Decision-making Issues Research Articles

Big Data Clustering Using Chemical Reaction Optimization Technique: A Computational Symmetry Paradigm for Location-Aware Decision Support in Geospatial Query Processing

The emergence of geospatial big data has opened up new avenues for identifying urban environments. Although both geographic information systems (GIS) and expert systems (ES) have been useful in resolving geographical decision issues, they are not without their own shortcomings. The combination of GIS and ES has gained popularity due to the necessity of boosting the effectiveness of these tools in resolving very difficult spatial decision-making problems. The clustering method generates the functional effects necessary to apply spatial analysis techniques. In a symmetric clustering system, two or more nodes run applications and monitor each other simultaneously. This system is more efficient than an asymmetric system since it utilizes all available hardware and does not maintain a node in a hot standby state. However, it is still a major issue to figure out how to expand and speed up clustering algorithms without sacrificing efficiency. The work presented in this paper introduces an optimized hierarchical distributed k-medoid symmetric clustering algorithm for big data spatial query processing. To increase the k-medoid method’s efficiency and create more precise clusters, a hybrid approach combining the k-medoid and Chemical Reaction Optimization (CRO) techniques is presented. CRO is used in this approach to broaden the scope of the optimal medoid and improve clustering by obtaining more accurate data. The suggested paradigm solves the current technique’s issue of predicting the accurate clusters’ number. The suggested approach includes two phases: in the first phase, the local clusters are built using Apache Spark’s parallelism paradigm based on their portion of the whole dataset. In the second phase, the local clusters are merged to create condensed and reliable final clusters. The suggested approach condenses the data provided during aggregation and creates the ideal clusters’ number automatically based on the dataset’s structures. The suggested approach is robust and delivers high-quality results for spatial query analysis, as shown by experimental results. The proposed model reduces average query latency by 23%.

Decision Making Near End of Life: A Qualitative Exploration of the Lived Experiences of Jordanian Healthcare Providers.

Discussions related to a patient's prognosis and interventions near end of life are challenging and stressful for healthcare providers. Many reported experiencing emotional distress and discomfort during the decision-making process. The aim of this study was to describe the lived experience of nurses and physicians who participate in decision making near the end of a patient's life. A descriptive, phenomenological approach was used with a purposive sample of 7 nurses and 6 physicians from 2 palliative and end-of-life care institutions in Jordan. Data derived from focus group discussions conducted online via the ZOOM video communication application, and analysis followed Colaizzi's qualitative methodology. Three major themes emerged from the healthcare providers' experiences. Initially, the participants described how they "strive toward optimal decision-making near end of life," "the overwhelming experience of decision making near end of life," and finally, their struggle in sharing details about the end of life when patients ask and whether to tell or not to tell them as "the time for hard talk." The healthcare providers' experiences revealed several issues of decision making near a patient's end of life, such as a lack of appropriate training and administrative support, emotional distress, fear of legal liability, and the burden of a hard talk with patients. These findings necessitate institutional support of setting appropriate policies and guidelines, staff counseling, and healthcare provider support. Institutions providing palliative and end-of-life care can use this study's findings to support their staff by setting appropriate practice guidelines and providing staff training and counseling.

Preventing profiling for ethical fake news detection

A news article’s online audience provides useful insights about the article’s identity. However, fake news classifiers using such information risk relying on profiling. In response to the rising demand for ethical AI, we present a profiling-avoiding algorithm that leverages Twitter users during model optimisation while excluding them when an article’s veracity is evaluated. For this, we take inspiration from the social sciences and introduce two objective functions that maximise correlation between the article and its spreaders, and among those spreaders. We applied our profiling-avoiding algorithm to three popular neural classifiers and obtained results on fake news data discussing a variety of news topics. The positive impact on prediction performance demonstrates the soundness of the proposed objective functions to integrate social context in text-based classifiers. Moreover, statistical visualisation and dimension reduction techniques show that the user-inspired classifiers better discriminate between unseen fake and true news in their latent spaces. Our study serves as a stepping stone to resolve the underexplored issue of profiling-dependent decision-making in user-informed fake news detection.

