Decision-making Issues Research Articles

A hybrid decision-making conceptual framework based on generalized information quality under neutrosophic evidence theory: a comparative analysis

Simulating uncertainty for dealing with actual events is one of Artificial Intelligence's key difficulties and challenges. The ultimate objective for decision-makers is to manage uncertainty, particularly in indeterminate scenarios when it is not the case that the solution of the problem can be expressed with true or false values. As a result, new techniques to facilitate the interpretation of indeterminacy are currently under development. Neutrosophic logic (NL), which addresses the concept of neutralities, extends classic logic, fuzzy logic, paraconsistent logic, intuitionistic logic, and so on. The single-valued neutrosophic set (SVN) is a subclass of neutrosophic sets that has recently been presented. Solving multicriteria decision-making problems is an essential use case for SVNs to be applied. The research objectives of the article are twofold. First, we examine the potential of utilizing single-valued neutrophilic sets in a more efficient manner to address the issue of multicriteria decision-making. Within this framework, our aim is to explore and extend the concept of information quality as an uncertainty measure by comparing it to neutrosophic Dempster–Shafer (D–S) evidence theory in the context of decision-making. As a proposed solution to the aforementioned research objectives, this manuscript suggests and implements a novel conceptual framework for determining and quantifying the similarity measure between SVNSs in a multicriteria decision-making context under the principles of D–S evidence theory. Finally, illustrative case studies are given to support the logic and practicality of the suggested methodology compared to current methodologies.

Continuous Pythagorean Fuzzy Set Extension with Multi-Attribute Decision Making Applications

One of the most recent innovations in the field of fuzzy sets has been continuous intuitionistic fuzzy sets (CINFUSs), where membership and non-membership degrees are defined by nonlinear functions, as a direct extension of intuitionistic fuzzy sets (IFSs). The membership and non-membership degrees of CINFUSs can account for uncertainty at every point since they are represented by continuous structures that change based on how the decision-maker responds to uncertainty. On the other hand, Pythagorean fuzzy sets (PFSs) allow for a more accurate representation of the data and a better way to handle uncertainty in decision issues by reflecting the hesitations of decision-makers over a larger range. Due to these superior advantages of CINFUSs and the fact that PFSs are more comprehensive than IFSs, in this study, continuous Pythagorean fuzzy sets (CPFUSs) have been aimed at introducing to define uncertainty more broadly and accurately by representing PFSs with a continuous structure as in IFSs. In this study, firstly, the basic principles and mathematical operators of CPFUSs have been developed and presented. Then, multi-attribute decision-making (MADM) models have been developed by considering different aggregation operators to indicate the feasibility and effectiveness of the continuous Pythagorean fuzzy (CPFU) extension. The developed CPFU-MADM models have been implemented to the solution of two different decision problems: green supplier selection and waste disposal site selection problems. In addition, sensitivity analyses have been conducted on criterion weights, expert weights and parameters in order to demonstrate the reliability and stability of the developed models. Furthermore, the validity and superiority of the developed models have been indicated by the comparative analysis conducted with IFSs and PFSs-based MADM models in the literature. MADM applications have shown that continuous Pythagorean fuzzy sets can successfully represent the expert decisions with different attitudes within the same model. It has been observed that the rankings of alternatives according to aggregation operators do not change even when there are differences in the score values of the alternatives.

Decision Making in the Case of Confirmed Data Neutrosophic Linear Models to Choose the Advertising Medium

In light of the great development witnessed by our contemporary world, it has become necessary to focus on scientific methods and use the quantitative method to reach more accurate decisions, appropriate to the surrounding circumstances and factors. The process of decision-making and choosing the optimal alternative depends on the type and quality of data that describes the issue for which the decision is to be made. Regarding it, in this chapter we present a study of the issue of determining the ideal advertising medium to display a company’s products. This issue is considered one of the issues of decision-making in the case of confirmed data, so we build the appropriate mathematical model and through the optimal solution to it we can make the ideal decision through which the company achieves its goal from the campaign. Informative, we will divide this study into two parts. In the first section, we will develop a general formula for this issue, and the data will be classical values. We will obtain a linear mathematical model. In the second section, we will formulate the issue from the perspective of neutrosophic science, meaning we will take the data as neutrosophic values, obtaining a linear neutrosophic model.

