scholarly journals Seeking the Important Nodes of Complex Networks in Product R&D Team Based on Fuzzy AHP and TOPSIS

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Zhang ◽  
Qingpu Zhang ◽  
Hamidreza Karimi
Keyword(s):  
Complex Networks ◽  
Fuzzy Ahp ◽  
Evaluation Criteria ◽  
Multiple Attribute Decision Making ◽  
Eigenvector Centrality ◽  
Analytic Hierarchy ◽  
Node Importance ◽  
Multiple Attribute ◽  
Order Preference ◽  
Important Nodes

How to seek the important nodes of complex networks in product research and development (R&D) team is particularly important for companies engaged in creativity and innovation. The previous literature mainly uses several single indicators to assess the node importance; this paper proposes a multiple attribute decision making model to tentatively solve these problems. Firstly, choose eight indicators as the evaluation criteria, four from centralization of complex networks: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality and four from structural holes of complex networks: effective size, efficiency, constraint, and hierarchy. Then, use fuzzy analytic hierarchy process (AHP) to obtain the weights of these indicators and use technique for order preference by similarity to an ideal solution (TOPSIS) to assess the importance degree of each node of complex networks. Finally, taking a product R&D team of a game software company as a research example, test the effectiveness, operability, and efficiency of the method we established.

Multi-index algorithm of identifying important nodes in complex networks based on linear discriminant analysis

2015 ◽  
Vol 29 (03) ◽  
pp. 1450268 ◽  
Author(s):  
Fang Hu ◽  
Yuhua Liu
Keyword(s):  
Discriminant Analysis ◽  
Complex Networks ◽  
Linear Discriminant Analysis ◽  
Eigenvector Centrality ◽  
Complex Situation ◽  
Linear Discriminant ◽  
Multi Index ◽  
Node Importance ◽  
Evaluation Algorithm ◽  
Important Nodes

The evaluation of node importance has great significance to complex network, so it is important to seek and protect important nodes to ensure the security and stability of the entire network. At present, most evaluation algorithms of node importance adopt the single-index methods, which are incomplete and limited, and cannot fully reflect the complex situation of network. In this paper, after synthesizing multi-index factors of node importance, including eigenvector centrality, betweenness centrality, closeness centrality, degree centrality, mutual-information, etc., the authors are proposing a new multi-index evaluation algorithm of identifying important nodes in complex networks based on linear discriminant analysis (LDA). In order to verify the validity of this algorithm, a series of simulation experiments have been done. Through comprehensive analysis, the simulation results show that the new algorithm is more rational, effective, integral and accurate.

Evaluation of environmentally conscious manufacturing programs using multiple attribute decision-making methods

2008 ◽  
Vol 222 (3) ◽  
pp. 441-451 ◽  
Author(s):  
R V Rao
Keyword(s):  
Decision Making ◽  
Raw Materials ◽  
Selection Index ◽  
Multiple Attribute Decision Making ◽  
Analytic Hierarchy ◽  
Environmentally Conscious ◽  
Environmentally Conscious Manufacturing ◽  
Multiple Attribute ◽  
Environmentally Responsible ◽  
Order Preference

Manufacturers are increasingly under pressure from their major stakeholders to integrate environmental issues in the design and management of their products. These stakeholders include customers, regulators, employees, communities, and interest groups who have a common stake in protecting the earth from pollution and in limiting the exploitation of earth's limited natural resources. Manufacturers recognize that being environmentally responsible also offers competitive advantage to the firm. An environmentally responsible or environmentally conscious manufacturing (ECM) program addresses the environmental impact of the interrelated decisions that are made at various stages of product life, from conception to design, raw materials consumption, processing, delivery, use, recycling, and/or disposal. The evaluation of alternative ECM programs for producing a given product is similar to many strategic initiatives and their justification methodologies. This similarity arises from the fact that there are multiple attributes that need to be considered, many of which have long-term and broad implications for an organization. This paper presents three multiple attribute decision-making methods for evaluation of environmentally conscious manufacturing (ECM) programs for producing a given product. These methods are based on the analytic hierarchy process (AHP), the technique for order preference by similarity to an ideal solution (TOPSIS), and the modified TOPSIS method. The proposed ‘ECM program selection index’ helps to evaluate and rank the ECM programs for producing a given product. An example is included to illustrate and compare the methods. It is proposed that the most sensible approach to select a particular alternative ECM program from among the given alternatives is to apply several valid decision-making methods to the same selection problem and then to make the final selection on the basis of aggregation of the results of those decision-making methods that have a very significant positive relationship.

