Fuzzy TOPSIS based selection index in the planning of emergency service facilities locations and goods transportation

2021 ◽  
pp. 1-14
Author(s):  
Gia Sirbiladze ◽  
Bidzina Matsaberidze ◽  
Bezhan Ghvaberidze ◽  
Bidzina Midodashvili ◽  
David Mikadze
Keyword(s):  
Emergency Service ◽  
Selection Index ◽  
Exact Algorithm ◽  
Fuzzy Topsis ◽  
Second Phase ◽  
Distribution Centers ◽  
Solution Approach ◽  
Order Preference ◽  
Two Phases ◽  
Topsis Technique

The attributes influencing the decision-making process in planning transportation of goods from selected facilities locations in disaster zones are considered. Experts evaluate each candidate for humanitarian aid distribution centers (HADCs) (service centers) against each uncertainty factor in q-rung orthopair fuzzy sets (q-ROFS). For representation of experts’ knowledge in the input data for planning emergency service facilities locations a q-rung orthopair fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach is developed. Based on the offered fuzzy TOPSIS aggregation a new innovative objective function is introduced which maximizes a candidate HADC’s selection index and reduces HADCs opening risks in disaster zones. The HADCs location and goods transportation problem is reduced to the bi-criteria problem of partitioning the set of customers by the set of service centers: 1) Minimization of opened HADCs and goods transportation total costs; 2) Maximization of HADCs selection index. Partitioning type transportation constraints are also constructed. Our approach for solving the constructed bi-criteria partitioning problem consists of two phases. In the first phase, based on the covering’s matrix, we generate a new matrix with columns allowing to find all possible partitioning of the demand points with the opened HADCs. In the second phase, using the generated matrix and our exact algorithm we find the partitioning –allocations of the HADCs to the centers corresponded to the Pareto-optimal solutions. The constructed model is illustrated with a numerical example.

scholarly journals Fuzzy TOPSIS Approach in Selection of Optimal Noise Barrier for Traffic Noise Abatement

2015 ◽  
Vol 40 (4) ◽  
pp. 453-467 ◽  
Author(s):  
Naveen Garg ◽  
Sagar Maji ◽  
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Fuzzy Topsis ◽  
Road Traffic Noise ◽  
Noise Abatement ◽  
Solution Approach ◽  
Order Preference ◽  
Noise Barrier ◽  
Topsis Technique ◽  
Selection Of

Abstract The paper presents a retrospective study for selection of noise barrier for road traffic noise abatement. The work proposes the application of Fuzzy TOPSIS (Technique for order preference by similarity to an ideal solution) approach is selection of optimal road traffic noise barrier. The present work utilizes the fuzzy TOPSIS model proposed by Mahdavi et al. (2008) in determination of ranking order of various types of noise barriers with respect to the various criteria considered. It is suggested that application of this approach can be very helpful in selection and application of optimal noise barrier for road traffic noise abatement.

scholarly journals Supplier selection using different metric functions

2015 ◽  
Vol 25 (3) ◽  
pp. 413-423 ◽  
Author(s):  
S.E. Omosigho ◽  
Dickson Omorogbe
Keyword(s):  
Supplier Selection ◽  
Selection Process ◽  
Fuzzy Topsis ◽  
Ideal Solution ◽  
Order Preference ◽  
Metric Functions ◽  
Metric Function ◽  
Topsis Technique ◽  
Intuitionistic Fuzzy Topsis ◽  
Selection Of

Supplier selection is an important component of supply chain management in today?s global competitive environment. Hence, the evaluation and selection of suppliers have received considerable attention in the literature. Many attributes of suppliers, other than cost, are considered in the evaluation and selection process. Therefore, the process of evaluation and selection of suppliers is a multi-criteria decision making process. The methodology adopted to solve the supplier selection problem is intuitionistic fuzzy TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution). Generally, TOPSIS is based on the concept of minimum distance from the positive ideal solution and maximum distance from the negative ideal solution. We examine the deficiencies of using only one metric function in TOPSIS and propose the use of spherical metric function in addition to the commonly used metric functions. For empirical supplier selection problems, more than one metric function should be used.

scholarly journals Fuzzy Topsis in the Assessment of OHS Management System

2019 ◽  
Vol 1 (1) ◽  
pp. 18-25
Author(s):  
Joanna Tabor
Keyword(s):  
Health And Safety ◽  
Fuzzy Topsis ◽  
Point Of View ◽  
Assessment Criteria ◽  
Management Systems ◽  
Manufacturing Companies ◽  
Topsis Method ◽  
Furniture Manufacturing ◽  
Order Preference ◽  
Topsis Technique