The aggregation of Z-numbers based on overlap functions and grouping functions and its application on group decision-making

In this paper, we propose a series of novel aggregation techniques based on overlap and grouping functions for group decision-making (GDM) issues in the Z-number environment. Firstly, we introduce an optimization model to determine the underlying distribution for Z-numbers, meanwhile, the mean function of Z-numbers is revised to better reflect the three-dimensional structure of the Z-numbers. And then, a series of operations for Z-numbers using overlap and grouping function are defined, and their properties are also investigated. Subsequently, we construct the overlap function-based Z-number weighted Bonferroni mean (OZWBM) operator to integrate the information in the Z-number environment. Furthermore, to verify the efficiency of novel aggregation techniques, a new energy investment selection issue is addressed. Besides giving the rankings of alternatives, by analyzing the calculation results, we can also derive the degree of preference from investment experts in scoring each investment object. Finally, by comparing the existing methodologies, we confirm that the new approach is effective and reasonable for the multi-criteria group decision-making (MCGDM).

2-tuple linguistic decision-making with consistency adjustment strategy and data envelopment analysis

2-tuple linguistic preference relations (2-TLPRs) are useful tools for addressing decision-making issues where the decision makers (DMs) are inclined to apply linguistic variables to express evaluation information. To retain the DM’s initial preference information as much as possible, this paper provides a 2-tuple linguistic decision-making method that incorporates a consistency adjustment algorithm and a 2-tuple linguistic data envelopment analysis (DEA) model. As two important aspects of decision-making, it is worthy to further study the improvements in consistency and weight generation for alternatives with 2-TLPRs. In this paper, in order to adjust the consistency of an original 2-TLPR to a predetermined level, a convergent consistency-improving algorithm is first presented, and we employ a minimum adjustment strategy to preserve the DM’s initial evaluation information. A 2-tuple linguistic DEA model is then developed to generate a vector of weights for the alternatives and we can derive reliable decision-making results. Finally, we provide a numerical example that can identify the most influential factor in Fog–haze weather to indicate the applicability of the 2-tuple linguistic decision-making method. The comparative analysis highlights the advantages and effectiveness of the 2-tuple linguistic decision-making method.

Nurse knowledge and attitudes towards organ donation and deemed consent: the Human Organ and Tissue Donation Act in Nova Scotia.

In April 2019, the Human Organ and Tissue Donation Act (HOTDA) in Nova Scotia was modified to incorporate a deemed consent model. In this study, we sought to understand intensive care unit (ICU) and emergency department (ED) nurses' knowledge of and confidence around organ donation and transplantation, experiences with organ donors and recipients, attitudes toward organ donation and deemed consent, and perceived opportunities and barriers to a deemed consent approach in view of the legislative change. We sent an electronic, self-administered survey to all ICU and ED nurses in Nova Scotia. The survey queried respondents on their knowledge of, experience with, and attitudes around organ donation and HOTDA, and opportunities and barriers to the implementation of HOTDA in clinical practice. Survey results were analyzed using descriptive statistics. One-hundred and ninety-four nurses responded to the survey. Nearly all (98%) supported organ donation, with 86% having signed an organ donor card to donate organs and/or tissues after death. A considerable majority (89%) also supported the new legislation. Nevertheless, a minority of respondents (13%) believed that deemed consent legislation would be considered a violation of the general principles of freedom and autonomy. The three most identified topics for ongoing training were coordination of the donation process (70%), clinical management of donors (70%), and family issues in decision-making (70%). Intensive care unit and ED nurses had positive attitudes toward organ donation, including deemed consent model. The findings should inform educational initiatives in Nova Scotia and beyond to optimize organ donation processes and outcomes.

