Decision Making: Applications in Management and Engineering

Hazard perception test among young inexperienced drivers and risk analysis while driving through a T-junction

Md Faysal Kabir — 2023-01-03

In this article, the hazard-based accident duration model and the reaction time due to various types of distractions are considered. This analysis is mainly considered T-junction or T-junctions like all other forms of road geometries which are more prone to accident. The model proposed is constructed by using a electronics devices along with driving simulator, to study the behavior of the yang inexperienced drivers as well as an experienced driver about their reaction by calling their phone from unknown numbers intentionally. The results show that drivers distracted by mobile phones uses the hard breaking due to least available time to respond after identification of an event. Some of the research and theory bearing on decision making and risk perception, driver situation awareness, and possible mediators of risk-taking is also analyzed here.

An application of a novel grey-CODAS method to the selection of hub airport in North Africa

Ibrahim Badi — 2023-01-19

Air transportation and airports are indispensable means of the modern world, where well-being and travel time reliability are pillars of strength. In the past few years, passengers traveling into, within, or out of Africa has enormously increased. However, the region lacks the basic facilities for linking African countries to the outside world. The research studies the possibility of assigning the optimal hub airport location in five North African countries, based on five main criteria. The criteria include airport pricing, hard and soft infrastructure, Catchment and landside access, as well as other aspects such as markets and airline partners. The study uses a hybrid grey-CODAS approach to decide the final priority of different decision alternatives. The method was implemented in steps to determine the criteria weights. Four experts participated in the evaluation to determine the importance of each criterion used for the ranking of suggested airport sites. The suggested sites include Cairo airport, Tripoli airport, Tunisia- Carthage airport, Algeria- Houari Boumediene airport, and Morocco- Mohammed V International airport. Model ranking suggested Morocco as the best alternative to locate a hub airport in North Africa.

New model for making resilient decisions in an uncertain context: The rational resilience-based decision-making model (R2DM)

Saloua Said — 2023-01-25

The present paper came into existence with the specific purpose of providing an optimized process that enables making resilient decisions in an uncertain context, and here our interest is particularly focused on the activity of new venture creation and on the entrepreneurial decision-making logic, in particular, effectuation theory. Within this framework, the rational resilience-based decision-making model (R²DM) is introduced. The relevant steps of this model are: (1) The identification of the problem and the available options. In this instance, the studied situation is the effectual customer co-creation case, and the available alternatives are planning, visionary, adaptative and transforming approaches, (2) The definition of the selection criteria that should be used to evaluate the available alternatives. In our case, these criteria are the six principles of entrepreneurial resilience, which are set out in detail, (3) The choice of the methodology to be followed in assessing the available options. To that end, three interconnected methods, based mainly on logical thinking and reasoning, are proposed. They are respectively devoted to Entrepreneurial resilience (ER) calculation, options classification using logistic regression algorithm, and the determination of the most resilient route to reach objectives employing graph theory. The obtained results are compared to what is advocated in the literature and conclusions are made.

A new framework for green selection of material handling equipment under fuzzy environment

Bipradas Bairagi — 2023-04-08

In the rapidly changing global circumstances, managements of industrial organizations are making decisions for their survival in business atmosphere in future. Decision makers in industries are steering their respective organizations towards for appropriate decision making satisfying the condition of ‘Go green’. Appropriate decision making in fuzzy environment is always a hard task. The current investigation explores a new multi criteria decision making approach for green selection of material handling equipment under fuzzy environment. The proposed technique has the capability of capturing effects of economical, environmental and social factors of benefit, non-benefit and target based criteria under uncertainty and vague information. The proposed method is illustrated with a suitable example on material handling equipment selection under fuzzy environment. The result clearly shows that the proposed technique is useful and effective in the decision making process regarding green material handling selection under fuzzy environment.

Understanding and acceptance of systems engineering in automotive product development

Tamás Kolossváry — 2023-04-08

Systems Engineering (SE) is a new engineering method for many firms in Automotive Product Development that expectedly advances their development processes to meet their stakeholder needs more effectively. Literature suggest that understanding and acceptance are key factors in the implementation, however comprehensive modes for their increase are barely discussed. In this paper, we propose a Participatory Action Research based on multiple research elements to find an effective technique for gaining understanding and acceptance on SE in a validated model environment of automotive industry called Formula SAE. We present practical outcomes at each steps of the implementation process and analyze the effect of improvements in the context of strategy, structure processes.

