• Title/Summary/Keyword: Decision making algorithm

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A Generic Multi-Level Algorithm for Prioritized Multi-Criteria Decision Making

  • G., AlShorbagy;Eslam, Hamouda;A.S., Abohamama
    • International Journal of Computer Science & Network Security
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    • v.23 no.1
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    • pp.25-32
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    • 2023
  • Decision-making refers to identifying the best alternative among a set of alternatives. When a set of criteria are involved, the decision-making is called multi-criteria decision-making (MCDM). In some cases, the involved criteria may be prioritized by the human decision-maker, which determines the importance degree for each criterion; hence, the decision-making becomes prioritized multi-criteria decision-making. The essence of prioritized MCDM is raking the different alternatives concerning the criteria and selecting best one(s) from the ranked list. This paper introduces a generic multi-level algorithm for ranking multiple alternatives in prioritized MCDM problems. The proposed algorithm is implemented by a decision support system for selecting the most critical short-road requests presented to the transportation ministry in the Kingdom of Saudi Arabia. The ranking results show that the proposed ranking algorithm achieves a good balance between the importance degrees determined by the human decision maker and the score value of the alternatives concerning the different criteria.

Distributed Relay Selection Algorithm for Cooperative Communication

  • Oo, Thant Zin;Hong, Choong-Seon
    • Proceedings of the Korean Information Science Society Conference
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    • 2011.06d
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    • pp.213-214
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    • 2011
  • This paper presents a distributed relay selection algorithm for cooperative communication. The algorithm separates the decision making into two simple steps, decision making for employing cooperative communication and decision making for relay selection.

ε-AMDA Algorithm and Its Application to Decision Making (ε-AMDA 알고리즘과 의사 결정에의 응용)

  • Choi, Dae-Young
    • The KIPS Transactions:PartB
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    • v.16B no.4
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    • pp.327-331
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    • 2009
  • In fuzzy logic, aggregating uncertainties is generally achieved by means of operators such as t-norms and t-conorms. However, existing aggregation operators have some disadvantages as follows : First, they are situation-independent. Thus, they may not be properly applied to dynamic aggregation process. Second, they do not give an intuitional sense to decision making process. To solve these problems, we propose a new $\varepsilon$-AMDA (Aggregation based on the fuzzy Multidimensional Decision Analysis) algorithm to reflect degrees of strength for option i (i = 1, 2, ..., n) in the decision making process. The $\varepsilon$-AMDA algorithm makes adaptive aggregation results between min (the most weakness for an option) and max (the most strength for an option) according to the values of the parameter representing degrees of strength for an option. In this respect, it may be applied to dynamic aggregation process. In addition, it provides a mechanism of the fuzzy multidimensional decision analysis for decision making, and gives an intuitional sense to decision making process. Thus, the proposed method aids the decision maker to get a suitable decision according to the degrees of strength for options (or alternatives).

On Evaluation Algorithm for Hierarchical Structure of Attributes with Interaction Relationship (상호연관성을 지닌 계층구조형문제의 평가 알고리즘)

  • Lee C.Y.;Lee S.T.
    • Journal of Korean Port Research
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    • v.7 no.1
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    • pp.5-12
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    • 1993
  • In complex decision making such as ill-defined system, one of the main problem is how to treat ambiguous aspect of the decision making. According to the complexity and ambiguity of the objective systems, many types of evaluation attributes are necessary for the rational decision and the relationship among the attributes become complex and fuzzy. Fuzzy integral is very effective to evalute the complex system with interaction between attributes but how to save the evaluation efforts in the decision making process of grading the membership of the objects or alternative is the problem to be tackled. Because the more object there are to evaluate, the number of decisions to made increase exponentially. Therefore, this paper aimes to propose a new evaluation algorithm based on fuzzy integral which can save the evaluator's efforts in decision making process. The proposed algorithm is constructed as follows : First, compose the fuzzy measure by introducing AHP(Analytical Hierachy Process) & mutual interaction coefficient. Second, generate fuzzy measure value of monotone family set for calculating the fuzzy integral. The effectiveness of the proposed algorithm is investigated through the example and sensitivity of interaction coefficient is illustrated.

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Distributed Decision-Making in Wireless Sensor Networks for Online Structural Health Monitoring

  • Ling, Qing;Tian, Zhi;Li, Yue
    • Journal of Communications and Networks
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    • v.11 no.4
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    • pp.350-358
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    • 2009
  • In a wireless sensor network (WSN) setting, this paper presents a distributed decision-making framework and illustrates its application in an online structural health monitoring (SHM) system. The objective is to recover a damage severity vector, which identifies, localizes, and quantifies damages in a structure, via distributive and collaborative decision-making among wireless sensors. Observing the fact that damages are generally scarce in a structure, this paper develops a nonlinear 0-norm minimization formulation to recover the sparse damage severity vector, then relaxes it to a linear and distributively tractable one. An optimal algorithm based on the alternating direction method of multipliers (ADMM) and a heuristic distributed linear programming (DLP) algorithm are proposed to estimate the damage severity vector distributively. By limiting sensors to exchange information among neighboring sensors, the distributed decision-making algorithms reduce communication costs, thus alleviate the channel interference and prolong the network lifetime. Simulation results in monitoring a steel frame structure prove the effectiveness of the proposed algorithms.