Optimal decision-making considering inter-supply-chain competition and negative-spillover from environmental effort

Focusing on negative-spillover from environmental effort, this paper explores the issues of competition and optimal decision-making based on two competing two-stage manufacturer-dominated supply chains: centralized and decentralized. We developed the Stackelberg competition models according to four identified competition scenarios (pure centralized structure-CC, mixed structure-DC, mixed structure-CD, and pure decentralized structure-DD). By comparing the results of the four scenarios, we find that negative-spillover from environmental effort negatively impacts environmental-effort providers and reduces the optimal profit of the manufacturer providing the environmental effort, which in turn reduces the profit of the entire supply chain. The inter-supply-chain competition also produces a negative incentive for environmental-effort providers but provides a free riding effect on the non-provider, and this effect increases as competition increases. In terms of network externality, the structural change of a supply chain from centralized to decentralized is altruistic, which generates a double-marginalization (i.e., the network externality enhances competitor performance). Furthermore, the leader supply chain with a centralized structure is more willing to provide greater environmental effort, whereas when the leader supply chain structure remains unchanged, its environmental effort depends on the negative-spillover effect and the follower supply chain structure. Therefore, excessive supply chain competition should be avoided, and the negative-spillover effect of environmental effort should be reduced to motivate environmental-effort providers to increase their environmental efforts and promote the development of sustainable green supply chains. Future research should examine retailer-dominated supply chain competition and compare the results with the conclusions of this study.

Valuing live births from assisted reproduction: A health economics viewpoint.

Infertility is a medical condition that can be overcome, thanks to advances in medically assisted reproductive (MAR) therapies. Despite the ultimate measure of MAR efficacy being the birth of a new human being, there are ethical and methodological questions as to which outcome best translates the value of MAR in cost-effectiveness analyses. Many authors favour cost per life birth outcomes instead of more traditional cost per quality-adjusted life years (QALYs), which raises generalizability issues for decision-makers. Nonetheless, infertility and infertility treatments substantially differ from other health conditions and health treatments, particularly in the way they affect the quality of life of the infertile couple. Collecting quality of life measures in infertility and pregnancy is also surrounded by challenges not easily overcome. We reflect on cost-effectiveness methods applied to MAR technologies, on ethical considerations for valuing a MAR-generated life, and on its broader societal value for consideration by decision-makers.

Current State of Flooding and Water Quality of Nokoue Lake in Benin (Ouest Africa)

A thorough literature review was conducted in order to discover the previous studies that have been done in relation to the problems of flooding and water pollution of Nokoue lake (Benin, West Africa), to identify the limitations of these previous studies, to draw lessons in order to propose how to overcome them. This bibliographic synthesis has shown that previous studies related to the flooding issue have focused on the dynamics of understanding the genesis of flooding. These studies range from the oldest conducted in 1979 to the most recent conducted in 2016. The major results of these studies in relation to floods are hydrodynamic techniques allowing to evaluate in a punctual way the hydrological regime of the Nokoue lake. In other words, they are solutions allowing to reconstitute the floods generated by the river waters. It appeared that these results of flood reconstitution are difficult to generalize because of the complexity of the hydrographic network of Nokoue lake. The hydrodynamics of Nokoue lake is influenced differently by its main water tributaries (the Sô River, the Ouémé River, the Porto Novo Lagoon and the Atlantic Ocean). Therefore, a decision-making issue on planning and flood management must be motivated by a decision support tool. It is moreover recommended in these studies the resumption of the process of hydraulic techniques periodically during the period of high water in order to take advantage of the hydrological behavior of Nokoue lake in terms of overflow which are at the origin of the floods. The need for innovative decision-making solutions on flood planning and management in order to reduce disasters is still a priority for decision-makers. These innovative solutions are undoubtedly the flood forecasting. On the water quality of Nokoue lake, the oldest study conducted dates back to 1987 and the most recent one in 2018. In these studies, water quality is evaluated in two seasons: the low water season (December to mid-May) and the high-water season. It was found that the salinity of the lake increased significantly with an average of 16 ppt during the low water period while during the high-water period, freshwater inflows from the tributaries invaded the lake and prevented water from the Atlantic Ocean from entering. The concentrations of inorganic nutrients in the lake increase from low water to high water. However, the quantification to date of nutrient loads from external sources as well as internal sources to the lake have not been quantified. This implies that further research on the water quality of the lake could be conducted. Prevention actions should consider a predictive monitoring of the water quality index of Nokoue lake.