The significance of warehouse management in supply chain: An ISM approach

Ashutosh Verma — 2023-04-08

Warehouse management is the key aspect for an uninterrupted flow of products within a supply chain. This paper deals with the critical factors that are responsible for creating an impactful influence on the working of warehouse management. The analysis involves the selection of critical factors then applying Interpretive Structural Modelling (ISM) methodology to them in order to get the level partition and final ISM model. This research also involves the MICMAC analysis on the factors which classifies all the selected factors into four groups namely, autonomous variables, dependent variables, linkage variables and driver variables. This research will help the supply chain architects to establish a better and reliable warehouse system. As this research involves analysis of multiple domains that is why a variety of users can refer to this work for their businesses, also the ISM approach gives a good accuracy of the hierarchy of the factors which helps in deciding the most effective chronology of the implementation of various warehousing operations. Researchers can also refer to this work to get insights of the significance of warehouse management in the supply chain and also the complete working of the ISM methodology.

Selecting features by utilizing intuitionistic fuzzy Entropy method

Kiran Pandey — 2023-04-08

Feature selection is the most significant pre-processing activity, which intends to reduce the data dimensionality for enhancing the machine learning process. The evaluation of feature selection must consider classification, performance, efficiency, stability, and many factors. Nowadays, uncertainty is commonly occurred in the feature selection process due to time limitations, imprecise information, and the subjectivity of human minds. Moreover, the theory of intuitionistic fuzzy set has been proven as an extremely valuable tool to tackle the uncertainty and ambiguity that arises in many practical situations. Thus, this study introduces a novel feature selection framework using intuitionistic fuzzy entropy. In this regard, new entropy for IFS is proposed first and then compared with some of the previously developed entropy measures. As entropy is a measure of uncertainty present in data (features), features with higher entropy values are filtered out, and the remaining features having lower entropy values have been used to classify the data. To verify the effectiveness of the proposed entropy-based feature selection, some experiments are done with ten standard benchmark datasets by employing a support vector machine, K-nearest neighbor, and Naïve Bias classifiers. The outcomes of the study validate that the proposed entropy-based filter feature selection is more feasible and impressive than existing filter-based feature selection methods.

Generalized Z-fuzzy soft β-covering based rough matrices and its application to MAGDM problem based on AHP method

Pavithra Sivaprakasam — 2023-04-08

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.

Sustainable resilient supplier selection for IoT implementation based on the integrated BWM and TRUST under spherical fuzzy sets

Shabnam Rahnamay Bonab — 2023-04-08

Supplier selection process plays a vital role in supply chain management and is the most important variable in its success. With increasing environmental considerations, organizations must consider sustainability considerations and economic goals to protect the environment. Furthermore, the destructive effects of disruptions on the supply chain performance of companies have prompted organizational experts to pay special attention to the concept of resilience. This study developed an integrated approach based on the extended version of Multi-Criteria Decision-Making (MCDM) methods in a spherical fuzzy (SFS) environment to address sustainable and resilient IoT supplier selection. In the proposed approach, the main criteria (i.e., resilience, and sustainability) have been used in the supplier selection process. Then, these criteria are weighted using the developed SFS-Best-Worst Method (BWM), which reduces uncertainty in pairwise comparisons. In the next step, the 14 selected IoT suppliers are evaluated and prioritized by applying SFS-mulTi-noRmalization mUlti-Distance aSsessmenT (TRUST) that considers a multi-normalization algorithm to reduce subjectivity in normalized data. The results of this study shows that the pollution control and risk-taking sub-criteria are placed in the first and second priorities, respectively. The comparison of the results of the SFS-TRUST with other MCDM methods and sensitivity analysis demonstrates the performance of the proposed approach and its ranking stability in various scenarios.