AbstractOccupational health and safety (OHS) management is a cycle of decision-making processes, many of which are in fact multi-criterion processes in nature. Therefore, it is important to look for and develop tools to support decision-makers in their actions aimed at improving work safety levels. The objective of this paper is to propose and verify the fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method applied to compare and assess the ways OHS management systems function in different companies. The fuzzy TOPSIS method has already been used for a number of years in assessments of alternative solutions in many different areas, but the application that uses ordered fuzzy numbers is quite original in nature. It is especially beneficial to use the fuzzy approach in OHS management systems, as it makes it possible for experts to assess different criteria using most frequently used linguistic variables. The adopted approach was verified in the study of OHS management systems in four furniture manufacturing companies. Assessment criteria were requirements of the PN-N 18001: 2004 Standard. Thanks to the ordered fuzzy TOPSIS method, the analysed OHS management systems were streamlined from the point of view of 24 assessment criteria, and the best and the worst functioning system was identified. The approach presented here may constitute a significant tool for improving OHS management systems.

Colleague recommender system in the Expert Cloud using features matrix

Kybernetes ◽  
2016 ◽  
Vol 45 (9) ◽  
pp. 1342-1357 ◽  
Author(s):  
Saeedeh Hazratzadeh ◽  
Nima Jafari Navimipour
Keyword(s):  
Recommender System ◽  
Design Methodology ◽  
Positive Impact ◽  
Second Phase ◽  
Content Type ◽  
Solution Approach ◽  
Cloud Systems ◽  
New Class ◽  
Final Score ◽  
Two Phases

Purpose Expert Cloud as a new class of cloud systems enables its users to request and share the skill, knowledge and expertise of people by employing internet infrastructures and cloud concepts. Since offering the most appropriate expertise to the customer is one of the clear objectives in Expert Cloud, colleague recommendation is a necessary part of it. So, the purpose of this paper is to develop a colleague recommender system for the Expert Cloud using features matrices of colleagues. Design/methodology/approach The new method is described in two phases. In the first phase, all possible colleagues of the user are found through the filtering mechanism and next features of the user and possible colleagues are calculated and collected in matrices. Six potential features of colleagues including reputation, expertise, trust, agility, cost and field of study were proposed. In the second phase, the final score is calculated for every possible colleague and then top-k colleagues are extracted among users. The survey was conducted using a simulation in MATLAB Software. Data were collected from Expert Cloud website. The method was tested using evaluating metrics such as precision, accuracy, incorrect recommendation and runtime. Findings The results of this study indicate that considering more features of colleagues has a positive impact on increasing the precision and accuracy of recommending new colleagues. Also, the proposed method has a better result in reducing incorrect recommendation. Originality/value In this paper, the colleague recommendation issue in the Expert Cloud is pointed out and the solution approach is applied into the Expert Cloud website.

Reverse Supply Chain Design

2010 ◽  
pp. 283-300 ◽  
Author(s):  
Kishore K. Pochampally ◽  
Surendra M. Gupta
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Phase I ◽  
Phase Ii ◽  
Fuzzy Topsis ◽  
Supply Chain Design ◽  
Reverse Supply Chain ◽  
Neural Network Approach ◽  
Solution Approach ◽  
Order Preference

The success of a reverse supply chain heavily relies on the efficiency of the collection facilities and recovery facilities chosen while designing that reverse supply chain. In this chapter, we propose a neural network approach to evaluate the efficiency of a facility (collection or recovery) of interest, which is being considered for inclusion in a reverse supply chain, using the available linguistic data of facilities that already exist in the reverse supply chain. The approach is carried out in four phases, as follows: In phase I, we identify criteria for evaluation of the facility of interest, for each group participating in the reverse supply chain. Then, in phase II, we use fuzzy ratings of already existing facilities to construct a neural network that gives impacts (importance values) of criteria identified for each group in phase I. Then, in phase III, using the impacts obtained in phase II, we employ a fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach to obtain the overall rating of the facility of interest, as calculated by each group. Finally, in phase IV, we employ Borda’s choice rule to calculate the maximized consensus (among the groups considered) rating of the facility of interest.

scholarly journals Multi-Criteria Decision Making Using TOPSIS Method Under Fuzzy Environment. Application in Spillway Selection

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 637 ◽  
Author(s):  
Vasiliki Balioti ◽  
Christos Tzimopoulos ◽  
Christos Evangelides
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Topsis Method ◽  
Northern Greece ◽  
Analytic Hierarchy ◽  
Order Preference ◽  
Optimum Type ◽  
Topsis Technique ◽  
Selection Of