Development of an Importance Scale for Seawater Intake Types Based on Physical Parameters Database

This article presents an advanced Geographic Information Systems (GIS) based interface that innovates coastal water resource management in Morocco, with specific application to the ASSILAH region. This solution integrates spatial analysis with multi-criteria decision support, particularly focusing on the geographical distribution and territorial challenges of the Moroccan coastline, using programming tools such as Excel Visual Basic for Applications (VBA). The methodology combines GIS spatial analysis capabilities with the Analytic Hierarchy Process (AHP) to evaluate complex geographical parameters including bathymetry and coastal infrastructure. The integration of geospatial data provides a clear and precise visualization of potential areas for seawater intake installations, thereby facilitating the understanding of territorial issues for decision-makers. The AHP method, known for its robustness in multi-criteria decision support, allows for choosing the optimal type of seawater intake. The interface, developed through Excel and VBA, transforms complex spatial and analytical data into an accessible decision support tool. The resulting system offers regional planners a comprehensive framework for coastal sustainable infrastructure development in the Assilah coastal region as a pilot case for Morocco's unique geographical context.

A Comprehensive Review of MCDM Methods, Applications, and Emerging Trends

Multiple-criteria decision-making (MCDM) approaches have become vital for tackling complicated, multi-objective decision-making issues in dynamic and unpredictable situations. This paper covers the history of MCDM methodologies, ranging from conventional methods like the analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to sophisticated innovations embracing fuzzy logic, hybrid models, and artificial intelligence (AI). It analyses the numerous uses of MCDM in business, engineering, healthcare, and environmental management, highlighting their adaptation to both qualitative and quantitative criteria. The approaches adopted include an examination of conventional and current frameworks, identifying their strengths and shortcomings. Emerging technologies such as integrating Blockchain, the Internet of Things, and big data analytics are studied, revealing their potential for real-time and dynamic decision-making. Key findings underscore the limitations of uncertainty modeling, computational complexity, and scalability while also revealing potential for multidisciplinary research and sustainability-focused applications. This study finds by offering actionable recommendations for researchers and practitioners, advocating for the establishment of AI-integrated, real-time decision-making frameworks and standardized evaluation standards to address contemporary challenges and enhance the practical relevance of MCDM methods.

Money and Mission: Models of Funding God’s Mission

While the Great Commission was not predicated on money, the lack of finances has been an obstacle to mission participation for many Global South Christians. This article examines the challenges that money poses for Global South mission, proposes that the use of “mission funding models” can clarify the issues of mission funding decisions, challenges the prevailing understanding of mission, and charts a way forward to increase mission participation for those who are financially disadvantaged.

Optimizing site selection for construction and demolition waste resource treatment plants using a hesitant neutrosophic set: a case study in Xiamen, China

The site selection of construction and demolition waste resource treatment plants is a complicated multi-criteria decision-making (MCDM) issue of ‘not in my back yard’ (NIMBY) facilities, which is affected by spatial, economic, societal, environmental and other factors. Traditional site selection MCDM methods expose the shortcomings of evaluation information representation and spatial factor analysis. This study develops an optimized MCDM site selection method in a hesitant neutrosophic set (HNS) environment, capable of effectively handling uncertainty and fully incorporating spatial considerations. The innovation of this research lies in integrating HNS into MCDM, combined with geographic information system technology, revised DEMATEL-based analytic network process method, cosine similarity and Euclidean distance similarity measures. A case study in Xiamen, China, validates this method’s effectiveness, and sensitivity analysis and comparative studies demonstrate its superior stability, robustness and resolution. The main contribution of this study is the development of an MCDM optimization method for siting large-scale NIMBY facilities.