Progress towards the innovation potential of the European union member states using grey relational analysis and multidimensional scaling methods

Magdalena Tutak — 2023-04-08

The article presents the results of a study aimed at assessing the level of innovation potential of European Union member states. The research was based on 8 diagnostic variables characterizing the two most important dimensions of innovation, namely human resources and R&D expenditures. As a result of the research, the levels of innovation potential of European Union countries between 2010-2020 were specified. The GRA approach and multidimensional scaling were used for the study. Based on the results, the European Union countries were divided into 4 classes. The findings showed large differences in this potential across countries, which was graphically illustrated by using the multidimensional scaling method. In addition, using two non-parametric tests, (Spearman Rank Correlation Coefficient and Kendall Correlation Coefficient), relationships between the innovation potential of member states and selected economic and innovation parameters of their economies were determined. The results of the study indicate that in the old EU-14 countries, this level was at a significantly higher level than in the new EU-13 countries. The EU-27 innovation potential leaders were found to be Finland, Sweden, Luxembourg, Denmark, and Germany. The worst performers, on the other hand, are Malta and Romania. Also, geographically, there were noticeable differences between the countries studied. The results presented should be used to develop strategies and implement policies for sustainable innovative development in the European Union. To the best of the authors' knowledge, this study is a new contribution to assessing the level of innovation potential of European Union member countries and determining the relationship of this potential with selected parameters of the economy of these countries.

The contagion number: How fast can a disease spread?

Misty Blessley — 2023-04-08

The burning number of a graph models the rate at which a disease, information, or other externality can propagate across a network. The burning number is known to be NP-hard even for a tree. Herein, we define a relative of the burning number that we coin the contagion number (CN). We aver that the CN is a better metric to model disease spread than the burning number as it only counts first time infections (i.e., constrains a node from getting the same disease/same variant/same alarm more than once). This is important because the Centers for Disease Control and Prevention report that COVID-19 reinfections are rare. This paper delineates a method to solve for the contagion number of any tree, in polynomial time, which addresses how fast a disease could spread (i.e., a worst-cast analysis) and then employs simulation to determine the average contagion number (ACN) (i.e., a most-likely analysis) of how fast a disease would spread. The latter is analyzed on scale-free graphs, which are used to model human social networks generated through a preferential attachment mechanism. With CN differing across network structures and almost identical to ACN, our findings advance disease spread understanding and reveal the importance of network structure. In a borderless world without replete resources, understanding disease spread can do much to inform public policy and managerial decision makers’ allocation decisions. Furthermore, our direct interactions with supply chain executives at two COVID-19 vaccine developers provided practical grounding on what the results suggest for achieving social welfare objectives.

The m-polar fuzzy set ELECTRE-I with revised Simos’ and AHP weight calculation methods for selection of non-traditional machining processes

Madan Jagtap — 2023-04-08

Using improvements to the recently published m-polar fuzzy set (mFS) elimination and choice translating reality-I (ELECTRE-I) approach for calculating criteria weights, the selection of a Non-Traditional Machining (NTM) process problem from the industry is solved in this research. The criteria weights for the m-polar fuzzy ELECTRE-I method are evaluated using the Analytical Hierarchy Process (AHP) approach and the Revised Simos' method. For the ELECTRE family's criteria weight calculations, the Simos’ approach has been revised. Many researchers calculated the weight of the criteria in the selection of the NTM process using the AHP approach. Problems with both physical and intangible properties can be solved using the m-polar fuzzy ELECTRE-I approach. Additionally, it has the ability to solve MCDM issues with more variables. The improved Simos' technique is used in this work because it incorporates user choices for the criteria, or user voting for the criterion. Using expert assistance, the AHP technique prioritizes the criterion based on pair-by-pair comparisons of the criteria. The AHP approach makes compromises between the criteria. The ultimate selection of the process based on the needed aim is affected by both tangible and intangible features in the NTM selection dilemma. The impact of criteria weight techniques on the choice of the NTM process is examined using a single dimensional sensitivity analysis. AHP approach is proven to be less stable for criteria weight variation than the improved Simos' weight calculation method. The updated Simos' method, which takes into account user preferences, performs better for the m-polar fuzzy ELECTRE-I algorithm than the AHP weight calculation method.

Examining the aspects of institution choice in connection with the internationalization of higher education

Henrietta Janik — 2023-04-08

International student mobility is increasing and transforming the image of higher education institutions. This study examines the factors that motivate international students in choosing a destination country. The aspects of the institution selection were examined using a non-parametric test, factor analysis and principal component analysis based on a sample of 270 people. Relying on the results of the research conducted in Hungary, the paper examines the factors influencing the selection of the destination country and further research examining the elements effecting the selection of the final host institution. Based on these results, the study disputes that economic and social impulses within the country of origin serve to “push” students abroad. However, the decision of which destination country to choose depends on several “pull” factors. The study features the usefulness of the quality of human environment, geographic proximity, tuition and living costs, scholarship opportunities, job opportunities after graduation, and the reputation of the destination country or institution, as well as the impact of linguistic proximity on student flow. The present empirical research reveals the close relationships between some pull factors, such as favourable geographical location, institutional support, the quality of the human environment, the expected balanced work environment after graduation, and the country of origin of international students. The results of the factor analysis confirm the underlying structure of the learning variables used in this research and provide empirical support for its application in future studies of international students' higher education study experiences.

