Pub Date : 2023-04-15DOI: 10.31181/dmame0329102022a
Gogineni Anusha, P. V. Ramana, Rupak Sarkar
The q-rung probabilistic dual hesitant fuzzy sets (qRPDHFSs), which outperform dual hesitant fuzzy sets, probabilistic dual hesitant fuzzy sets, and probabilistic dual hesitant Pythagorean fuzzy sets, are used in this research to develop an interactive group decision-making approach. We first suggest the Archimedean Copula-based operations on q-rung probabilistic dual hesitant fuzzy (qRPDHF) components and investigate their key features before constructing the approach. We then create some new aggregation operators (AOs) in light of these operations, including the qRPDHF generalized Maclaurin symmetric mean (MSM) operator, qRPDHF geometric generalized MSM operator, qRPDHF weighted generalized MSM operator, and qRPDHF weighted generalized geometric generalized MSM operator. These aggregation operators are better than current operators on qRPDHF because they can take into account the interactions between a large number of criteria and probability distributions. The evaluation findings are distorted since the present methodologies do not take expert involvement into account in order to achieve the required consistency level. We employ the idea of interaction, consistency, resemblance, and consensus-building among the decision-makers in our method to get around this. We create an optimization model based on the cross-entropy of the qRPDHF components to estimate the weights of the criterion. We provide contextual research on the choice of open-source software LMS in order to demonstrate the relevance of the recommended AOs. Likewise, we ran a sensitivity test on the weights of the criterion to make sure that our model is consistent. The comparison investigation has demonstrated that the suggested approach can overcome the challenges of previous works.
{"title":"Hybridizations of Archimedean copula and generalized MSM operators and their applications in interactive decision-making with q-rung probabilistic dual hesitant fuzzy environment","authors":"Gogineni Anusha, P. V. Ramana, Rupak Sarkar","doi":"10.31181/dmame0329102022a","DOIUrl":"https://doi.org/10.31181/dmame0329102022a","url":null,"abstract":"The q-rung probabilistic dual hesitant fuzzy sets (qRPDHFSs), which outperform dual hesitant fuzzy sets, probabilistic dual hesitant fuzzy sets, and probabilistic dual hesitant Pythagorean fuzzy sets, are used in this research to develop an interactive group decision-making approach. We first suggest the Archimedean Copula-based operations on q-rung probabilistic dual hesitant fuzzy (qRPDHF) components and investigate their key features before constructing the approach. We then create some new aggregation operators (AOs) in light of these operations, including the qRPDHF generalized Maclaurin symmetric mean (MSM) operator, qRPDHF geometric generalized MSM operator, qRPDHF weighted generalized MSM operator, and qRPDHF weighted generalized geometric generalized MSM operator. These aggregation operators are better than current operators on qRPDHF because they can take into account the interactions between a large number of criteria and probability distributions. The evaluation findings are distorted since the present methodologies do not take expert involvement into account in order to achieve the required consistency level. We employ the idea of interaction, consistency, resemblance, and consensus-building among the decision-makers in our method to get around this. We create an optimization model based on the cross-entropy of the qRPDHF components to estimate the weights of the criterion. We provide contextual research on the choice of open-source software LMS in order to demonstrate the relevance of the recommended AOs. Likewise, we ran a sensitivity test on the weights of the criterion to make sure that our model is consistent. The comparison investigation has demonstrated that the suggested approach can overcome the challenges of previous works.","PeriodicalId":32695,"journal":{"name":"Decision Making Applications in Management and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47684672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-15DOI: 10.31181/dmame060120032023b
Bo Dong
Based on Amazon's successful business experience, the flywheel effect has proven to be an effective method for guiding companies across the S-curve. More scholars have investigated the flywheel model of business growth, which uses the flywheel effect to help companies achieve leapfrogging growth. More research is needed to determine whether the structured business growth model is universally applicable to different industries and stages of enterprise development. According to this study, in the VUCA era, businesses are forced to accelerate their transformation due to rapid changes in the competitive environment. A more agile approach to growth model optimization is required there. As a result, this study takes a traditional theory approach, and this research builds a flywheel model of enterprise growth on the original flywheel effect theory. The three-step method of producing the corporate growth flywheel model proposed in this study is validated by the empirical results of the case study, and the universality and operability of the structured business growth flywheel model are verified by the case study of the leading real estate intermediary company, Lianjia.