An Empirical Study on the Development and Assessment of a Supplier Selection Model based on the Analytical Hierarchy Process in the Al Kharj Industrial Sector

Selecting a supplier is a critical strategic decision for supply chain management in today's global context. The process involves evaluating suppliers based on core competencies, pricing, delivery timeframes, location, data gathering, and related risks. Suppliers play a crucial role in an organization's profitability and stability. Finding the most optimal supplier can help industries reduce material expenses and maintain their competitive advantage. The supplier not only impacts the organization's profit margin but also its economic strength. Choosing a supplier requires considering qualitative and quantitative elements, making it a decision issue with several criteria. This study aims to create and evaluate a supplier selection model using the analytical hierarchy approach, focusing on a specific case study. When selecting the best supplier, it is crucial to consider tangible and intangible elements that may conflict. The supplier selection process considers several criteria, including qualitative and quantitative variables. The proposed methodology involved a literature review and informal interviews with industry experts and academics to establish the selection criteria. "Quality Supplier Co." was chosen due to the paramount importance of their quality. This research will comprehensively analyze several criteria to identify suppliers accurately.

A Hybrid Model to Enhance The Performance of Classifier in Financial Distress Prediction

Accurately predicting financial distress is a critical issue in financial decision-making. Financial distress must be detected as early as possible as an important determining factor in decision-making for internal companies and financial institutions related to financing or loan decisions. Various studies on financial distress prediction in Indonesia have been carried out, ranging from traditional statistical approaches to machine learning. However, the performance of the two methods is still not optimal. Therefore, this study tries to develop machine learning techniques by combining cluster analysis and classification in a hybrid model to improve the prediction model’s performance. The case study adopted in this study is the prediction of financial distress in non-financial companies listed on the IDX from 2018-2021 by combining k-means clustering and Support Vector Machine. The analysis results show that the hybrid classifier has an accuracy value of 92.7%, which is higher than the accuracy of the single classifier, which is 88.6%.

Organisational justice and employees’ commitment of selected Nigerian banking sector

Nigerian banking institutions in the 21st century face significant workplace challenges, including employee commitment, managerial decision-making issues, perceptions of inequality, unfair treatment and a lack of stability and reliability due to competitive forces. This study investigates the effect of organisational justice variables on employee commitment in a specific sector. A descriptive research approach was used with 361 questionnaires distributed but only 332 returned resulting in a 91.97% return rate. This study used Statistical Packages for the Social Sciences (SPSS) for descriptive statistics and the Structural Equation Model (SEM-PLS) for inferential statistics to evaluate the generated hypothesis. This study reveals that employees' perception of distributive justice significantly influences their commitment; procedural justice affects their normative commitment and interactional justice influences their continuance commitment. It emphasises the importance of understanding the relationship between organisational justice and employee commitment to guide stakeholders in creating fair and positive work environments promoting employee well-being and enhancing organisational outcomes.

How might model uncertainty and transaction costs impact retained earning & dividend strategies? An examination through a classical insurance risk model

Model uncertainty and ambiguity aversion have important consequences for decision-making under uncertainty in diverse fields such as insurance, finance and economics. Although model uncertainty has been considered in decision-making problems in finance and economics, as well as problems relevant to (re)-insurance, relatively little attention has been given to exploring implications of model uncertainty and ambiguity aversion for the optimal policies governing cash retention and dividend payout. On the other hand, taxes and transaction costs/fees have a significant impact on retained earnings and dividend strategies. Despite its technically challenging, their impacts on optimal dividend strategies have been studied in the literature. However, consequences of model uncertainty and ambiguity aversion for optimal dividend payout policies and related decision-making issues in the presence of transaction costs/taxes have not been well-understood. This paper aims to explore this relatively unknown zone and to articulate this technically challenging problem. Specifically, we shall provide a rigorous approach to examine the impacts of model uncertainty and ambiguity aversion on optimal cash retention and dividend payout strategies with fixed and proportional transaction costs/taxes. Our key findings include (1) model uncertainty and ambiguity aversion change the qualitative behaviour of optimal strategies. Say the optimal strategy is a multi-level lump-sum strategy and tends to have more levels than that of the problem without capturing model uncertainty (2) the value function tends to be rougher (in terms of smoothness) than that of the problem without incorporating model uncertainty.