Imprecise covering ring star problem

Anupam Mukherjee — 2023-04-08

In this paper, we formulate and solve an Imprecise Covering Ring Star Problem (ICRSP), which is a generalization of the Ring Star Problem (RSP). Here the objective of this problem is to find a subset of nodes in a network to minimize the sum of routing costs of interconnecting cycle and assignment costs of the nodes which are out of cycle, to their nearest concentrators such that no assigned node exceeds a predetermined distance (say, covering distance) from the concentrators. The covering distance, as well as the routing and assignments costs, are considered as fuzzy in the proposed ICRSP. A Modified Genetic Algorithm (MGA) is developed and used to solve this model for different confidence levels depending on the corresponding imprecise parameters, reducing it to deterministic form with fuzzy possibility and necessity approaches. Some comparisons with existing benchmark problems are made to justify the performance of the algorithm. As individual cases, some practical ICRSPs are also solved and presented numerically.

Prioritizing power outages causes in different scenarios of the global business network matrix

Saeed Shahi Moridi — 2023-04-08

Power outage is one of the significant problems for electricity distribution companies. Power outages cause customer dissatisfaction and reduce distribution companies' profits and revenues. Therefore, the electricity distribution companies are trying to moderate the leading causes of the outage. However, the dynamics of environmental conditions create uncertainties that require prioritizing the solutions of outages causes in different situations. Therefore, this study presents a scenario-based approach to prioritize power outage causes. Four case studies have been conducted in four cities of Kerman province in Iran. First, the prioritization criteria and causes of the outage were identified using literature and interviews with experts in this field. Then, the Global Business Network matrix was used to create four possible scenarios. Then, the Best-Worst method and TOPSIS method were applied to weight the prioritizing criteria and prioritize the causes of the outages in different scenarios. The results showed that working in the power network limit zone, as one of the causes of outage in Sirjan and Jiroft cities, has the most priority. Also, the collision of external objects, birds, and annoying trees should be considered by managers as the leading causes of outages in Bam and Kahnuj cities.

A comparative study of metaheuristics algorithms based on their performance of complex benchmark problems

Tithli Sadhu — 2023-04-08

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.

Association rule mining for prediction of COVID-19

Vishnu Kumar Rai — 2023-04-08

COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. The catastrophic shock of COVID-19 in India is also enormous. Currently, India has the largest number of COVID cases in Asia. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Based on a large clinical dataset, a linear regression model is also proposed having an accuracy of 73.9% in correctly predicting the occurrence of COVID-19.

Hybrid MCDM method on pythagorean fuzzy set and its application

Rishikesh Chaurasiya — 2023-04-08

Here in this article, a hybrid MCDM method on the Pythagorean fuzzy-environment is presented. This method is based on the Pythagorean Fuzzy Method based on Removal Effects of Criterion (PF-MEREC) and Stepwise Weight Assessment Ratio Analysis (SWARA) approaches. Here, the objective and subjective weights are assessed by PF-MEREC, SWARA model and the preference order ranking of the various alternatives is done through Complex Proportional Assessment (COPRAS) framework on the Pythagorean fuzzy set (PFS). The proposed method is the hybrid model of MEREC, SWARA and COPRAS methods. Further, the proposed model is used to identify the best banking management software (BMS) so that the bank can choose the robust bank management software tool to enhance its efficiency and excellence. Thereafter, a comparative discussion and sensitivity analysis of the proposed model is done with the existing techniques to judge the reasonability and efficiency of the proposed model.

On the conformity of scales of multidimensional normalization: An application for the problems of decision making

Irik Mukhametzyanov — 2023-04-08

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.