Holistic simulation-based optimisation methodology for facility layout design with consideration to production and logistics constraints

2021 ◽  
pp. 095440542110173
Author(s):  
Enrique Ruiz Zúñiga ◽  
Erik Flores García ◽  
Matías Urenda Moris ◽  
Masood Fathi ◽  
Anna Syberfeldt
Keyword(s):  
Material Handling ◽  
Facility Layout ◽  
Holistic Approach ◽  
Multiple Attribute Decision Making ◽  
Layout Design ◽  
Analytic Hierarchy ◽  
Facility Layout Design ◽  
Multiple Attribute ◽  
Simulation Based ◽  
Optimisation Methods

Facility layout design is becoming more challenging as manufacturing moves from traditionally emphasised mass production to mass customisation. The increasing demand for customised products and services is driving the need to increase flexibility and adaptability of both production processes and their material handling systems. A holistic approach for designing facility layouts with optimised flows considering production and logistics systems constraints seems to be missing in the literature. Several tools, including traditional methods, analytic hierarchy process, multiple-attribute decision making, simulation, and optimisation methods, can support such a process. Among these, simulation-based optimisation is the most promising. This paper aims to develop a facility layout design methodology supported by simulation-based optimisation while considering both production and logistics constraints. A literature review of facility layout design with simulation and optimisation and the theoretical and empirical challenges are presented. The integration of simulation-based optimisation in the proposed methodology serves to overcome the identified challenges, providing managers and stakeholders with a decision support system that handles the complex task of facility layout design.

scholarly journals Identifying Node Importance in a Complex Network Based on Node Bridging Feature

2018 ◽  
Vol 8 (10) ◽  
pp. 1914 ◽  
Author(s):  
Lincheng Jiang ◽  
Yumei Jing ◽  
Shengze Hu ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Shortest Paths ◽  
Evaluation Criteria ◽  
Local Network ◽  
Network Efficiency ◽  
Network Attack ◽  
Node Importance ◽  
Pass Through ◽  
Large Scale Networks

Identifying node importance in complex networks is of great significance to improve the network damage resistance and robustness. In the era of big data, the size of the network is huge and the network structure tends to change dynamically over time. Due to the high complexity, the algorithm based on the global information of the network is not suitable for the analysis of large-scale networks. Taking into account the bridging feature of nodes in the local network, this paper proposes a simple and efficient ranking algorithm to identify node importance in complex networks. In the algorithm, if there are more numbers of node pairs whose shortest paths pass through the target node and there are less numbers of shortest paths in its neighborhood, the bridging function of the node between its neighborhood nodes is more obvious, and its ranking score is also higher. The algorithm takes only local information of the target nodes, thereby greatly improving the efficiency of the algorithm. Experiments performed on real and synthetic networks show that the proposed algorithm is more effective than benchmark algorithms on the evaluation criteria of the maximum connectivity coefficient and the decline rate of network efficiency, no matter in the static or dynamic attack manner. Especially in the initial stage of attack, the advantage is more obvious, which makes the proposed algorithm applicable in the background of limited network attack cost.

scholarly journals Sistem Pendukung Keputusan Pemberian Reward Pegawai Menggunakan Metode TOPSIS

2020 ◽  
Vol 18 (1) ◽  
pp. 11
Author(s):  
Aisyah Mutia Dawis
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Selection Process ◽  
Multiple Attribute Decision Making ◽  
Management Problem ◽  
Multiple Attribute ◽  
Ideal Solutions ◽  
Order Preference ◽  
Topsis Technique ◽  
A Company