Building integrated assessment model for IoT technology deployment in the Industry 4.0

Internet of Things (IoT) platforms have become the building blocks of any automated system but they are more important in case of industrial systems where sensitive data are captured and handled by the information system. Therefore, it is imperative to deploy the right IoT platform to perform the computational and operational tasks in a better way. During the last few years, an array of IoT technologies/platforms with different capabilities and features were introduced in the markets. This abrupt rise created selection and decision-making issues particularly for the network engineers, designers, and industrial managers due to a lack of technical understanding and skill in this area. Therefore, we present an integrated assessment model focusing on evaluating and ranking IoT platforms in the industrial environment. It encompasses multiple methods such as the proposed model leverages a well-known data collection technique such as Delphi for data collection related to the criteria features. It adopts the Analytic Hierarchy Process (AHP) for giving weights to the criteria features. The technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been applied for the evaluation of the top twenty (20) Industrial IoT(IIoT) platform alternatives according to the proposed criteria. It selects the most rational choice of IoT platform that can be employed in the Industry 4.0 setting. The proposed integrated assessment model produces the most accurate and consistent outcomes. Hence, it is believed that it can be used as a guideline by different stakeholders like researchers, developers, network engineers, and policymakers for the assessment and deployment of IoT platforms in the industrial environment. It is believed that it is the first kind of multi-methods integrated assessment mode for the assessment, decision-making, and prioritization of IoT technologies in the industry 4.0 domain.

KEY ASPECTS THAT MAKE DECISION-MAKING TRAINING IN THE FIELD OF CRISIS MANAGEMENT TAILORED TO THE IDENTIFIED GAPS AND NEEDS

The fundamental objective of crisis management exercises is to provide participants with therequisite knowledge and skills to enable optimal responses in crisis situations. One particularlyefficacious and engaging form of exercise is decision-making training. The utilisation of virtualreality-based decision-making training, incorporating multimedia and “acting,” represents aneffective methodology for the assessment of procedures that involve multiple entities. The objectiveof this study is to present a methodology for adapting decision-making training to address theexisting gaps, needs, and decision-making issues of the entity for which the exercise is organized,thereby facilitating more effective and optimal execution. Furthermore, the paper presents datasources utilized by the authors in the organization of decision-making training. The intendedaudience for the publication is any individual, entity, and/or institution that engages in crisismanagement on a daily basis, including strategic planners, decision makers and practitioners.Consequently, the paper offers practical recommendations for the organization of an effective anddedicated decision-making training in the field of crisis management, based on a synthesis of theliterature and the authors’ experience.

Enhancing Expert Decision-Making for Wastewater Treatment Plants with Seidel Laplacian Energy and Cosine Similarity Measure in Intuitionistic Fuzzy Graphs

Wastewater treatment facilities’ main goal is to protect the public and environment from the hazardous and poisonous materials found in wastewater. Water treatment facilities were developed to speed up the natural process of cleansing water. A novel cosine similarity measure across intuitionistic fuzzy graphs has been proven to be more effective than certain present ones in group decision-making issues using example verification. This paper provides a unique approach for calculating expert-certified, well-known scores by finding the ambiguous information of intuitionistic fuzzy preference relations as well as the regular cosine similarity grades from one separable intuitionistic fuzzy preference relation to another. The new technique considers both "objective" and "subjective" information provided by experts. Using intuitionistic fuzzy preference relations, we provide workable techniques for judging experts’ eligible reputational ratings. This can be used to raise or decrease the relevance of the stated criteria in an evaluation that takes into account several competing elements. We give a solution to a decisional problem by using two effective methods: the newly constructed cosine similarity measure and the Seidel Laplacian energy (SLe+) of an intuitionistic fuzzy graph. Finally, two working procedures and circumstances are offered to show the effectiveness and superiority of the proposed techniques.