{"title":"Logic behind the continuous growth across the s-curve - A method for the structured construction of a business growth flywheel model","authors":"Bo Dong","doi":"10.31181/dmame060120032023b","DOIUrl":"https://doi.org/10.31181/dmame060120032023b","url":null,"abstract":"Based on Amazon's successful business experience, the flywheel effect has proven to be an effective method for guiding companies across the S-curve. More scholars have investigated the flywheel model of business growth, which uses the flywheel effect to help companies achieve leapfrogging growth. More research is needed to determine whether the structured business growth model is universally applicable to different industries and stages of enterprise development. According to this study, in the VUCA era, businesses are forced to accelerate their transformation due to rapid changes in the competitive environment. A more agile approach to growth model optimization is required there. As a result, this study takes a traditional theory approach, and this research builds a flywheel model of enterprise growth on the original flywheel effect theory. The three-step method of producing the corporate growth flywheel model proposed in this study is validated by the empirical results of the case study, and the universality and operability of the structured business growth flywheel model are verified by the case study of the leading real estate intermediary company, Lianjia.","PeriodicalId":32695,"journal":{"name":"Decision Making Applications in Management and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42733552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Broumi, S. Mohanaselvi, T. Witczak, M. Talea, A. Bakali, F. Smarandache
A new growing area of neutrosophic set (NS) theory called complex neutrosophic sets (CNS) provides useful tools for dealing with uncertainty in complex valued physical variables that are observed in the actual world. A CNS take values for the truth, indeterminacy and falsity membership functions in the complex plane's unit circle. In this research, a novel concept of complex fermatean neutrosophic graph (CFNG) is established. We proposed the order, size, degree and total degree of a vertex of CFNG. Also, we presented the primary operations such as complement, union, join, ring-sum and cartesian product of CFNG. Moreover, the concept of regular graph under complex fermatean neutrosophic environment is discussed. Finally, an application of multi criteria decision making problem in educational system to evaluate lecturer’s research productivity using CFNG is discussed.
{"title":"Complex fermatean neutrosophic graph and application to decision making","authors":"S. Broumi, S. Mohanaselvi, T. Witczak, M. Talea, A. Bakali, F. Smarandache","doi":"10.31181/dmame24022023b","DOIUrl":"https://doi.org/10.31181/dmame24022023b","url":null,"abstract":"A new growing area of neutrosophic set (NS) theory called complex neutrosophic sets (CNS) provides useful tools for dealing with uncertainty in complex valued physical variables that are observed in the actual world. A CNS take values for the truth, indeterminacy and falsity membership functions in the complex plane's unit circle. In this research, a novel concept of complex fermatean neutrosophic graph (CFNG) is established. We proposed the order, size, degree and total degree of a vertex of CFNG. Also, we presented the primary operations such as complement, union, join, ring-sum and cartesian product of CFNG. Moreover, the concept of regular graph under complex fermatean neutrosophic environment is discussed. Finally, an application of multi criteria decision making problem in educational system to evaluate lecturer’s research productivity using CFNG is discussed.","PeriodicalId":32695,"journal":{"name":"Decision Making Applications in Management and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69623495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nadia Batool, Sadaqat Hussain, N. Kausar, Mohammed Munir, R. Li, Salma Khan
Real-world data is often partial, uncertain, or incomplete. Decision-making based on data as such can be addressed by fuzzy sets and related systems. This article studies the intuitionistic multi-fuzzy sub-near rings and Intuitionistic multi-fuzzy ideals of near rings. It presents some of the elementary operations and relations defined on these structures. The concept of level subsets and support of the Intuitionistic multi-fuzzy sub-near ring is also presented. It looks into and demonstrates a few characteristics of intuitionistic multi-fuzzy near-rings and ideals. This research advances fuzzy set theory, which is often applied to problems involving pattern recognition and multiple criterion decision-making. Thus, the results may be beneficial to artificial intelligence related research. Alternatively, the intuitionistic multi-fuzzy approach may be applied to vector spaces and modules or extended to inter-valued fuzzy systems.