Cancer Therapy Assessment Accounting for Heterogeneity Using q-Rung Picture Fuzzy Dynamic Aggregation Approach

Due to the fact that there is no symmetry in the division of cancer cells, it is important to consider this asymmetrical behavior. Because of this heterogeneity during any therapy, not every cancer cell that is killed only is abolished, which is sensitive to the particular treatment chosen. Mathematical models that describe these pathways are critical for predicting cancer cell proliferation behavior. The literature on the mathematical modeling of cancer onset, growth, and metastasis is extensive. Both deterministic and stochastic factors were used to develop mathematical models to mimic the development rate of cancer cells. We focus on the cell’s heterogeneity in our model so that the cells generally responsible for spreading cancer, which are called stem cells, can be killed. Aggregation operators (AOs) play an important role in decision making, especially when there are several competing factors. A key issue in the case of uncertain data is to develop appropriate solutions for the aggregation process. We presented two novel Einstein AOs: q-rung picture fuzzy dynamic Einstein weighted averaging (q-RPFDEWA) operator and q-rung picture fuzzy dynamic Einstein weighted geometric (q-RPFDEWG) operator. Several enticing aspects of these AOs are thoroughly discussed. Furthermore, we provide a method for dealing with multi-period decision-making (MPDM) issues by applying optimal solutions. A numerical example is presented to explain how the recommended technique can be used in cancer therapy assessment. Authenticity analysis is also presented to demonstrate the efficacy of the proposed technique. The suggested AOs and decision-making methodologies are generally applicable in real-world multi-stage and dynamic decision analysis.

The vision of on-demand architectural knowledge systems as a decision-making companion

Cobbler’s children do not wear shoes. Software engineers build sophisticated software but we often cannot find the needed information and knowledge for ourselves. Issues are the amount of development information that can be captured, organizing that information to make them useable for other developers as well as human decision-making issues. Current architectural knowledge management systems cannot handle these issues properly. In this paper, we outline a research agenda for intelligent tools to support the knowledge management and decision making of architects. The research agenda consists of a vision and research challenges on the way to realize this vision. We call our vision on-demand architectural knowledge systems (ODAKS). Based on literature review, analysis, and synthesis of past research works, we derive our vision of ODAKS as decision-making companions to architects. ODAKS organize and provide relevant information and knowledge to the architect through an assistive conversation. ODAKS use probing to understand the architects’ goals and their questions, they suggest relevant knowledge and present reflective hints to mitigate human decision-making issues, such as cognitive bias, cognitive limitations, as well as design process aspects, such as problem-solution co-evolution and the balance between intuitive and rational decision-making. We present the main features of ODAKS, investigate current potential technologies for the implementation of ODAKS and discuss the main research challenges.

Up/down processing modes effects on residual stress for belt grinding

ABSTRACT Belt grinding is increasingly being used to process complex parts because of its ability to achieve flexible, form-following machining. Although the choice between down-grinding or up-grinding is one of the most common issues in process decisions, the influences of the differences in material removal mechanisms remain unclear, especially on surface residual stresses (RS). Thus, this paper focuses on the different grinding direction’s influence on residual stress distribution, followed by a causal analysis of the abrasive cutting mechanism. The results suggest that the up-grinding surface is more prone to compressive RS of σ a due to the different material flow directions close to the machined surface, and has stronger stress amplitudes of σ t due to the increased belt wear caused by the grits entering from the hardened machined surfaces. For both down- and up-grinding, σ a along the grinding trace’s cross-section is symmetrically distributed in a peaked pattern, but σ t is closer to a uniform distribution. One possible reason is that the contact wheel dissipates heat more quickly on both sides than in the center. Besides, the consistency of the RS distribution is better for up-grinding both within the same grinding trace and among different grinding traces.