Every company has management providing wages or rewards to employees. This is because employees are one of the resources that are used as a driving force in advancing a company. Besides, many companies provide rewards to their employees with the aim of motivating employees to help more. There is management problem in PKU Muhammadiyah Gamping Hospital for determining the number of rewards obtained by employees because many variables are determined. Therefore, the need of management information system can facilitate the Management of the PKU Muhammadiyah Gamping Hospital in determining decision making for providing rewards. One method that is often used in implementing decision support systems is Multiple Attribute Decision Making (MADM), focusing TOPSIS (Technique for Order Preference with Similarities to Ideal Solutions). By the implementation of the decision support system, PKU Muhammadiyah Gamping Hospital can carry out the selection process more efficiently.The test results by matching the employee data results at PKU Muhammadiyah Hospital obtained 95.83% accuracy so that this system can help the PKU Muhammadiyah Hospital in determining employee rewards.

Evaluating hotel websites through the use of fuzzy AHP and fuzzy TOPSIS

2020 ◽  
Vol 13 (12) ◽  
pp. 3747-3765
Author(s):  
Rahmi Baki
Keyword(s):  
Information Quality ◽  
Fuzzy Ahp ◽  
Fuzzy Topsis ◽  
Step Method ◽  
Analytic Hierarchy ◽  
Website Evaluation ◽  
Content Type ◽  
Ideal Situation ◽  
Order Preference ◽  
Hotel Websites

Purpose The purpose of this study is to develop a useful, effective and comprehensive approach to facilitate the evaluation of hotel websites. Design/methodology/approach The paper examines the literature evaluating e-commerce sites, particularly that is focused on hotel, tourism and travel. Moreover, 5 criteria and 19 sub-criteria are identified, and a two-step method is proposed for the assessment of hotel websites whereby the global weights of the proposed criteria are determined by the fuzzy analytic hierarchy process, and hotel websites are ranked through the fuzzy technique for order preference by similarity to ideal situation. Findings The results show that the leading criteria to effectively evaluate hotel websites are trust and information quality and that the most important sub-criteria are special discounts, assurance and reservation information. Practical implications This research offers practical advice to increase understanding of the determinants of an effective hotel website so that appropriate strategies can be developed to convert a website visitor into a customer. Originality/value The study aims to contribute to businesses operating in the tourism sector which seeks to increase the effectiveness of their websites by identifying criteria and proposing a methodology for hotel website evaluation.

scholarly journals Assessing the Human Resource in Science and Technology for Asian Countries: Application of Fuzzy AHP and Fuzzy TOPSIS

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 251 ◽  
Author(s):  
Ying-Chyi Chou ◽  
Hsin-Yi Yen ◽  
Van Thac Dang ◽  
Chia-Chi Sun
Keyword(s):  
Human Resource ◽  
Fuzzy Ahp ◽  
Science And Technology ◽  
Fuzzy Topsis ◽  
Southeast Asian ◽  
Asian Countries ◽  
Analytic Hierarchy ◽  
Order Preference ◽  
Hierarchy Process ◽  
Southeast Asian Countries

The fuzzy analytic hierarchy process (AHP) and fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are extremely beneficial when a decision-making process is complex. The reason is that AHP and TOPSIS can prioritize multiple-choice criteria into a hierarchy by assessing the relative importance of criteria and can thus generate an overall ranking of the alternatives. This study uses fuzzy AHP and fuzzy TOPSIS to evaluate the human resource in science and technology (HRST) performance of Southeast Asian countries. The fuzzy TOPSIS analysis indicates that Singapore, South Korea, and Taiwan have similarities in their desired levels of HRST performance. That is, these three countries have better HRST performances than other Southeast Asian countries.

