scholarly journals A new decision-making model for solving multi-objective large-scale programming problems with a block angular structure

2014 ◽  
Vol 2014 ◽  
pp. 1-14
Author(s):  
Meghdad Salimi
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Angular Structure ◽  
Multi Objective ◽  
Decision Making Model

A New Compromise Solution Model Based on Dantzig–Wolfe Decomposition for Solving Belief Multi-Objective Nonlinear Programming Problems with Block Angular Structure

2017 ◽  
Vol 16 (02) ◽  
pp. 333-387 ◽  
Author(s):  
Behnam Vahdani ◽  
Meghdad Salimi ◽  
Seyed Meysam Mousavi
Keyword(s):  
Decision Making ◽  
Nonlinear Programming ◽  
Large Scale ◽  
Dimensional Space ◽  
Independent Solution ◽  
Angular Structure ◽  
Multi Objective ◽  
Model Based ◽  
Solution Methods ◽  
Single Objective

This paper presents an integrated model based on a compromised solution method to solve fuzzy belief multi-objective large-scale nonlinear programming (FBMOLSNLP) problem with block angular structure. A new method is proposed to transfer each belief decision-making problem into some fuzzy problems. Furthermore, we propose a new compromise method of decision-making as one of the most efficient methods based on the particular measure of closeness to the ideal solution to aggregate multi-objective decision-making (MODM) problems into a single problem. The decomposition algorithm based on Dantzig–Wolfe is utilized to reduce the large-dimensional objective space into a two-dimensional space. Then, Zimmerman method is applied to transfer each bi-objective to a single-objective. Moreover, TOPSIS and VIKOR are utilized as two independent solution methods to aggregate each multi-objective sub-problem. Finally, a new single-objective nonlinear programming problem is solved to find the final solution. To justify the proposed model, two illustrative examples are provided, and the results of three decision methods are compromised.

scholarly journals Applying ANP in Establishing a Decision-Making Model for the Destination Selection of Large-scale Exhibitions

2017 ◽  
Vol 7 (1) ◽  
pp. 100
Author(s):  
Wen-Tsung Wu ◽  
Chie-Bein Chen
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Analytic Network Process ◽  
Cost Ratio ◽  
Benefit Cost Ratio ◽  
Network Process ◽  
Second Tier ◽  
High Benefit ◽  
Decision Making Model ◽  
Selection Of

This study investigates the decision-making issues in the selection of destinations for large-scale exhibitions by the cultural and creative industry. We use the Rubber Duck China Tour by the Dutch artist Florentijn Hofman as an example and adopt the analytic network process technique to evaluate destination options for the exhibition, as well as to explore the impacts of the evaluation of destination feasibilities on exhibition investment. The results show that power, a high benefit-cost ratio, first-tier cities, integration with local communities, and a rich and interesting theme are the top five factors that curators should consider when planning exhibitions. Considering the priority among cities of various tiers, first-tier cities are the most favorable, followed by fourth-tier, third-tier, and second-tier cities. The decision-making model provides curators with a reliable reference for selecting destinations for future exhibitions.

Multi-Attribute Group Decision Making Method for Preference Conflicting with Heterogeneous Information

2018 ◽  
Vol 7 (4) ◽  
pp. 1-14 ◽  
Author(s):  
Kai-Rong Liang
Keyword(s):  
Decision Making ◽  
Objective Function ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Makers ◽  
Heterogeneous Information ◽  
Multi Objective ◽  
Attribute Weights ◽  
Relative Closeness ◽  
Decision Making Model

The aim of this article is to propose a multi-objective decision-making method for researching and solving multi-attribute heterogeneous group decision-making problems. This is in the case that the characters of the decision information and decision makers' preferences are heterogeneous, and the weight information is incomplete. In this method, the multi-objective decision-making model, which considers the alternatives decision relative closeness and the preference of heterogeneous degree of decision makers in the objective function, is put forward. In addition, this article uses the minimax method to derive the multi-objective decision-making model and obtain the attribute weights and decision makers weights, and then the optimal scheme is established. Finally, an illustrative example shows the effectiveness of the proposed method.

scholarly journals Decision-Making of Underwater Cooperative Confrontation Based on MODPSO

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2211
Author(s):  
Na Wei ◽  
Mingyong Liu ◽  
Weibin Cheng
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Communication Delay ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Objective ◽  
Decision Making Model ◽  
The Impact

This paper proposes a multi-objective decision-making model for underwater countermeasures based on a multi-objective decision theory and solves it using the multi-objective discrete particle swarm optimization (MODPSO) algorithm. Existing decision-making models are based on fully allocated assignment without considering the weapon consumption and communication delay, which does not conform to the actual naval combat process. The minimum opponent residual threat probability and minimum own-weapon consumption are selected as two functions of the multi-objective decision-making model in this paper. Considering the impact of the communication delay, the multi-objective discrete particle swarm optimization (MODPSO) algorithm is proposed to obtain the optimal solution of the distribution scheme with different weapon consumptions. The algorithm adopts the natural number coding method, and the particle corresponds to the confrontation strategy. The simulation result shows that underwater communication delay impacts the decision-making selection. It verifies the effectiveness of the proposed model and the proposed multi-objective discrete particle swarm optimization algorithm.

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