A hierarchical sorting method based on hierarchical clustering and its application in port evaluation

Abstract A hierarchical sorting method with semantic description is developed to address sorting issues in management decisions. This approach achieves optimal hierarchical clustering results by incorporating indicators of intra-similarity, inter-similarity, and total similarity. Additionally, it integrates the axiomatic fuzzy sets theory to produce a semantic description of the hierarchical sorting results. The developed method can provide a good semantic expression for sorting results from a multi-level perspective and assist decision-makers in making reasonable decisions at different levels. This study takes the 40 main seaports as application examples for analyzing the applicability and practicability of the designed method.

Dynamics of Urban Electric Transport Development in Million Cities in Russia

This article analyzes the dynamics of state policy development in the field of urban electric transport development in the Russian Federation on the example of cities with millions of inhabitants. The paper addresses the issue of decision-making by executive authorities at the state and regional levels on the development of all types of urban passenger electric transport that have developed on the territory of the Russian Federation in the period from the appearance of the first such city — St. Petersburg — in 1897 to the present. The approach to the analysis of state policy in the field of urban electric transport development is systematized: the diversification of types of electric transport into main and auxiliary ones is carried out; the periods of assigning the status of million-city to cities are determined; the key evaluation parameters are indicated — the length of the network, the number of rolling stock and the number of routes (lines). As a result of the conducted diachronic analysis, the high role of electric transport in the life of cities with millions of inhabitants has been revealed, while in most such cities there is a stagnation of urban electric transport systems. Metro systems are characterized by gradual development with different dynamics, tram systems are mainly characterized by stagnation with local development, and trolleybus systems are equally characterized by stagnation and degradation, with individual cases of development. The dynamics shows that preferences are given mainly to such types of transport as tram and subway. Despite the decline of electric transport systems in a number of million cities, at the moment there is a gradual growth of existing systems. It is determined that from the point of view of electric transport, as well as mass transport in general, the most negative situation is in Voronezh, where the tram network has been eliminated, the metro system is missing, and the trolleybus system has significantly degraded.

Cultural dynamics in business streamlining: A comprehensive investigation into the impact of cross-cultural management on the streamlining of outsourced business processes

Outsourcing has, over the years, become one best strategic tool for increasing efficiency and competitiveness in the international business environment. However, as it relates to the perspective of enhancing business achievement through cultural factors, cost reduction, and focus on firm-specific competencies, contextual factors still control the effectiveness of outsourced business processes. In this essay, cultural management is set as the cornerstone or the key to dealing with issues in cross-cultural communication, decision-making, and cooperation. Using data from industry sources, including Deloitte and SHRM, the paper establishes the consequences of cross-cultural disparities on project delivery and company profitability as the key areas of interest. These frameworks consist of Hofstede’s Cultural Dimensions, Trompenaars’ Model, and GLOBE Study which give essential knowledge on how management can enhance an organization’s cultural diversity. This paper shows that cultural management is becoming increasingly critical at a time when companies need to manage diverse teams and develop sustainable cooperation strategies. The focus of the analysis is on the prevention and utilization of cultural differences to enhance negative factors and operational results. Thus, filling the gaps in the existing literature, this study provides practical recommendations and theoretical frameworks that businesses may use to enhance the effectiveness of outsourced activities and develop long-term success across the increasingly interconnected global marketplace.