Applicability of American College of Radiology Appropriateness Criteria Decision-Making Model for Acute Appendicitis Diagnosis in Children.

Acute appendicitis is one of the most common causes of abdominal pain in the emergency department and the most common surgical emergency reason for children younger than 15 years of age, which could be enormously dangerous when ruptured. The choice of radiological approach is very important for the diagnosis. In this way, unnecessary surgery is avoided. The aim of this study was to examine the validity of the American College of Radiology appropriateness criteria for radiological imaging in diagnosing acute appendicitis with multivariate decision criteria. In our study, pediatric patients who presented to the emergency department with abdominal pain were grouped according to the Appendicitis Inflammatory Response (AIR) score and the choice of radiological examinations was evaluated with fuzzy-based Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) and with the fuzzy-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model for the validation of the results. As a result of this study, non-contrast computed tomography (CT) was recommended as the first choice for patients with low AIR score (where Φnet=0.0733) and with high AIR scores (where Φnet=0.0702) while ultrasound (US) examination was ranked third in patients with high scores. While computed tomography is at the forefront with many criteria used in the study, it is still a remarkable practice that US examination is in the first place in daily routine. Even though there are studies showing the strengths of these tools, this study is unique in that it provides analytical ranking results for this complex decision-making issue and shows the strengths and weaknesses of each alternative for different scenarios, even considering vague information for the acute appendicitis diagnosis in children for different scenarios.

Quick intention identification of an enemy aerial target through information classification processing

Rapidly and accurately identifying the tactical intention of an enemy aerial target is an important issue for combat decision making. To this end, a quick intention identification model based on hybrid neural network is established in this paper. With available feature sequential measurements of the enemy target as inputs of the model, possibilities of different intentions are calculated timely. To increase the training efficiency and accuracy of recognition, the measurement information is processed using different neural network. Maneuvering data with large variations over time are processed using gated recurrent unit (GRU), while other data are processed using back propagation (BP) neural network. Besides, the fitting cubic sample interpolation is adopted to deal with incomplete information. Monte Carlo simulations demonstrate the robustness and accuracy of the established model, and training comparison with conventional models shows that the proposed method has higher training efficiency and better identification performance.

Decision-Making Dilemmas within Integrated Care Service Networks: A Systematic Literature Review.

The diverse nature of people's care needs requires collaboration between different organisations and sectors. One way of achieving such collaboration is through integrated care service networks. Decision-making is considered an important aspect of network governance and key to achieve further integration of care services. As integrated care scholars only implicitly seem to touch upon the issue of decision-making, we aimed to identify multiple decision-making dilemmas. A systematic literature review was conducted of eighteen empirical studies in which decision-making dilemmas in integrated care service networks were inductively identified. To frame and understand these dilemmas, we partly drew on Provan and Kenis' governance models and their hypothesised decision-making dilemma for service networks. Identified decision-making dilemmas included 1) autonomy versus interdependence, 2) diversity versus coherence, and 3) self-interest versus common goals. In line with Provan and Kenis' hypothesis, we highlight a cross-cutting dilemma of inclusiveness (all viewpoints are considered hence widely supported decisions) vs. efficiency (reaching timely decisions). We believe that network- and 'systemic' stakeholders both need to reflect upon and learn from decision-making dilemmas to work towards widely supported and adequate decisions. This is important for achieving aligned and holistic care services that many people desire.

