PaletteViz: A Visualization Method for Functional Understanding of High-Dimensional Pareto-Optimal Data-Sets to Aid Multi-Criteria Decision Making

2020 ◽  
Vol 15 (2) ◽  
pp. 36-48 ◽  
Author(s):  
AKM Khaled Ahsan Talukder ◽  
Kalyanmoy Deb
Keyword(s):  
Decision Making ◽  
High Dimensional ◽  
Pareto Optimal ◽  
Data Sets ◽  
Multi Criteria Decision Making ◽  
Visualization Method ◽  
Functional Understanding

An Improved Task Scheduling Mechanism Using Multi-Criteria Decision Making in Cloud Computing

2021 ◽  
pp. 767-795
Author(s):  
Suvendu Chandan Nayak ◽  
Chitaranjan Tripathy
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Synthetic Data ◽  
Task Completion ◽  
Data Sets ◽  
Multi Criteria Decision Making ◽  
Order Preference ◽  
First Come First Serve

In this work, the authors propose Multi-criteria Decision-making to schedule deadline based tasks in cloud computing. The existing backfilling task scheduling algorithm could not handle similar tasks for scheduling. In backfilling algorithm, tasks are backfilled to provide ideal resources to schedule other deadline sensitive tasks. However, the task to be backfilled is selected on first come, first serve (FCFS) basis from scheduling queue. The scheduling performances require to be improved when, there are similar tasks. In this proposed work, the authors propose to implement MCDM technique, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to improve the performance of the backfilling algorithm through scheduling deadline sensitive tasks in cloud computing. It resolves the conflicts among the similar tasks that is used as a decision support system. The work is simulated with synthetic data sets based on slack values of the tasks. The performance results affirm the task completion and reduction in task rejection compared to the existing backfilling algorithm.

An Improved Task Scheduling Mechanism Using Multi-Criteria Decision Making in Cloud Computing

2019 ◽  
Vol 14 (2) ◽  
pp. 92-117 ◽  
Author(s):  
Suvendu Chandan Nayak ◽  
Chitaranjan Tripathy
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Synthetic Data ◽  
Task Completion ◽  
Data Sets ◽  
Multi Criteria Decision Making ◽  
First Come First Serve ◽  
Topsis Technique

In this work, the authors propose Multi-criteria Decision-making to schedule deadline based tasks in cloud computing. The existing backfilling task scheduling algorithm could not handle similar tasks for scheduling. In backfilling algorithm, tasks are backfilled to provide ideal resources to schedule other deadline sensitive tasks. However, the task to be backfilled is selected on first come, first serve (FCFS) basis from scheduling queue. The scheduling performances require to be improved when, there are similar tasks. In this proposed work, the authors propose to implement MCDM technique, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to improve the performance of the backfilling algorithm through scheduling deadline sensitive tasks in cloud computing. It resolves the conflicts among the similar tasks that is used as a decision support system. The work is simulated with synthetic data sets based on slack values of the tasks. The performance results affirm the task completion and reduction in task rejection compared to the existing backfilling algorithm.

Comparison of Normalization Techniques on Data Sets with Outliers

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Decision Making ◽  
Input Data ◽  
Decision Problems ◽  
Data Sets ◽  
Multi Criteria Decision Making ◽  
Assessment Framework ◽  
Ranking Of Alternatives ◽  
Complex Decision ◽  
Vector Sum ◽  
Ongoing Evaluation

With the fast growing of data-rich systems, dealing with complex decision problems with skewed input data sets and respective outliers is unavoidable. Generally, data skewness refers to a non-uniform distribution in a dataset, i.e. a dataset which contains asymmetries and/or outliers. Normalization is the first step of most multi-criteria decision making (MCDM) problems to obtain dimensionless data, from heterogeneous input data sets, that enable aggregation of criteria and thereby ranking of alternatives. Therefore, when in presence of outliers in criteria datasets, finding a suitable normalization technique is of utmost importance. As such, in this work, we compare seven normalization techniques (Max, Max-Min, Vector, Sum, Logarithmic, Target-based, and Fuzzification) on criteria datasets, which contain outliers to analyse their results for MCDM problems. A numerical example illustrates the behaviour of the chosen normalization techniques and an (ongoing) evaluation assessment framework is used to recommend the best normalization technique for this type of criteria.

scholarly journals Pareto Optimal Decisions in Multi-Criteria Decision Making Explained with Construction Cost Cases

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 46
Author(s):  
Hubert Anysz ◽  
Aleksander Nicał ◽  
Željko Stević ◽  
Michał Grzegorzewski ◽  
Karol Sikora
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Pareto Optimal ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Optimal Decisions ◽  
Cost Criterion ◽  
The Cost ◽  
Financial Result ◽  
The Ideal

In multi-criteria decision-making (MCDM) problems the decision-maker is often forced to accept a not ideal solution. If the ideal choice exists, it would be certainly chosen. The acceptance of a non- ideal solution leads to some inadequate properties in the chosen solution. MCDM methods help the decision-maker to structure his needs considering different units, in which the properties of the solutions are expressed. Secondly, with MCDM tools the assessment of the available solutions can be calculated with consideration of the decision-maker’s needs. The incorporation of the cost criterion into the decision maker’s preferences calculation, and the solution assessment calculation, deprives the decision-maker of the ability to calculate the financial result of the decision he must make. A new multi-criteria decision making with cost criterion analysed at the final stage (MCDM-CCAF) method is developed based on principle of Pareto optimal decisions. It is proposed to exclude the cost criterion from the MCDM analysis and consider it at the final phase of the decision-making process. It is illustrated by example solutions with consideration of cost criterion and without it. It is proposed to apply the invented post-processing method to all MCDM analyses where the cost criterion of analysed variants is considered.

Perancangan aplikasi multi criteria decision making dalam penerimaan beasiswa kepada dosen studi lanjut STMIK Balikpapan menggunakan metode SAW

2018 ◽  
Vol 1 (1) ◽  
pp. 35-42
Author(s):  
Muslimin B ◽  
Sumardi Sumardi
Keyword(s):  
Decision Making ◽  
Learning Process ◽  
Teaching And Learning ◽  
Teaching Assistants ◽  
Multi Criteria Decision Making ◽  
Scholarship Recipients ◽  
The Matrix ◽  
Application Decision ◽  
High Degree ◽  
New Student

 Interests and number of STMIK Balikpapan new student enrollments are increasing every year. The balance of the ratio of lecturers to students is one of the most important components in improving the quality and teaching and learning process of a university. Avoiding shortages in the number of lecturers can be realized by providing scholarship programs to alumni and teaching assistants. This study aims to build a multi criteria decision making application that can assist the Head of HRD in the process of receiving scholarships to advanced and effective study lecturers. The multi criteria decision making application developed in this study uses the SAW method. The implementation of the SAW method includes the process of evaluating the weighting of criteria, evaluating alternative weights, the matrix process, the results of decision making preferences, resulting in the weighting and ranking of each alternative candidate for the scholarship recipient. The results of the evaluation of multi-criteria application decision making in the study are expected to produce modeling with a high degree of accuracy. The results of the analysis carried out can provide alternative recommendations for prospective scholarship recipients to advanced study lecturers in STMIK Balikpapan.

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