{"title":"Intuitionistic multi fuzzy ideals of near-rings","authors":"Nadia Batool, Sadaqat Hussain, N. Kausar, Mohammed Munir, R. Li, Salma Khan","doi":"10.31181/dmame04012023b","DOIUrl":"https://doi.org/10.31181/dmame04012023b","url":null,"abstract":"Real-world data is often partial, uncertain, or incomplete. Decision-making based on data as such can be addressed by fuzzy sets and related systems. This article studies the intuitionistic multi-fuzzy sub-near rings and Intuitionistic multi-fuzzy ideals of near rings. It presents some of the elementary operations and relations defined on these structures. The concept of level subsets and support of the Intuitionistic multi-fuzzy sub-near ring is also presented. It looks into and demonstrates a few characteristics of intuitionistic multi-fuzzy near-rings and ideals. This research advances fuzzy set theory, which is often applied to problems involving pattern recognition and multiple criterion decision-making. Thus, the results may be beneficial to artificial intelligence related research. Alternatively, the intuitionistic multi-fuzzy approach may be applied to vector spaces and modules or extended to inter-valued fuzzy systems.","PeriodicalId":32695,"journal":{"name":"Decision Making Applications in Management and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46355202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-15DOI: 10.31181/dmame0306102022r
Tithli Sadhu, Somanth Chowdhury, Shubham Mondal, Jagannath Roy, J. Chakrabarty, S. Lahiri
Metaheuristic approaches with extremely important improvements are very promising in the solution of intractable optimization problems. The objective of the present study is to test the capability of applications and compare the performance of the four selected algorithms from “classical” (simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE)) and “new generation” (firefly algorithm (FFA), krill herd (KH), grey wolf optimization (GWO), and symbiotic organism search (SOS)) each by solving selected benchmark problems that are used in the literature for algorithm testing purpose. The selected test problems had very complex objective functions and associated constraints with multiple local optima. Among all selected algorithms, the “new generation” SOS and KH algorithm successfully solved most of all the selected benchmark problems and achieved the best solution for most of them. Among four “classical” algorithms, DE, and PSO effectively attained the optimal solution which was very close to the best one. However, the “new generation” algorithm performed much better than the “classical” one. Therefore, no firm conclusion can be done about the universally best algorithm and their performance may be varied for different benchmark problems. However, in this study for the seven selected test problems, SOS and KH exhibited the most promising result and great potential with respect to execution time also. This study gives some insights to use SOS and KH as the best-performing algorithms to the novice user who can easily get lost in the plethora of large optimization algorithms.
{"title":"A comparative study of metaheuristics algorithms based on their performance of complex benchmark problems","authors":"Tithli Sadhu, Somanth Chowdhury, Shubham Mondal, Jagannath Roy, J. Chakrabarty, S. Lahiri","doi":"10.31181/dmame0306102022r","DOIUrl":"https://doi.org/10.31181/dmame0306102022r","url":null,"abstract":"Metaheuristic approaches with extremely important improvements are very promising in the solution of intractable optimization problems. The objective of the present study is to test the capability of applications and compare the performance of the four selected algorithms from “classical” (simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE)) and “new generation” (firefly algorithm (FFA), krill herd (KH), grey wolf optimization (GWO), and symbiotic organism search (SOS)) each by solving selected benchmark problems that are used in the literature for algorithm testing purpose. The selected test problems had very complex objective functions and associated constraints with multiple local optima. Among all selected algorithms, the “new generation” SOS and KH algorithm successfully solved most of all the selected benchmark problems and achieved the best solution for most of them. Among four “classical” algorithms, DE, and PSO effectively attained the optimal solution which was very close to the best one. However, the “new generation” algorithm performed much better than the “classical” one. Therefore, no firm conclusion can be done about the universally best algorithm and their performance may be varied for different benchmark problems. However, in this study for the seven selected test problems, SOS and KH exhibited the most promising result and great potential with respect to execution time also. This study gives some insights to use SOS and KH as the best-performing algorithms to the novice user who can easily get lost in the plethora of large optimization algorithms.","PeriodicalId":32695,"journal":{"name":"Decision Making Applications in Management and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46362774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-15DOI: 10.31181/dmame060129022023u
A. Utku
The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries.
{"title":"Deep learning based an efficient hybrid prediction model for Covid-19 cross-country spread among E7 and G7 countries","authors":"A. Utku","doi":"10.31181/dmame060129022023u","DOIUrl":"https://doi.org/10.31181/dmame060129022023u","url":null,"abstract":"The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries.","PeriodicalId":32695,"journal":{"name":"Decision Making Applications in Management and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44161222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-15DOI: 10.31181/dmame060127022023r
H. Rana, Muhammad Umer, Uzma Hassan, Umer Asgher, Fabián Silva-Aravena, N. Ehsan
Prioritizing patients is a growing concern in healthcare. Once resources are limited, prioritization is considered an effective and viable solution in provision of healthcare treatment to awaiting patients. Prioritization is a preferred approach that helps clinicians to apportion scarce resources fairly and transparently. In this study, a novel methodology of prioritizing the patient is formulated using fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The objective is based on actual hospital conditions in Pakistan. The proposed methodology has two contributions: objective scoring mechanism that translates the patient’s condition given in human linguistic terms; and second methodology to prioritize patients according to corresponding scores. To validate the proposed methodology, simulation was carried out on actual data collected in real-time by surgeons, while providing consultations to their patients. The proposed methodology outperforms the traditional methodology by reducing average waiting time by 34% (from 4.246 to 2.810 days), minimize wait time and delays by 46.7% (from 15 to 8 days), and number of surgery days by 18%. The majority of the previously presented researched methodologies prioritize the patients subjectively. This study presents an objective methodology to prioritize the patients and decrease wait-times while ensuring transparency and equity.
