Compounding problem in an interactive multiple objective optimization method

2016 ◽  
Vol 248 (3) ◽  
pp. 1132-1135 ◽  
Author(s):  
K. Sam Park ◽  
Pyoungsoo Lee
Keyword(s):  
Optimization Method ◽  
Multiple Objective ◽  
Multiple Objective Optimization

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

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.

scholarly journals System Dynamics-Multiple Objective Optimization Model for Water Resource Management: A Case Study in Jiaxing City, China

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 671
Author(s):  
Xiaoying Zhou ◽  
Feier Wang ◽  
Kuan Huang ◽  
Huichun Zhang ◽  
Jie Yu ◽  
...  
Keyword(s):  
Resource Management ◽  
System Dynamics ◽  
Water Resource ◽  
Water Allocation ◽  
Water Resource Management ◽  
Water Shortage ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Model Framework

Predicting and allocating water resources have become important tasks in water resource management. System dynamics and optimal planning models are widely applied to solve individual problems, but are seldom combined in studies. In this work, we developed a framework involving a system dynamics-multiple objective optimization (SD-MOO) model, which integrated the functions of simulation, policy control, and water allocation, and applied it to a case study of water management in Jiaxing, China to demonstrate the modeling. The predicted results of the case study showed that water shortage would not occur at a high-inflow level during 2018–2035 but would appear at mid- and low-inflow levels in 2025 and 2022, respectively. After we made dynamic adjustments to water use efficiency, economic growth, population growth, and water resource utilization, the predicted water shortage rates decreased by approximately 69–70% at the mid- and low-inflow levels in 2025 and 2035 compared to the scenarios without any adjustment strategies. Water allocation schemes obtained from the “prediction + dynamic regulation + optimization” framework were competitive in terms of social, economic and environmental benefits and flexibly satisfied the water demands. The case study demonstrated that the SD-MOO model framework could be an effective tool in achieving sustainable water resource management.

Multiple-objective optimization applied in extracting multiple-choice tests

2021 ◽  
Vol 105 ◽  
pp. 104439
Author(s):  
Tram Nguyen ◽  
Toan Bui ◽  
Hamido Fujita ◽  
Tzung-Pei Hong ◽  
Ho Dac Loc ◽  
...  
Keyword(s):  
Multiple Choice ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Multiple Choice Tests ◽  
Choice Tests
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