Zeghdοudi distribution in acceptance sampling plans based on truncated life tests with real data application

Rehab AlSultan — 2023-04-08

The acceptance sampling plan (ASP) is one of the key statistics resources in the industrial sector. It entails the decision-making process for accepting or rejecting the products. The investigated quality parameter is the test unit's mean lifespan. This study develops a new ASP for Zeghdοudi distribution (ZD) when the lifetime is shortened to a specific degree. The optimal plan parameters are accomplished by obtaining the minimum sample size mandatory to ensure the identified mean lifetime for fixing the consumer's risk. Besides, the characteristic operating function (OCF) values for the ASP are displayed, and the producer's risk is determined. Several helpful tables are developed for the suggested ASP based on the ZD for suitable employment. An actual data set is fitted to the Zeghdoudi model and other models to examine the applicability of the suggested ASP in the production sector.

Omni-Channel retailing enhancing unified experience amidst pandemic: An emerging market perspective

Sudhanshu Joshi — 2023-04-08

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.

Complex fermatean neutrosophic graph and application to decision making

Said Broumi — 2023-04-08

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.

Deep learning based an efficient hybrid prediction model for Covid-19 cross-country spread among E7 and G7 countries

Anıl Utku — 2023-04-08

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.

New intuitionistic fuzzy parametric divergence measures and score function-based CoCoSo method for decision-making problems

Dinesh Kumar Tripathi — 2023-04-08

The present study introduces a decision-making approach with the combined compromise solution (CoCoSo) under intuitionistic fuzzy sets (IFSs) named as the IF-CoCoSo method based on proposed divergence measures and score function. The aim of the presented approach is to obtain an effective solution for multi-criteria decision-making problems on IFSs context. In this line, a new procedure is presented to derive the criteria weights using generalized score function and parametric divergence measures of IFSs. To compute the criteria weight, a generalized score function and parametric divergence measures are developed on IFSs and discussed some interesting properties. Further, the presented approach is applied to rank and evaluate the therapies for medical decision making problems, which demonstrates its applicability and feasibility. Finally, comparative and sensitivity analyses are discussed for validating the developed method.

Intuitionistic multi fuzzy ideals of near-rings

Nadia Batool — 2023-04-08

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.

Evaluation of the social-cultural competitiveness of cities based on sustainable development approach

Hossein Komasi — 2023-04-08

The terms of competitiveness at the level of places and sustainable development were created in the 1980s and 1990s. In the beginning, most researchers emphasized the dimension of economic competitiveness, but in recent decades, other aspects of competitiveness, such as socio-cultural and environmental, have been highlighted. The aim of the present research is evaluation the social-cultural competitiveness in the city of Kermanshah based on a sustainable development approach. This research is descriptive and analytical, the TOPSIS model was used for data analysis, and Entropy was used to weight the indicators. After applying the weight of the indicators in the TOPSIS model, The research findings showed that the city of Kermanshah in the TOPSIS model, ranks 15th with a coefficient of 0.209; in the MABAC model, ranks 14th with a coefficient of -0.116 and in the EDAS model, ranks 14th with a coefficient of 0.122579271 is in the last and 14th position of socio-cultural competitiveness. The results showed that; Considering the nature of urban competitiveness, which is multidimensional, it is necessary to avoid a one-sided approach in urban competitiveness planning and to consider the socio-cultural, environmental, and security dimensions of urban competitiveness in an integrated manner. To improve the competitiveness of Iranian cities, while paying attention to the internal competitive advantages of the 15 studied cities, special attention should be paid to the fields of economic, environmental, socio-cultural, and security competitiveness. Each of these cities concerns their competitive role in transnational dimensions. This requires special attention to the national macro-plans.

Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach

Hassan Rana — 2023-04-09

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.

Reliability based framework for failure analysis in milk process industry

Nand Gopal — 2023-04-09

The suggested hybridized framework offers a paradigm for performance optimization-reliability-based analysis of milk processing unit’s (MPU) failure behavior in the dairy industry. The proposed hybridized framework led to the development of fuzzy Jaya Based Lambda-Tau (JBLT) technique-based mathematical model for computing various performance parameters of the under-consideration unit. The availability of the system drops by 0.044% as the level of uncertainty or spread level increases from ± 15% to ± 25% and drops to 0.088% as the level of uncertainty increases from ± 25% to ± 60%. To corroborate the system’s availability downward trend, the results of JBLT approach were compared with Particle Swarm Optimization-Based Lambda-Tau (PSOBLT) and conventional Fuzzy Lambda-Tau (FLT) techniques. The analysis findings were given to the maintenance manager so they could create the best maintenance schedule for the considered plant.