{"title":"Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach","authors":"H. Rana, Muhammad Umer, Uzma Hassan, Umer Asgher, Fabián Silva-Aravena, N. Ehsan","doi":"10.31181/dmame060127022023r","DOIUrl":"https://doi.org/10.31181/dmame060127022023r","url":null,"abstract":"Prioritizing patients is a growing concern in healthcare. Once resources are limited, prioritization is considered an effective and viable solution in provision of healthcare treatment to awaiting patients. Prioritization is a preferred approach that helps clinicians to apportion scarce resources fairly and transparently. In this study, a novel methodology of prioritizing the patient is formulated using fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The objective is based on actual hospital conditions in Pakistan. The proposed methodology has two contributions: objective scoring mechanism that translates the patient’s condition given in human linguistic terms; and second methodology to prioritize patients according to corresponding scores. To validate the proposed methodology, simulation was carried out on actual data collected in real-time by surgeons, while providing consultations to their patients. The proposed methodology outperforms the traditional methodology by reducing average waiting time by 34% (from 4.246 to 2.810 days), minimize wait time and delays by 46.7% (from 15 to 8 days), and number of surgery days by 18%. The majority of the previously presented researched methodologies prioritize the patients subjectively. This study presents an objective methodology to prioritize the patients and decrease wait-times while ensuring transparency and equity.","PeriodicalId":32695,"journal":{"name":"Decision Making Applications in Management and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47151604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main goal of this paper is to harmonize the scales of normalized values of various attributes for multi-criteria decision-making models (MCDM). A class of models is considered in which the ranking of alternatives is performed based on the performance indicators of alternatives obtained by aggregating private attributes. The displacement of the domains of the normalized values of various attributes relative to each other and the local priorities of the alternatives are the main factors that change the rating when using various normalization methods. Three different linear transformations are proposed, which make it possible to bring the scales of normalized values of various attributes into conformity. The first transformation, the Reverse Sorting (ReS) algorithm, inverts the direction of optimization without displacing the areas of normalized values. The second transformation ‒ IZ-method ‒ allows researchers to align the boundaries of the domains of normalized values of various attributes in each range. The third transformation ‒ MS-method ‒ converts Z-scores into a sub-domain of the interval [0, 1] with the same mean values and the same variance values for all attributes. All transformations preserve the dispositions of the natural values of the attributes of the alternatives and ensure the equality of the contributions of various criteria to the performance indicator of the alternatives. The ReS-algorithm is universal for all normalization methods when converting cost attributes to benefit attributes. IZ and MS transformations expand the range of normalization methods when using nonlinear functions aggregation of attributes.
{"title":"On the conformity of scales of multidimensional normalization: An application for the problems of decision making","authors":"Irik Z. Mukhametzyanov","doi":"10.31181/dmame05012023i","DOIUrl":"https://doi.org/10.31181/dmame05012023i","url":null,"abstract":"The main goal of this paper is to harmonize the scales of normalized values of various attributes for multi-criteria decision-making models (MCDM). A class of models is considered in which the ranking of alternatives is performed based on the performance indicators of alternatives obtained by aggregating private attributes. The displacement of the domains of the normalized values of various attributes relative to each other and the local priorities of the alternatives are the main factors that change the rating when using various normalization methods. Three different linear transformations are proposed, which make it possible to bring the scales of normalized values of various attributes into conformity. The first transformation, the Reverse Sorting (ReS) algorithm, inverts the direction of optimization without displacing the areas of normalized values. The second transformation ‒ IZ-method ‒ allows researchers to align the boundaries of the domains of normalized values of various attributes in each range. The third transformation ‒ MS-method ‒ converts Z-scores into a sub-domain of the interval [0, 1] with the same mean values and the same variance values for all attributes. All transformations preserve the dispositions of the natural values of the attributes of the alternatives and ensure the equality of the contributions of various criteria to the performance indicator of the alternatives. The ReS-algorithm is universal for all normalization methods when converting cost attributes to benefit attributes. IZ and MS transformations expand the range of normalization methods when using nonlinear functions aggregation of attributes.","PeriodicalId":32695,"journal":{"name":"Decision Making Applications in Management and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44066845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fuzzy, rough, and soft sets are different mathematical tools mainly developed to deal with uncertainty. Combining these theories has a wide range of applications in decision analysis. In this paper, we defined a generalized Z-fuzzy soft -covering-based rough matrices. Some algebraic properties are explored for this newly constructed matrix. The main aim of this paper is to propose a novel MAGDM model using generalized Z-fuzzy soft -covering-based rough matrices. A MAGDM algorithm based on the AHP method is created to recruit the best candidate for an assistant professor job in an institute, and a numerical example is presented to demonstrate the created method.