Palliative Care Competencies in Nephrology: A Scoping Review

Introduction: There is a growing need to train nonpalliative physicians in palliative care, as it is increasingly recognized by the medical profession. Political and opinion leaders are also becoming aware of the urgent and growing need for palliative care education. Objective: The aim of this study is to provide an overview of the information available in the literature on the topic of palliative care competencies for nephrologists, using clearly defined and transparent methods to search, summarize, and interpret the relevant literature based on a systematic review approach. Methods: The scoping review is based on the Joanna Briggs Institute methodology. The search was conducted in December 2019 using publications in national and international databases and grey literature in English, Spanish, French and Portuguese. Results: Of the total 4668 publications, 168 were eligible for review based on title and abstract. A full-text review of these 168 publications resulted in the selection of 27 articles that met the predefined inclusion and exclusion criteria and were therefore included in the study. The following competencies were considered necessary to provide high quality PC in various nephrology settings: Communication skills (21.3%); End-of-life care, loss, grief, and bereavement (18.6%); Control of pain and other renal symptoms (16%); Advance care planning (16%); Ethical and legal issues in dialysis decision making (12.2%); Teamwork (6.6%); Ability to provide psychosocial and spiritual support to patients and families (5.6%). Application of palliative care principles (3.7%). Percentage refers to the number of publications addressing each topic. Conclusion: The implementation of an integrated care model that includes both PC and curative treatments places high demands on nephrologists, who must acquire PC competencies. Training in palliative care should be included in the curricula for nephrologists.

TOPSIS methods for probabilistic hesitant fuzzy multiple attribute group decision-making and application to performance evaluation of public charging service quality

The performance evaluation of public charging service quality is frequently viewed as the multiple attribute group decision-making (MAGDM) issue. In this paper, an extended TOPSIS model is established to provide a new means to solve the performance evaluation of public charging service quality. The TOPSIS method integrated with FUCOM method in probabilistic hesitant fuzzy circumstance is applied to rank the optional alternatives and a numerical example for performance evaluation of public charging service quality is used to test the newly proposed method’s practicability with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute. The main results of this paper: (1) A novel PHF-TOPSIS method is proposed; (2) The extended TOPSIS method is developed in the probabilistic hesitant fuzzy environment; (3) The FUCOM method is used to obtain the attribute weight; (4) The normalization process of the original data has adapted the latest method to verify the precision; (5) The built models and methods are useful for other selection issues and evaluation issues.

Social labs as temporary intermediary learning organizations to help implement complex normative policies. The case of Responsible Research and Innovation in European science governance

PurposeThis study aims to discuss science governance in Europe and the network of associated nonprofit institutions. The authors posit that this network, which comprises both (partial) learning organizations and non-learning organizations, has been observed to postpone taking up “responsibility” as an issue in science governance and funding decisions.Design/methodology/approachThis paper discusses the challenge of learning and policy implementation within the European science governance system. By exploring how learning on responsible innovation (RI) in this governance system can be provoked, it addresses the question how Senge’s insights in organizational learning can clarify discourses on and practices of RI and responsibility in research. This study explores the potential of a new organizational form, that of Social Labs, to support learning on Responsible Research and Innovation (RRI) in standing governance organizations.FindingsThis study concludes that Social Labs are a suitable format for enacting the five disciplines as identified by Senge, and a Social Lab may turn into a learning organization, be it a temporary one. Responsibility in research and innovation is conducive for learning in the setting of a Social Lab, and Social Labs act as intermediary organizations, which not merely pass on information among actors but also actively give substantive shape to what they convey from a practice-informed, normative orientation.Research limitations/implicationsThis empirical work on RRI-oriented Social Labs therefore suggests that Social Lab–oriented temporary, intermediary learning organizations present a promising form for implementing complex normative policies in a networked, nonhierarchical governance setting.Practical implicationsBased on this research funding and governance organizations in research, policy-makers in other domains may take up and create such intermediary organizations to aid learning in (science) governance.Social implicationsThis research suggests that RRI-oriented Social Labs present a promising form for implementing complex normative policies, thus integrate learning on and by responsible practices in various governance settings.Originality/valueEuropean science governance is characterized by a network of partial Learning Organization (LOs) and Non-Learning Organization (nLOs) who postpone decision-making on topics around “responsibility” and “solving societal challenges” or delegate authority to reviewers and individual actors, filtering possibilities for collaborative transformation toward RRI. social lab (SLs) are spaces that can address social problems or social challenges in an open, action-oriented and creative manner. As such, they may function as temporary, intermediary LOs bringing together diverse actors from a specific context to work on and learn about issues of science and society where standing organizations avoid doing so. Taken together, SLs may offer temporary organizational structures and spaces to move beyond top-down exercise of power or lack of real change to more open, deliberative and creative forms of sociopolitical coordination between multiple actors cutting across realms of state, practitioners of research and innovation and civil society. By taking the role of temporary LOs, they may support existing research and innovation organizations and research governance to become more flexible and adaptive.