Reinforcement Learning-Based Intelligent Decision-Making for Communication Parameters

  • Xie, Xia.;Dou, Zheng;Zhang, Yabin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.9
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    • pp.2942-2960
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    • 2022
  • The core of cognitive radio is the problem concerning intelligent decision-making for communication parameters, the objective of which is to find the most appropriate parameter configuration to optimize transmission performance. The current algorithms have the disadvantages of high dependence on prior knowledge, large amount of calculation, and high complexity. We propose a new decision-making model by making full use of the interactivity of reinforcement learning (RL) and applying the Q-learning algorithm. By simplifying the decision-making process, we avoid large-scale RL, reduce complexity and improve timeliness. The proposed model is able to find the optimal waveform parameter configuration for the communication system in complex channels without prior knowledge. Moreover, this model is more flexible than previous decision-making models. The simulation results demonstrate the effectiveness of our model. The model not only exhibits better decision-making performance in the AWGN channels than the traditional method, but also make reasonable decisions in the fading channels.

Compensatory Decision-Making for Multiobjective Nonlinear Programming Problem in a Fuzzy Environment (퍼지환경에서 다목적 비선형계획문제의 절충 의사결정)

  • Lee, Sang-Wan;Nam, Hyun-Woo
    • Journal of Korean Institute of Industrial Engineers
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    • v.23 no.1
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    • pp.163-175
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    • 1997
  • This paper presents the algorithm for finding the compensatory solution for fuzzy multiobjective nonlinear programming problem using $\gamma$-operator. The proposed algorithm can be applied to all cases with multiobjective problems since the interactive process with a decision maker is simple, various uncertainties involved on decision making are eliminated and all the objectives are well balanced. On the basis of proposed algorithm, an illustrative numerical example is presented.

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Multi-Valued Decision Making for Transitional Stochastic Event: Determination of Sleep Stages Through EEG Record

  • Nakamura, Masatoshi;Sugi, Takenao
    • Transactions on Control, Automation and Systems Engineering
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    • v.4 no.3
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    • pp.239-243
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    • 2002
  • Multi-valued decision making for transitional stochastic events was newly derived based on conditional probability of knowledge database which included experts'knowledge and experience. The proposed multi-valued decision making was successfully adopted to the determination of the five levels of the vigilance of a subject during the EEG (electroencephalogram) recording; awake stage (stage W), and sleep stages (stage REM (rapid eye movement), stage 1, stage 2, stage $\sfrac{3}{4}$). Innovative feature of the proposed method is that the algorithm of decision making can be constructed only by use of the knowledge database, inspected by experts. The proposed multi-valued decision making with a mathematical background of the probability can also be applicable widely, in industries and in other medical fields for purposes of the multi-valued decision making.

A Multi-Objective Decision Making Procedure for Web-based GDSS (웹기반 그룹의사결정지원시스템을 위한 다목적 의사결정 알고리즘 개발)

  • Kim, Jae-Kyeong;Cho, Yoon-Ho
    • Journal of the Korean Operations Research and Management Science Society
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    • v.27 no.2
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    • pp.15-31
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    • 2002
  • This research suggests an interactive methodology for multiple objective linear programming problems to help the group select a compromising solution in the World Wide Web environment. Our methodology lessens the burden of group decision makers, which is one of necessary conditions of the web environment. Only the partial weak order of variables and objectives from the group decision makers are enough for searching the best compromising solution. For such a purpose, we expand the Dror and Gass algorithm to the group decision context. And we suggest the system architecture of a web-based GDSS for the Implementation of our methodology.

Multi-Valued Decision Making for Transitional Stochastic Event: Determination of Sleep Stages through EEG Record

  • Nakamura, Masatoshi;Sugi, Takenaop;Morota, Yukinao;Tachibana, Naoko;Shibasaki, Hiroshi
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.493-493
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    • 2000
  • Multi-valued decision making for transitional stochastic events was newly derived based on conditional probability of database. The two values (on-off) decision making method without transition had been proposed by one of the author in a previous work for a purpose of realizing human on-off decision making. The current method is an extension of the previous on-off decision making. By combining the conditional probability and the transitional probability, the closed form of the algorithm for the multi-valued transitional decision making was derived. The proposed multi-valued decision making was successfully applied to the determination of the five levels of the vigilance of a subject during the EEG recording; awake stage, drowsy stage and sleeping stages (stage 1, stage 2/3, REM (rapid eye movement)). The method for determining the vigilance level can be directly usable for the two purposes; selection of awake EEG segments for automatic EEG interpretation, and determination of sleep stages through sleep EEG. The proposed multi-valued decision making with a mathematical background of the probability can be applicable widely, in industries and in medical fields for purposes of the multi-valued decision making.

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