Research on the spatial optimal aggregation method of decision maker preference information based on Steiner-Weber points

2021 ◽  
pp. 107819
Author(s):  
Wei Liu ◽  
Yuhong Wang
Keyword(s):  
Decision Maker ◽  
Aggregation Method ◽  
Preference Information

scholarly journals Assessing the Performance of Interactive Multiobjective Optimization Methods

2021 ◽  
Vol 54 (4) ◽  
pp. 1-27
Author(s):  
Bekir Afsar ◽  
Kaisa Miettinen ◽  
Francisco Ruiz
Keyword(s):  
Multiobjective Optimization ◽  
Decision Maker ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Interactive Methods ◽  
Value Functions ◽  
Technical Aspects ◽  
Preference Information ◽  
Multiobjective Optimization Problems ◽  
Human Decision

Interactive methods are useful decision-making tools for multiobjective optimization problems, because they allow a decision-maker to provide her/his preference information iteratively in a comfortable way at the same time as (s)he learns about all different aspects of the problem. A wide variety of interactive methods is nowadays available, and they differ from each other in both technical aspects and type of preference information employed. Therefore, assessing the performance of interactive methods can help users to choose the most appropriate one for a given problem. This is a challenging task, which has been tackled from different perspectives in the published literature. We present a bibliographic survey of papers where interactive multiobjective optimization methods have been assessed (either individually or compared to other methods). Besides other features, we collect information about the type of decision-maker involved (utility or value functions, artificial or human decision-maker), the type of preference information provided, and aspects of interactive methods that were somehow measured. Based on the survey and on our own experiences, we identify a series of desirable properties of interactive methods that we believe should be assessed.

scholarly journals Comparing interactive evolutionary multiobjective optimization methods with an artificial decision maker

2021 ◽  
Author(s):  
Bekir Afsar ◽  
Ana B. Ruiz ◽  
Kaisa Miettinen
Keyword(s):  
Multiobjective Optimization ◽  
Decision Maker ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Decision Makers ◽  
Interactive Methods ◽  
Preference Information ◽  
Evolutionary Methods ◽  
Solution Processes ◽  
Trade Offs

AbstractSolving multiobjective optimization problems with interactive methods enables a decision maker with domain expertise to direct the search for the most preferred trade-offs with preference information and learn about the problem. There are different interactive methods, and it is important to compare them and find the best-suited one for solving the problem in question. Comparisons with real decision makers are expensive, and artificial decision makers (ADMs) have been proposed to simulate humans in basic testing before involving real decision makers. Existing ADMs only consider one type of preference information. In this paper, we propose ADM-II, which is tailored to assess several interactive evolutionary methods and is able to handle different types of preference information. We consider two phases of interactive solution processes, i.e., learning and decision phases separately, so that the proposed ADM-II generates preference information in different ways in each of them to reflect the nature of the phases. We demonstrate how ADM-II can be applied with different methods and problems. We also propose an indicator to assess and compare the performance of interactive evolutionary methods.

Two-Decision-Maker Conflict Resolution with Fuzzy Preferences

2014 ◽  
Vol 2 (2) ◽  
pp. 40-59
Author(s):  
Mubarak S. Al-Mutairi
Keyword(s):  
Fuzzy Logic ◽  
Conflict Resolution ◽  
Decision Maker ◽  
Decision Makers ◽  
Fuzzy Approach ◽  
Model Uncertainties ◽  
Preference Information ◽  
Fuzzy Preferences ◽  
Illustrative Application ◽  
Fuzzy Preference

A unique fuzzy approach is developed to model uncertainties in the preferences of a decision maker involved in a conflict. Human judgments, including expressing preferences over a set of feasible outcomes or states in a conflict, are usually imprecise. Situations characterized by vagueness, impreciseness, incompleteness and ambiguity, are often reflected in the decision maker's preferences. When modeling a conflict, it is assumed that the decision makers, the courses of actions available for each, and the preferences of each decision maker are known. When the preferences of the decision maker over a certain set of actions are not known with certainty, this could affect the overall equilibria which are predicted in an analysis. Hence, fuzzy logic is used to handle imprecise or vague preference information so that realistic equilibria can be found. The well-known game of Prisoner's Dilemma, in which one must decide whether or not to cooperate, is employed as an illustrative application to demonstrate how the fuzzy preference methodology works in practice.

scholarly journals A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization

2015 ◽  
Vol 10 (4) ◽  
pp. 508 ◽  
Author(s):  
Ernestas Filatovas ◽  
Dmitry Podkopaev ◽  
Olga Kurasova
Keyword(s):  
Multiobjective Optimization ◽  
Dimensionality Reduction ◽  
Decision Maker ◽  
Optimization Problem ◽  
Interactive Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Visualization Technique ◽  
Preference Information ◽  
Optimal Set

<pre>Interactive methods of <span>multiobjective</span> optimization repetitively derive <span>Pareto</span> optimal solutions based on decision maker's preference information and present the obtained solutions for his/her consideration. Some interactive methods save the obtained solutions into a solution pool and, at each iteration, allow the decision maker considering any of solutions obtained earlier. This feature contributes to the flexibility of exploring the <span>Pareto</span> optimal set and learning about the optimization problem. However, in the case of many objective functions, the accumulation of derived solutions makes accessing the solution pool cognitively difficult for the decision maker. We propose to enhance interactive methods with visualization of the set of solution outcomes using dimensionality reduction and interactive mechanisms for exploration of the solution pool. We describe a proposed visualization technique and demonstrate its usage with an example problem solved using the interactive method NIMBUS.</pre>

