scholarly journals Design of super-elliptical gradient coils based on multiple objective Pareto optimization method

2017 ◽  
Vol 66 (9) ◽  
pp. 098301
Author(s):  
Pan Hui ◽  
Wang Liang ◽  
Wang Qiang-Long ◽  
Chen Li-Min ◽  
Jia Feng ◽  
...  
Keyword(s):  
Optimization Method ◽  
Pareto Optimization ◽  
Multiple Objective ◽  
Gradient Coils

ENHANCED INTERACTIVE SATISFYING OPTIMIZATION APPROACH TO MULTIPLE OBJECTIVE OPTIMIZATION WITH PREEMPTIVE PRIORITIES

2006 ◽  
Vol 05 (01) ◽  
pp. 47-63 ◽  
Author(s):  
CHAOFANG HU ◽  
SHAOYUAN LI
Keyword(s):  
Optimization Problem ◽  
Optimization Method ◽  
Previous Method ◽  
Optimization Models ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Optimization Approach ◽  
Fuzzy Relations ◽  
Objective Functions ◽  
Preemptive Priority

This paper proposes an enhanced interactive satisfying optimization method based on goal programming for the multiple objective optimization problem with preemptive priorities. Based on the previous method, the approach presented makes the higher priority achieve the higher satisfying degree. For three fuzzy relations of the objective functions, the corresponding optimization models are proposed. Not only can satisfying results for all the objectives be acquired, but the preemptive priority requirement can also be simultaneously actualized. The balance between optimization and priorities is realized. We demonstrate the power of this proposed method by illustrative examples.

J1030205 Identification of Dynamic Structural Model Using Multiple Objective Optimization Method

2015 ◽  
Vol 2015 (0) ◽  
pp. _J1030205--_J1030205- ◽  
Author(s):  
Yuki MIMURA ◽  
Masayuki ICHIMONJI ◽  
Kyohei HIRAI ◽  
Toshikazu NAGATA ◽  
Toshiaki HIRATE ◽  
...  
Keyword(s):  
Structural Model ◽  
Optimization Method ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Dynamic Structural Model

Integrating a Weighted Additive Multiple Objective Linear Model with Possibilistic Linear Programming for Fuzzy Aggregate Production Planning Problems

2020 ◽  
Vol 9 (2) ◽  
pp. 1-30
Author(s):  
Navee Chiadamrong ◽  
Noppasorn Sutthibutr
Keyword(s):  
Linear Programming ◽  
Linear Model ◽  
Production Planning ◽  
Hybrid Approach ◽  
Optimization Method ◽  
Multiple Objective ◽  
Aggregate Production Planning ◽  
Aggregate Production ◽  
Planning Problems ◽  
Possibilistic Linear Programming

This study uses an integrated optimization method by applying a weighted additive multiple objective linear model with Possibilistic Linear Programming (PLP) to fuzzy Aggregate Production Planning (APP) problems under an uncertain environment. The uncertainty conditions include uncertainties of operating times and costs, customer demand, labor level, as well as machine capacity. The aim of this study is to minimize total costs of the plan that consist of the production cost and costs of changes in labor level. The proposed hybrid approach minimizes the most possible value of the imprecise total costs, maximizes the possibility of obtaining lower total costs, and minimizes the risk of obtaining higher total costs from PLP as multiple objectives for the fuzzy multiple objective linear model optimization. The outcome of the proposed approach shows that the solution is closer to the ideal solution obtained from Linear Programming than a typical solution obtained from PLP. There is also a higher overall satisfaction value.

TWO-PHASE INTERACTIVE SATISFYING METHOD OF FUZZY MULTIPLE OBJECTIVE OPTIMIZATION WITH LINGUISTIC PREFERENCE

2009 ◽  
Vol 08 (03) ◽  
pp. 427-443 ◽  
Author(s):  
CHAOFANG HU ◽  
SHAOYUAN LI
Keyword(s):  
Decision Maker ◽  
Optimization Problem ◽  
Optimization Method ◽  
Multiple Objectives ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Two Phase ◽  
Numerical Example ◽  
Complex Optimization ◽  
Important Objective

This paper presents a two-phase interactive satisfying optimization method for fuzzy multiple objectives optimization with linguistic preference. This proposed approach utilizes the view that the more important objective has the higher desirable satisfying degree. The originally complex optimization problem is simplified and divided into two parts that are solved one by one. The decision maker can acquire satisfying solution of all the objectives under linguistic preference. Numerical example shows the efficiency, flexibility, and sensitivity of the proposed method.

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