The selection of an appropriate spillway has a significant effect to the construction of a dam and several procedures and considerations are needed. In the past, this selection of the type of the spillway was arbitrary and sometimes with bad results. Recently the Multiple Criteria Decision Making theory has given the possibility to make a decision about the optimum form of a spillway under complex circumstances. In this paper, the above method is used and especially the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for the selection of a spillway for a dam in the district of Kilkis in Northern Greece—‘Dam Pigi’. As the criteria were fuzzy and uncertain, the Fuzzy TOPSIS method is introduced together with the AHP (Analytic Hierarchy Process), which is used for the evaluation of criteria and weights. Five types of spillways were selected as alternatives and nine criteria. The criteria are expressed as triangular fuzzy numbers in order to formulate the problem. Finally, using the Fuzzy TOPSIS method, the alternatives were ranked and the optimum type of spillway was obtained.

scholarly journals MODEL PENILAIAN KONTRAKTOR PADA PROYEK KETENAGALISTRIKAN MENGGUNAKAN METODE AHP DAN FUZZY TOPSIS

BISMA ◽  
2018 ◽  
Vol 12 (3) ◽  
pp. 272
Author(s):  
Dodi Angga Kusuma ◽  
Bambang Syairuddin ◽  
Fuad Achmadi
Keyword(s):  
Customer Satisfaction ◽  
Fuzzy Topsis ◽  
Ideal Solution ◽  
Procurement Process ◽  
Solution Models ◽  
Production Experience ◽  
Order Preference ◽  
Topsis Technique ◽  
Price Quotes ◽  
Selection Of

Abstract: The objective of this study is to analyze the contractor assessmentmodel for the electricity project. This study focused on the procurement process ofgoods/services for the construction of a 500 kV Duri Kosambi Gas InsulatedSwitchgear Tegangan Ekstra Tinggi (GISTET). The sample consisted of 2 (two)contractors, i.e., PT CA and PT PCE. Data were analyzed using AHP (AnalyticalHierarchy Process) and Fuzzy TOPSIS (Technique for Order Preference bySimilarity to Ideal Solution) models. Referring to AHP and Fuzzy TOPSIS models,the selection of project contractor was based on 3 (three) main criteria:administration, technical, price, and 10 (ten) sub criteria: financial qualificationdocuments, documents from the bank, list of jobs in the implementation, TKDN,manufacturer's production experience, customer satisfaction certificates, technicalspecifications, brochure and TPG, price quotes, and fairness of offers. The resultsof the assessment showed that PT CA with the value of 0.723 was the bestcontractor for the project.Keywords: Procurement, Electricity Project, AHP, and Fuzzy TOPSIS.Abstrak: Tujuan dari penelitian ini adalah untuk menganalisis model penilaiankontraktor pada proyek ketenagalistrikan. Fokus penelitian adalah pada pemilihanpenyedia barang/jasa untuk pembangunan Gas Insulated Switchgear TeganganEkstra Tinggi (GISTET) 500 kV Duri Kosambi. Metode analisis yang digunakanadalah metode AHP (Analytical Hierarcy Process), dan Fuzzy TOPSIS. Pemilihankontraktor proyek dengan model AHP dan Fuzzy TOPSIS menggunakan 3 (tiga)kriteria utama, yaitu: administrasi, teknis, dan harga, serta 10 (sepuluh) subkriteria, yaitu: dokumen kualifikasi keuangan, dokumen dari bank, daftar pekerjaandalam pelaksanaan, dokumen kualifikasi finansial, TKDN, pengalaman produksipabrikan, surat keterangan kepuasan pelanggan, spesifikasi teknis, brochure danTPG, penawaran harga, dan kewajaran penawaran. Hasil penilaian yang dilakukanmenunjukan bahwa PT CA dengan nilai 0,723 merupakan kontraktor terbaik.Kata Kunci: Pengadaan Barang dan Jasa, Proyek Ketenagalistrikan, AHP, danFuzzy TOPSIS

scholarly journals Selection of Social Media Applications for Ubiquitous Learning using Fuzzy TOPSIS

2021 ◽  
Author(s):  
Caitlin Sam
Keyword(s):  
Social Media ◽  
Fuzzy Topsis ◽  
Information And Communications Technology ◽  
Ubiquitous Learning ◽  
Order Preference ◽  
Intelligent Decision Support Systems ◽  
Topsis Technique ◽  
Everyday Learning ◽  
Media Applications ◽  
Intelligent Decision Support