Research on the optimal aggregation method of fuzzy preference information based on spatial Steiner-Weber point

2021 ◽  
pp. 1-19
Author(s):  
Wei Liu ◽  
Yuhong Wang
Keyword(s):  
Three Dimensional ◽  
Spatial Aggregation ◽  
Decision Makers ◽  
Aggregation Method ◽  
Group Preference ◽  
Preference Information ◽  
Novel Method ◽  
Three Dimensional Coordinate ◽  
Plant Growth Simulation Algorithm ◽  
Fuzzy Preference

In view of the present situation that most aggregation methods of fuzzy preference information are extended or mixed by classical aggregation operators, which leads to the aggregation accuracy is not high. The purpose of this paper is to develop a novel method for spatial aggregation of fuzzy preference information. Thus we map the fuzzy preference information to a set of three-dimensional coordinate and construct the spatial aggregation model based on Steiner-Weber point. Then, the plant growth simulation algorithm (PGSA) algorithm is used to find the spatial aggregation point. According to the comparison and analysis of the numerical example, the aggregation matrix established by our method is closer to the group preference matrices. Therefore, the optimal aggregation point obtained by using the optimal aggregation method based on spatial Steiner-Weber point can best represent the comprehensive opinion of the decision makers.

Model and Algorithm Based on Sum of Deviation Squares for Multi-Criteria Decision-Making

2014 ◽  
Vol 651-653 ◽  
pp. 1603-1606
Author(s):  
Hong An Zhou
Keyword(s):  
Decision Making ◽  
Quadratic Programming ◽  
Decision Maker ◽  
Programming Model ◽  
Multi Criteria Decision Making ◽  
Preference Information ◽  
Criteria Weights

With regard to the multi-criteria decision making (MCDM) problems that the criteria weights are unknown and the decision maker (DM) has avail preference information on alternatives. Firstly, a quadratic programming model is established by using the minimum sum of deviation squares between the subjective and objective decision-making preference information on alternatives. Secondly, the existent condition of solution is given and the calculated formula of the criteria weights are obtained by solving the model, thus the overall values of the alternatives are gained. Based on these values, the ranking priorities on alternatives are processed. Finally, a practical example is illustrated to show the feasibility and availability of the developed model and method.

Interactive Desirability Function Approach to Multi-Response Surface Optimization

2003 ◽  
Vol 10 (02) ◽  
pp. 205-217 ◽  
Author(s):  
In-Jun Jeong ◽  
Kwang-Jae Kim
Keyword(s):  
Response Surface ◽  
Decision Maker ◽  
Desirability Function ◽  
Function Approach ◽  
Interactive Approach ◽  
Preference Information ◽  
Response Characteristics ◽  
Response Surface Optimization ◽  
Desirability Function Approach ◽  
Selection Of

A common problem encountered in product or process design is the selection of optimal parameters that involves simultaneous consideration of multi-response characteristics, called a multi-response surface (MRS) problem. There are several approaches proposed for MRS optimization (MRO), including the priority-based approach, the desirability function approach, and the loss function approach. The existing MRO approaches require that all the preference information of a decision maker be articulated prior to solving the problem. However, it is difficult for the decision maker to articulate all the preference information in advance. This paper proposes an interactive approach, called an interactive desirability function approach (IDFA), to overcome the common limitation of the existing approaches. IDFA focuses on extracting the decision maker's preference information in an interactive manner. IDFA requires no explicit tradeoffs among the responses and gives an opportunity for the decision maker to learn his/her own tradeoff space. Consequently, through IDFA, it is more likely that the decision maker finds a solution which is faithful to his/her preference structure.

A Robust Posterior Method to Multiresponse Optimization Using the VIKOR Method

2012 ◽  
Vol 433-440 ◽  
pp. 3060-3065 ◽  
Author(s):  
Reza Baradaran Kazemzadeh ◽  
Mohammad Reza Amin Naseri ◽  
Ali Salmasnia
Keyword(s):  
Decision Maker ◽  
Quality Characteristics ◽  
Quality Characteristic ◽  
Process Variables ◽  
Vikor Method ◽  
Nondominated Solutions ◽  
Preference Information ◽  
Multiresponse Optimization ◽  
Minimum Variation ◽  
Common Problems

Setting of process variables to meet a required specification of quality characteristic (or response variable) in a process is one of common problems in the process quality control. But generally there are more than one quality characteristics in the process. In these situations, to obtain a satisfactory compromise in such a case, a decision maker (DM)’s preference information about the tradeoffs among the responses should be considered. In some cases, it is difficult for a DM to express an explicit approximation of the preference function. Therefore it can be effective to allow the decision maker to choose from a set of solutions. To this end, an algorithm is used to determine a representation of the nondominated solutions set. Such methods incorporate a posterior articulation of preferences. This paper proposes a posterior method based on taguchi’s signal-to-noise (S/N) ratios to facilitate the preference articulation process and incorporate both systematic and random deviations from target in a single criterion. Finally, the VIKOR method is used to extract of optimal compromise solution that leads to minimum variation in relative deviations of responses.

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