Attribute Decision Research Articles

A novel grey relational clustering model under sequential three-way decision framework

In the context of addressing clustering problems with small samples, grey relational clustering (GRC) plays a crucial role. Currently, a three-way GRC has gained popularity, particularly for its ability to handle the uncertain relationship between objects and classes. However, explicit clustering for some objects may not be achievable when employing the one-step decision, which is often a coarse-grained approach. In such cases, re-clustering these objects with a finer granularity becomes necessary. This is where the sequential three-way decision (STWD) proves to be an effective solution. Therefore, this paper is dedicated to designing a novel GRC under the STWD framework, namely GRC-STWD. Specifically, recognizing that some existing threshold calculations in GRCs can be overly subjective and that a pair-wise global threshold may not efficiently leverage available information for accurate object clustering, we introduce a novel threshold algorithm to address these limitations. Furthermore, we propose a conditional probability calculation method combining TOPSIS and grey relational degrees for clustering data lacking decision attributes or class labels. As the development of GRC within portfolio environments remains relatively nascent, we apply the proposed model to portfolio strategy construction. Finally, comparative and experimental analyses validate the model's effectiveness and feasibility. Notably, the developed model serves as a generalization of GRC, contributing to the broader advancement of GRC and STWD methodologies.

Feature selection for hybrid information systems based on fuzzy β covering and fuzzy evidence theory

Feature selection can remove data noise and redundancy and reduce computational complexity, which is vital for machine learning. Because the difference between nominal attribute values is difficult to measure, feature selection for hybrid information systems faces challenges. In addition, many existing feature selection methods are susceptible to noise, such as Fisher, LASSO, random forest, mutual information, rough-set-based methods, etc. This paper proposes some techniques that consider the above problems from the perspective of fuzzy evidence theory. Firstly, a new distance incorporating decision attributes is defined, and then a relation between fuzzy evidence theory and fuzzy β covering with an anti-noise mechanism is established. Based on fuzzy belief and fuzzy plausibility, two robust feature selection algorithms for hybrid data are proposed in this framework. Experiments on 10 datasets of various types have shown that the proposed algorithms achieved the highest classification accuracy 11 times out of 20 experiments, significantly surpassing the performance of the other 6 state-of-the-art algorithms, achieved dimension reduction of 84.13% on seven UCI datasets and 99.90% on three large-scale gene datasets, and have a noise tolerance that is at least 6% higher than the other 6 state-of-the-art algorithms. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability.

Research on peak load shifting for hybrid energy system with wind power and energy storage based on situation awareness

The uncertainty of wind power and load fluctuations can elevate the peaking pressure on the power grid and influence the optimization strategy for peak load shifting. Additionally, there is a need to explore the trade-off and dynamic adjustment between economic considerations and the effectiveness of peak load shifting strategies. In this paper, based on the situation awareness theory, an optimization model on peak load shifting is proposed for a hybrid energy system with wind power and energy storage unit. First, in the situation perception stage, simulations that incorporate stochastic volatility are executed for renewable energy outputs and electric loads, leading to the establishment of uncertainty models for both wind power and electric load. During the situation comprehension stage, an optimization model with multi-objective is proposed to simulate the hybrid system situation, in which the mutual constraints and games are considered between peak load shifting effects and system economy. Then, in the stage of situation presentation and situation orientation, by setting quantitative indicators to analyze and judge the system situation, the problem of changing uncertainty from the weights of various attributes in multi-objective decision-making is transformed into the prediction problem of the adjustable factor, and a hybrid network model is proposed to realize the interval prediction of the adjustable factor, further balancing the goals of load leveling and system economy. Example simulations demonstrate that the proposed optimization model for peak load shifting can effectively reduce the peak-valley difference ratio of the net load by over 39.08 %, thereby smoothing the overall net load fluctuation. In the face of uncertainty, the model shows minimal impact on the peak-valley difference ratio of the net load, experiencing only 3.91 % and 7.13 % changes in response to wind power and load fluctuations, respectively. These findings underscore the model's sensitivity to different levels of uncertainty in both wind power and load scenarios. The MAML-LSTM-FC model can clarify the selection range of the adjustable factor according to the requirements of system quantitative indexes, thereby achieving dynamic coordination between the effects of peak load shifting and economics.

Application of the Multi-Objective Optimization By Ratio Analysis (MOORA) Method in the Chili Seed Selection System (Case Study of Asuli Village, East Luwu Towuti District)

Currently chili plants are plants that are needed by the community, these plants can be processed into cooking spices, the food industry and medicines. In East Luwu Regency chili plants are also the main commodity in agriculture, many farmers make chili plants as a business opportunity, but the production of chili plants in East Luwu Regency has decreased, this decrease in production is due to the lack of yields of chili farmers. So that the supply of chili is reduced and can not meet market demand. The lack of yields was caused by farmers using different types of chili seeds because farmers still had difficulty in determining the right type of chili seeds to develop which resulted in chili cultivation failing to harvest. For this reason, a system is needed that can assist farmers in determining chili seeds that are suitable for cultivation in Asuli Village, East Towuti Luwu District. The criteria used in determining the location of the business are harvest time, number of stalks, weight of chilies, altitude and longevity. This study aims to produce a decision support system in recommending the best chili seeds so that they can help farmers increase red chili production among farmers. The method used is the MOORA method, this method was chosen because this method is very simple, stable, and robust, even this method is able to determine goals from conflicting criteria, where criteria can be of beneficial or unfavorable value (cost). In addition, MOORA also has the ability to easily separate subjective elements from an evaluation process into weighted decision criteria that have several decision-making attributes. The results showed that Salo Dua chili seeds were suitable for cultivation in Asuli Village, East Luwu Towuti District