Hybridizations of Archimedean copula and generalized MSM operators and their applications in interactive decision-making with q-rung probabilistic dual hesitant fuzzy environment

Gogineni Anusha — 2023-04-09

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.

Coordination of a single-manufacturer multi-retailer supply chain with price and green sensitive demand under stochastic lead time

Anamika Dash — 2023-04-09

When dealing with uncertainties in the supply chain and ensuring customer satisfaction, efficient management of lead time plays a significant role. Likewise, besides managing inventory and pricing strategies adeptly in multi-retailer supply chains, it has become inevitable for firms to embrace green and sustainable business practices. In this context, this paper considers a two-level supply chain consisting of a single manufacturer and multiple retailers in which the manufacturer produces a single product and delivers it to the retailers in equal-sized batches. Each retailer faces a price and green-sensitive market demand. The lead time is assumed to be a random variable that follows a normal distribution. Shortages for retailer inventory are allowed to occur and are completely backlogged. The centralized model and a decentralized model based on the leader-follower Stackelberg gaming approach are developed. A price discount mechanism between the manufacturer and retailers is proposed. For the acceptance of this contract, the upper and lower limits of the price discount rate are established. Numerical outcomes exhibit that the price discount mechanism effectively coordinates the supply chain and enhances both environmental and economical performances. A sensitivity analysis with respect to some key parameters is performed, and certain managerial insights are emphasized.

Logic behind the continuous growth across the s-curve - A method for the structured construction of a business growth flywheel model

Bo Dong — 2023-04-09

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.

Efficient routing optimization with discrete penguins search algorithm for MTSP

Ilyass Mzili — 2023-04-11

The Travelling Salesman Problem (TSP) is a well-known combinatorial optimization problem that belongs to a class of problems known as NP-hard, which is an exceptional case of traveling salesman problem (TSP), which determines a set of routes enabling multiple salesmen to start at and return to home cities (depots). The penguins search optimization algorithm (PeSOA) is a new metaheuristic optimization algorithm. This paper presents a discrete penguins search optimization algorithm (PeSOA) for solving the multiple traveling salesman problem (MTSP). The PeSOA is evaluated by a set of benchmarks of TSP instances from TSPLIB library. The experimental results show that PeSOA is very efficient in finding the right solutions in a reasonable time.

Detecting business cycles for Hungarian leading and coincident indicators with a Markov switching dynamic model to improve sustainability in economic growth

Albert Molnár — 2023-04-20

This paper applies the hidden Markov switching dynamic regression (MSDR) model to estimate transition probabilities of the Hungarian GDP between recessionary and expansionary periods. The transition probabilities are then compared to the OECD Hungarian binary business cycle indicator to assess the predictive power of the model. The paper proposes a linear model with a mean and a homoscedastic component. The level of symmetricity between the GDP and business cycles is explained by the panel data variables (Unemployment rate, IPI index, Inflation, BUX year-on-year change, and 10-3 Year sovereign bond yield spreads). It is assumed in this paper that by extending the model to encompass an exogenous variable listed in the panel data, essentially making the model bivariate, the maximum likelihood function would capture the business cycle more accurately. The results show that by plugging the unemployment rate as the exogenous variable in the regression, our model’s accuracy is 70%.

An integrated framework for classification and selection of stocks for portfolio construction: Evidence from NSE, India

Sayan Gupta — 2023-04-24

Investment extortion in the stock market is a crucial aspect considered by the investors. Therefore, investors implemented different strategies. This study was intended at constructing an investment portfolio (IP) of stocks within the NSE 100 listed companies of Non-parametric nature, fulfilling the basic premise of portfolio making that is, reducing risks while yielding an attractive return higher than any other instrument for the investors. Using DP omnibus test, the desired sample of companies following the non-normal distribution was achieved. Using financial beta, we have selected the outcome based on the nature of their ‘return’ and ‘risk'. We introduce TOPSIS (Technique for order of performance by similarity to ideal solution), a multi-criteria decision-making process (MCDM) to study the profitability of stocks, rank wise for each year, and finally, the Bayes portfolio model help to select the overall profitability associate with low risk for the construction of the portfolio.