Similarity measure for complex non-linear Diophantine fuzzy hypersoft set and its application in pattern recognition

As a hybrid fuzzy extension of the complex non-linear Diophantine fuzzy set, the complex non-linear Diophantine fuzzy hypersoft set was developed by fusing it with the hypersoft set. To address multi-sub-attributed real-world similarity problems within complex non-linear Diophantine fuzzy ambiance, this study proposes distance measures and five innovative similarity measures such as Jaccard similarity measure, exponential similarity measure, cosine similarity measure, similarity measure based on cos function, and similarity measure based on cot function for complex non-linear Diophantine fuzzy hypersoft set. Furthermore, based on proposed similarity measures, a highly effective algorithm is provided for handling decision-making issues exquisitely in the pattern recognition field, along with an illustrative example of mineral identification. Then, to demonstrate the validity, reliability, robustness, and superiority of the proposed notion and algorithm, a detailed comparative study with proper discussion has been presented in the study.

Evaluating Enterprise Resource Planning Adoption Using the EDAS Method: A Strategic Approach

Adoption of enterprise resource planning (ERP) systems is the process of putting ERP systems into use inside an organisation. It entails the fusion of diverse corporate operations into a centralised software platform, including finance, human resources, and supply chain management. By enabling real-time utilisation of integrated data and automated processes, ERP adoption attempts to improve decision-making, increase efficiency, and streamline operations. To achieve successful installation and user acceptance, rigorous planning, arrangement, and instruction are needed. For businesses looking to streamline operations and obtain a competitive edge in today's digital environment, ERP adoption is essential. The implementation of enterprise resource planning (ERP) is significant for research since it can help to better understand and manage organisational operations. Scientists can learn more about the elements that contribute to successful implementation, pinpoint best practises, and address issues that organisations have throughout the adoption process by researching ERP adoption. By optimising resource allocation, increasing operational efficiency, and implementing ERP, this research will assist organisations in making knowledgeable judgements. Additionally, research on ERP adoption aids in the creation of frameworks and theories in the discipline of information technology and offers advice to practitioners and decision-makers working on ERP projects. A technique for making decisions that takes into account an alternative's distance from the mean answer is called evaluation based on distance from the average solution (EDAS). It gauges the performance of the alternatives using the distance between them and the typical solution. When evaluating options, EDAS takes both positive and negative distances into account. To rank and choose the best alternative in multi-criteria decision-making issues, this method is frequently utilised. Decision-makers can use EDAS to make well-informed decisions by taking into account how far away various options are from the typical solution. The Shipping took the first place while Tracking took the Last place in the Enterprise resource planning adoption. In this paper Enterprise resource planning adoption, The Shipping took the first place while Tracking took the Last plac

ORGANIZATION OF COUNTERMEASURES AGAINST THE NEGATIVE IMPACT OF RISKS IN LOGISTICS TRANSPORT SYSTEMS

The article is devoted to the issue of effective management decision-making in the organization of counteracting the negative consequences of the impact of risks in logistics transport systems. The purpose of the article is to identify risks in the logistics transport system and provide adaptive information on risk management strategies and recommendations. Modernization of the management mechanism based on risk management tools is relevant to ensure the efficiency and reliability of the logistics transport system at all its levels. The study established the fundamental characteristics of the risk management process and developed a set of management measures to reduce transport risks for logistics transport systems. This set of measures significantly affects the reduction of the negative impact of risks on each of the components of the transport and logistics system at different levels of management and is the basis for the integration of management methods, models and tools into a single mechanism, which is a systematic approach to risk management of the logistics transport system. The results of this study create a comprehensive basis for risk management of the logistics transport system. The results of this study indicate that the proposed systematic approach to risk management of the logistics transport system can be defined as an integrated management of business processes related to the promotion of products and the corresponding flows from their source to the final consumer. The effectiveness of this approach has been demonstrated in achieving optimal efficiency of transport enterprises. In the light of the above considerations, the implementation of this systemic approach serves to create an appropriate organizational basis for the implementation of countermeasures against the negative consequences of the impact of risks in logistics transport systems. In essence, it is a risk management tool.