{"title":"Generalized Z-fuzzy soft β-covering based rough matrices and its application to MAGDM problem based on AHP method","authors":"Pavithra Sivaprakasam, Manimaran Angamuthu","doi":"10.31181/dmame04012023p","DOIUrl":"https://doi.org/10.31181/dmame04012023p","url":null,"abstract":"Fuzzy, rough, and soft sets are different mathematical tools mainly developed to deal with uncertainty. Combining these theories has a wide range of applications in decision analysis. In this paper, we defined a generalized Z-fuzzy soft -covering-based rough matrices. Some algebraic properties are explored for this newly constructed matrix. The main aim of this paper is to propose a novel MAGDM model using generalized Z-fuzzy soft -covering-based rough matrices. A MAGDM algorithm based on the AHP method is created to recruit the best candidate for an assistant professor job in an institute, and a numerical example is presented to demonstrate the created method.","PeriodicalId":32695,"journal":{"name":"Decision Making Applications in Management and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47439902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-15DOI: 10.31181/dmame0310112022j
Sudhanshu Joshi, Manu Sharma, Prasenjit Chatterjee
The research aims to explore the strength of enablers, and adoption barriers present in omnichannel retailing (OCR), and discuss how organizations may focus to redesign their business models in emerging markets to manage the disruptive environment. The prominent enablers may enhance the omnichannel’ performance to deliver a unified experience across all channels during the pandemic time. The paper has used hybrid Multi-Criteria Decision-Making (MCDM) Methods. These methods are widely used by organizations for the exploration of the interrelationship among barriers and enablers affecting their performance. In the current study, 18 experts from different domains have examined and evaluated the 10 barriers and 7 enablers. The study reveals that integration, visibility, internet accessibility, and advanced distribution centers are the prominent enablers and driving the customer analytics enabler to strengthen their customer engagement and providing a unified experience to the. During the pandemic time the usage of the online channels have increased and thus retail channels may consider these enablers to enhance the unified experience level of the customers. The study also shows that inconsistency in price is the main adoption barrier followed by inconsistency in product discounts that should be minimized to engage customers effectively. The retail organizations need to understand the roadblocks in the adoption of OCR and should take relevant actions to minimize them. The retail organization or marketers may redesign their existing strategies based on price consistency, integration, visibility, information systems, and coordination to develop a unified experience across channels during the pandemic situation.
{"title":"Omni-Channel retailing enhancing unified experience amidst pandemic: An emerging market perspective","authors":"Sudhanshu Joshi, Manu Sharma, Prasenjit Chatterjee","doi":"10.31181/dmame0310112022j","DOIUrl":"https://doi.org/10.31181/dmame0310112022j","url":null,"abstract":"The research aims to explore the strength of enablers, and adoption barriers present in omnichannel retailing (OCR), and discuss how organizations may focus to redesign their business models in emerging markets to manage the disruptive environment. The prominent enablers may enhance the omnichannel’ performance to deliver a unified experience across all channels during the pandemic time. The paper has used hybrid Multi-Criteria Decision-Making (MCDM) Methods. These methods are widely used by organizations for the exploration of the interrelationship among barriers and enablers affecting their performance. In the current study, 18 experts from different domains have examined and evaluated the 10 barriers and 7 enablers. The study reveals that integration, visibility, internet accessibility, and advanced distribution centers are the prominent enablers and driving the customer analytics enabler to strengthen their customer engagement and providing a unified experience to the. During the pandemic time the usage of the online channels have increased and thus retail channels may consider these enablers to enhance the unified experience level of the customers. The study also shows that inconsistency in price is the main adoption barrier followed by inconsistency in product discounts that should be minimized to engage customers effectively. The retail organizations need to understand the roadblocks in the adoption of OCR and should take relevant actions to minimize them. The retail organization or marketers may redesign their existing strategies based on price consistency, integration, visibility, information systems, and coordination to develop a unified experience across channels during the pandemic situation.","PeriodicalId":32695,"journal":{"name":"Decision Making Applications in Management and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43406378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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