strategy under the three uncertainties is determined. A sensitivity analysis is conducted to analyze the effects of probability degree and weighting coefficient on total annual cost. Results show that uncertainties exert a cumulative effect on system optimization outcomes, and the proposed interval optimization model can obtain robust solutions to uncertainties.

Multi-criteria optimization of the four-finger gripper mechanism

2020 ◽  
Vol 8 (4) ◽  
pp. 276-286
Author(s):  
Vu Duc Quyen ◽  
Andrey Ronzhin
Keyword(s):  
Multicriteria Optimization ◽  
Kinematic Model ◽  
Objective Functions ◽  
Nsga Ii ◽  
Suction Nozzle

Three posterior algorithms NSGA-II, MOGWO and MOPSO to solve the problem of multicriteria optimization of the robotic gripper design are considered. The description of the kinematic model of the developed prototype of the four-fingered gripper for picking tomatoes, its limitations and objective functions used in the optimization of the design are given. The main advantage of the developed prototype is the use of one actuator for the control of the fingers and the suction nozzle. The results of optimization of the kinematic model and the dimensions of the elements of robotic gripper using the considered posterior algorithms are presented.

Optimal design of 6-DOF eclipse mechanism based on task-oriented workspace

Robotica ◽  
2011 ◽  
Vol 30 (7) ◽  
pp. 1041-1048 ◽  
Author(s):  
Donghun Lee ◽  
Jongwon Kim ◽  
TaeWon Seo
Keyword(s):  
Optimal Design ◽  
Unit Length ◽  
Numerical Procedure ◽  
Design Procedure ◽  
Mass Reduction ◽  
Objective Functions ◽  
Design Efficiency ◽  
Task Oriented ◽  
Structural Mass ◽  
Optimal Set

SUMMARYWe present a new numerical optimal design for a redundant parallel manipulator, the eclipse, which has a geometrically symmetric workspace shape. We simultaneously consider the structural mass and design efficiency as objective functions to maximize the mass reduction and minimize the loss of design efficiency. The task-oriented workspace (TOW) and its partial workspace (PW) are considered in efficiently obtaining an optimal design by excluding useless orientations of the end-effector and by including just one cross-sectional area of the TOW. The proposed numerical procedure is composed of coarse and fine search steps. In the coarse search step, we find the feasible parameter regions (FPR) in which the set of parameters only satisfy the marginal constraints. In the fine search step, we consider the multiobjective function in the FPR to find the optimal set of parameters. In this step, fine search will be kept until it reaches the optimal set of parameters that minimize the proposed objective functions by continuously updating the PW in every iteration. By applying the proposed approach to an eclipse-rapid prototyping machine, the structural mass of the machine can be reduced by 8.79% while the design efficiency is increased by 6.2%. This can be physically interpreted as a mass reduction of 49 kg (the initial structural mass was 554.7 kg) and a loss of 496 mm3/mm in the workspace volume per unit length. The proposed optimal design procedure could be applied to other serial or parallel mechanism platforms that have geometrically symmetric workspace shapes.

scholarly journals A Multiobjective Scheduling Optimization Model for Multienergy Complementary System Integrated by Wind-Photovoltaic-Convention Gas Turbines considering Demand Response

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Zhang Lihui ◽  
Xin He ◽  
Ju Liwei
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Gas Turbines ◽  
Risk Level ◽  
Objective Functions ◽  
Entropy Weight ◽  
Scheduling Optimization ◽  
Power Sources ◽  
Complementary System ◽  
Scheduling Model

To utilize the complementary feature of different power sources, wind power plant (WPP), and solar photovoltaic power (PV), convention gas turbines (CGT) and incentive-based demand response (IBDR) are integrated into a multienergy complementary system (MECS) with the implementation of price-based demand response (PBDR). Firstly, the power output model of WPP, PV, and CGT is constructed and the mathematical model of DR is presented. Then, a multiobjective scheduling model is proposed for MECS operation under the objective functions of the maximum economic benefit, the minimum abandoned energy, and the minimum risk level. Thirdly, the payoff table of objective functions is put forward for converting the multiobjective model into a single objective model by using entropy weight method to calculate weighting coefficients of different objective functions. Finally, the improved IEEE 30 bus system is taken as the simulation system with four simulation scenarios for comparatively analyzing the influence of PBDR and IBDR on MECS operation. The simulation results show the following: (1) The MECS fully utilized the complementarity of different power sources; CGT and IBDR can provide peaking service for WPP and PV to optimize overall system operation. (2) The proposed algorithm can solve the MECS multiobjective scheduling optimization model, and the system scheduling results in the comprehensive optimal mode can take into account different appeal. And the total revenue, abandoned energy capacity, and load fluctuation are, respectively, 108009.30¥, 11.62 MW h, and 9.74 MW. (3) PBDR and IBDR have significant synergistic optimization effects, which can promote the grid connection of WPP and PV. When they are both introduced, the peak-to-valley ratio of the load curve is 1.19, and the abandoned energy is 5.85 MW h. Therefore, the proposed MECS scheduling model and solution algorithm could provide the decision basis for decision makers based on their actual situation.

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.

Study on Optimization for Electric-Mechanic Transmission Parameters

2014 ◽  
Vol 654 ◽  
pp. 221-228
Author(s):  
Yi Yuan ◽  
Jiang Tao Gai ◽  
Zheng Da Han ◽  
Xin Zhang ◽  
Fan Wan
Keyword(s):  
Optimal Design ◽  
Optimization Model ◽  
Design Theory ◽  
Transmission Parameters ◽  
Motor Design ◽  
Genetic Arithmetic

Based on the optimal design theory, the optimization model for electric-mechanic transmission parameters is established in this paper, and genetic arithmetic is used to solve the optimization model. Comparing the transmission’s performance before optimization with that after optimization, the result shows that the motor design difficulty is reduced and the transmission’s performance is improved after optimaization.

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