Combining AHP and TOPSIS Methods to Evaluate Investment for Wind Farm Construction

2013 ◽  
Vol 860-863 ◽  
pp. 280-286 ◽  
Author(s):  
Xiang Feng Zhang
Keyword(s):  
Alternative Energy ◽  
Wind Farm ◽  
Evaluation Method ◽  
Evaluation Criteria ◽  
Wind Farms ◽  
Investment Strategy ◽  
Analytic Hierarchy ◽  
Multi Criteria Evaluation ◽  
Order Preference ◽  
Rational Investment

Wind is one of the most promising sources of alternative energy. The construction of wind farms grows quickly in China. It is necessary for stakeholders to estimate investment costs and make good decisions on a wind power project by making a budget for the investment. However, the identification of rational investment practices is technically challenging because of the lack of scientific tools to evaluate optimal decisions. A multi-criteria evaluation method was proposed to select rational investment strategy for wind farm construction. The method is based on the analytic hierarchy process (AHP) together with a technique for order preference by similarity to ideal solution (TOPSIS). A decision problem hierarchy with three layers were investigated. The top layer is an objective layer for evaluating the investment rationality. The intermediate layer includes three evaluation criteria, that is, configuration of wind turbine generator systems, physical environment and social environment. Some relative and important indicators for each criterion are in the low layer. The evaluation results illustrate that the proposed method is practical and helpful to indentify the investment rationality for wind farms.

scholarly journals SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN PERGURUAN TINGGI SWASTA MENGGUNAKAN TOPSIS

2019 ◽  
Vol 1 (2) ◽  
pp. 25-34
Author(s):  
Tati Mardiana ◽  
Siska Selvia Tanjung
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Private Colleges ◽  
Multiple Attribute Decision Making ◽  
Private College ◽  
Functional Requirements ◽  
Multiple Attribute ◽  
Order Preference ◽  
The Right

Choosing the right college is a crucial step for students in preparing for their careers and future. With education at the college, the student increases their chances of getting better jobs. But because of the limited capacity of public college, students and parents must select a private college that agrees with desires and abilities. Errors in choosing a college result in students experiencing failure in carrying out education in the college. Therefore, students and parents need to consider several factors such as accreditation status, costs, the number of students, lecturers, facilities, study programs, and others to select a private college. Nevertheless, students and parents experience confusion in choosing a private college. This is due to many private colleges and the lack of information about these private colleges. Therefore, the aim of this study is to build a decision support system to select a private college that matches the desires and abilities of students and parents. This study uses Fuzzy Multiple Attribute Decision Making (FMADM) logic with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to rank each alternative private college. The test results showed that the system performance meets functional requirements and the perform system achieved an accuracy of 83,33%. This decision support system helps students and parents make decisions to select a private college that according to their desires and abilities accurately.  

Multi-attribute ranking method for identifying key nodes in complex networks based on GRA

2018 ◽  
Vol 32 (32) ◽  
pp. 1850363 ◽  
Author(s):  
Pingle Yang ◽  
Guiqiong Xu ◽  
Huiping Chen
Keyword(s):  
Complex Networks ◽  
Centrality Measures ◽  
Topsis Method ◽  
Node Importance ◽  
Significant Research ◽  
Order Preference ◽  
Limited Application ◽  
Multi Attribute Decision Making ◽  
Grey Relational ◽  
Key Nodes

How to identify key nodes is a challenging and significant research issue in complex networks. Some existing evaluation indicators of node importance have the disadvantages of limited application scope and one-sided evaluation results. This paper takes advantage of multiple centrality measures comprehensively, by regarding the identification of key nodes as a multi-attribute decision making (MADM) problem. Firstly, a new local centrality (NLC) measure is put forward through considering multi-layer neighbor nodes and clustering coefficients. Secondly, combining the grey relational analysis (GRA) method and the susceptible-infectious-recovered (SIR) model, a modified dynamic weighted technique for order preference by similarity to ideal solution (TOPSIS) method is proposed. Finally, the effectiveness of the NLC is illustrated by applications to nine actual networks. Furthermore, the experimental results on four actual networks demonstrate that the proposed method can identify key nodes more accurately than the existing weighted TOPSIS method.

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