The exponential advancements in Information and Communications Technology has led to its prevalence in education, especially with the arrival of COVID-19. Ubiquitous learning (u-learning) is everyday learning that happens irrespective of time and place and it is enabled by m-learning, elearning, and social computing such as social media. Due to its popularity, there has been an expansion of social media applications for u-learning. The aim of this research paper was to establish the most relevant social media applications for ulearning in schools. Data was collected from 260 respondents, which comprised learners, and instructors in high schools who were asked to rank 14 of the top social media applications for ubiquitous learning. Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) was the method employed for the ranking of the 14 of the most popular social media applications using 15 education requirements, 15 technology criteria, and 260 decision makers. The simulation was implemented on MATLAB R2020a. The results showed that YouTube was the most likely social media application to be selected for u-learning with a closeness coefficient of 0.9188 and that Viber was the least likely selected social media application with a closeness coefficient of 0.0165. The inferences of this research study will advise researchers in the intelligent decision support systems field to reduce the time and effort made by instructors and learners to select the most beneficial social media application for u-learning

scholarly journals INTEGRATED APPROACH OF FUZZY MULTI-ATTRIBUTE DECISION MAKING AND DATA MINING FOR CUSTOMER SEGMENTATION

2021 ◽  
Vol 24 (4) ◽  
pp. 174-188
Author(s):  
Manidatta Ray ◽  
Mamata Ray ◽  
Kamalakanta Muduli ◽  
Audrius Banaitis ◽  
Anil Kumar
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Fuzzy Ahp ◽  
Integrated Approach ◽  
Fuzzy Topsis ◽  
Ideal Solution ◽  
Fuzzy C Means ◽  
Order Preference ◽  
Multi Attribute Decision Making ◽  
Topsis Technique

This research work focuses on integrating the multi attribute decision making with data mining in a fuzzy decision environment for customer relationship management. The main objective is to analyse the relation between multi attribute decision making and data mining considering a complex problem of ordering customers segments, which is based on four criteria of customer’s life time value, viz. length (L), recency (R), frequency (F) and monetary value (M). The proposed integrated approach involves fuzzy C-means (FCM) cluster analysis as data mining tool. The experiment conducted using MATLAB 12.0 for identifying eight clusters of customers. The two multi attribute decision making tools i.e., fuzzy AHP (Analytic Hierarchy Process) and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) are used for ranking these identified clusters. The applicability of the integrated decision making technique is also demonstrated in this paper considering the case of Indian retail sector. This research collected responses from nine experts from Indian retail industry regarding their perception of relative importance of four criteria of customer life value and evaluated weights of each criterion using fuzzy AHP. Transaction data of 18 months of the case retail store was analysed to segment 1,600 customers into eight clusters using fuzzy c-means clustering analysis technique. Finally, these eight clusters were ranked using fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The findings of this research could be helpful for firms in identifying the more valuable customers for them and allocate more resources to satisfy them. The findings will be also helpful in developing different loyalty program strategies for customers of different clusters.

Mobile Wallets: Theoretical and Empirical Analysis

2020 ◽  
pp. 097215092096125
Author(s):  
Ashwarya Kapoor ◽  
Rajiv Sindwani ◽  
Manisha Goel
Keyword(s):  
Service Quality ◽  
Quality Measurement ◽  
Fuzzy Topsis ◽  
Quality Factors ◽  
Customer Base ◽  
Order Preference ◽  
Quality Dimensions ◽  
Service Quality Dimensions ◽  
Topsis Approach ◽  
Topsis Technique

Mobile wallets (m-wallet) have played a prominent role in changing our system of payment from cash to digital payment. The current study employs a systematic review of articles published on m-wallets between January 2008 and January 2020. To identify the relevant studies, research databases are searched using different keywords. Research articles published on m-wallets are selected based on particular inclusion criteria and clustered on the basis of keywords, abstract and title. To ensure a reasonable accuracy in the selection and grouping of published literature, suggestions of experts are taken into consideration. The articles on m-wallets are categorized on the basis of their themes. Eight dominant themes are identified by the authors. The findings suggest that although m-wallets have been in existence for more than a decade, there is hardly any study related to m-wallet service quality measurement. Thus, the authors propose 10 dimensions to measure the service quality of m-wallets based on the extensive review of studies on electronic services (e-services) and m-wallets. The proposed dimensions of m-wallet service quality are prioritized using the fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach. The m-wallet service quality dimensions proposed in the article will help practitioners to develop strategies for building a customer base. Moreover, the results of the fuzzy TOPSIS technique employed in this study will apprise managers about critical quality factors that need sincere attention for gaining competitive advantage and retaining customers.

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