Frédéric Patras.The Essence of Numbers

This book approaches the issue of the essence of numbers from a very broad perspective: historical, philosophical, and mathematical. The author is a practicing mathematician who has been working in philosophy since the 1990s, with a particular focus on phenomenology, while maintaining his activity in mathematics. As he explains in the preface to the English translation, the original French version began with a series of lectures introducing philosophy students at Nice to the philosophy of science and of mathematics via the philosophy of arithmetic. The initial aim was to develop in the students an understanding of the issues from the origins in classical Greek thought to the point where they would be ready to follow Husserl’s Philosophy of Arithmetic, which Patras considers ‘one of the deepest texts ever written on mathematical thought’. Each chapter is a manageable chunk of about ten pages, presumably covering what was given as a single lecture. Chapter 1 is an introduction, setting up the plan for the book. While our intuitive conception of numbers appears not to have changed much since classical Greece, the development within mathematics of the theory of numbers is quite extensive. The book aims to help resolve this tension by investigating the ‘essence’ of numbers, starting with classical Greece and following the development of the concept through to the modern era with its rigorous definitions. There is then a very brief overview: starting with the Greeks and the ‘problem of the One’ — is one a number? While civilizations before ancient Greece used numbers and calculation, the Greeks were the first to consider its definition. Number, and the study of the arithmetical properties of the continuum, have been a concern of mathematics throughout its history. Developments at the end of the nineteenth century, and especially Frege’s contributions bringing ‘arithmetic back to the pure laws of thought’, were central to the development of the concept of number, ‘a decisive issue for the whole theory of knowledge’. The algebraic nature of numbers and arithmetic led to the possibility of extending beyond the natural numbers.

Logistic autonomous vehicles assessment using decision support model under spherical fuzzy set integrated Choquet Integral approach

Autonomous vehicles (AVs) are the newest products in the intelligent transportation system that can move around with minimal human intervention. These products continue their path with all kinds of sensors with different parts. Effective use of these technologies in the logistics industry can create a competitive advantage. Nowadays, there are many AVs, some of which are superior to others in terms of build quality, variety of features, and design. Choosing an efficient, optimal, and reliable vehicle is one of the most important challenges in logistics planning. Therefore, choosing an AV based on a series of criteria can be considered a multi-criteria decision-making (MCDM) problem. Due to the complication of decision-making issues, criteria are usually not independent of each other and there are relationships between them. Therefore, this study develop an extended MCDM framework based on Choquet integral (CI) under group decision-making with a Spherical fuzzy set (SFS) for assessing logistics AVs. The CI technique is expanded with SFS to increase the power of CI. Furthermore, the combination of CI with SFS leads to greater freedom for decision makers to express opinions and use three independent membership functions. Accordingly, the interactions between the criteria are considered and the skepticism and uncertainty present during the decision are controlled. The proposed approach is implemented in selecting the best AVs in the logistics industry, and the results are compared with Pythagorean fuzzy CI and Intuitionistic fuzzy CI. Moreover, sensitivity analysis is done by changing the weights and creating different scenarios to confirm and check the robustness of the proposed approach results. The results indicate the suggested approach's efficiency and the ranking's stability in different scenarios.