Attribute reduction algorithms with an anti-noise mechanism for hybrid data based on fuzzy evidence theory

Attribute reduction can remove data noise and redundancy, thus reducing computational complexity, which is very important for machine learning. Because the difference between nominal attribute values is difficult to measure, attribute reduction for hybrid data faces challenges. In addition, most of the existing methods are sensitive to noise due to the lack of an anti-noise mechanism. Decision attribute contains the most important information of data. This paper proposes some techniques that consider the above problems from the perspective of fuzzy evidence theory. First of all, a new distance incorporating decision attributes is defined, and then a new fuzzy relation with an anti-noise mechanism is defined. Furthermore, fuzzy belief and fuzzy plausibility are defined based on the defined new distance and new fuzzy relation. In this framework, two anti-noise attribute reduction algorithms for hybrid data are proposed. Experiments on 12 data sets of various types show that compared with the other 8 state-of-the-art algorithms, the proposed algorithms improve the classification accuracy by at least 2% and the anti-noise ability by at least 11%. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability.

An integrated three-way decision methodology for sustainability of wastewater circularity in thermal power plants

The presence of multiple criteria for evaluating wastewater reuse applications indicates the potential usage of Multi-Criteria Decision-Making (MCDM) methods for this purpose. However, there is currently a scarcity of studies in the domain literature that utilize MCDM approaches in this application topic. This paper therefore advances the domain literature in two distinctive ways. Firstly, it analyzes and advances the reuse agenda of wastewater from thermal power plants, recognized as large-scale users of water, thus promoting greater water circularity. Secondly, it provides a methodological advance by integrating the notion of Three-Way Decision (3WD) into the computational structure of MCDM methods by introducing a middle reference point. Such an initiative results in a novel 3WD extension of the Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) method. Additionally, this work provides a proof that the MARCOS method utilizes a compromise solution in identifying priority alternatives, along with the integration of a Weighted Aggregated Sum Product ASsessment (WASPAS) metric. An initial hypothetical example illustrates how the proposed approach augments the canonical MARCOS method, particularly in promoting the “thinking in threes” as a more natural information processing approach and the high degree of distinguishability of priorities between decision alternatives. An actual case study in a thermal power plant then demonstrates the contributions of this work. With the best-worst method used to determine the priorities of the decision attributes, the findings reveal that wastewater reuse applications achieving reduced costs for needed infrastructures, operational simplicity, technological compatibility, consumer safety and household savings are preferred by stakeholders. The 3WD-MARCOS approach identifies industrial and commercial use, municipal use, environmental restoration, and household use as the high-priority alternatives, with cooking and drinking as least preferred. These insights guide stakeholders in their design of initiatives that allocate resources for greater wastewater reuse. A comparative analysis yields high consistency of these findings with similar MCDM methods. In addition, the efficacy of the novel 3WD-MARCOS method highlights its potential in handling MCDM problems, including those promoting water circularity.

Nature of decision valuations in elimination of redundant attributes

Information systems are the basic building blocks of the theory of rough sets which, based on information signatures of objects, develops strategies for classifying concepts and/or deriving significant information about decision attributes. During such processes of aggregating decision information, one needs to focus on the aspects of designing its representation and considering various ways for reducing attributes. We introduce a general mathematical apparatus of decision valuations that are aimed at representing information derivable from data. We establish a strong connection between the notion of attribute reduction considered in the context of decision valuations and the analogous notions developed in relational databases, semigraphoid models of conditional independence statements, etc. Based on different decision making strategies, we discuss different properties of decision valuations and explore different examples in the light of those properties. We also investigate interrelationships among those properties. Going back to attribute reduction, i.e. elimination of redundant attributes, we consider the prevalent property of discernibility and compare it with the properties of semigraphoid models, such as weak union and decomposition.

A novel method for generating a canonical basis for decision implications based on object-induced three-way operators

Three-way concept analysis can express not only the information that the objects of study have in common but also the information that they do not have in common, which is an extension of classical formal concept analysis. The decision implication is a special representation of the implication in the decision formal context, which reveals the dependency between conditional attributes and decision attributes. This paper is devoted to the study of a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). First, the decision implication logic based on OE operators is described from the two aspects of semantics and syntax, which is applied to the decision formal context. Then, a method of obtaining the canonical basis for decision implications based on decision the premise of the OE operators is given, and the completeness, the nonredundancy and the optimality of the canonical basis are proven. Finally, a method of generating true premises based on OE operators is proposed by means of an incremental method, and the canonical basis for decision implications is then generated by the true premises based on OE operators.

Probabilistic Interval Ordering Prioritized Averaging Operator and Its Application in Bank Investment Decision Making

Probabilistic interval ordering, as a helpful tool for expressing positive and negative information, can effectively address multi-attribute decision-making (MADM) problems in reality. However, when dealing with a significant number of decision-makers and decision attributes, the priority relationships between different attributes and their relative importance are often neglected, resulting in deviations in decision outcomes. Therefore, this paper combines probability interval ordering, the prioritized aggregation (PA) operator, and the Gauss–Legendre algorithm to address the MADM problem with prioritized attributes. First, considering the significance of interval priority ordering and the distribution characteristics of attribute priority, the paper introduces probability interval ordering elements that incorporate attribute priority, and it proposes the probabilistic interval ordering prioritized averaging (PIOPA) operator. Then, the probabilistic interval ordering Gauss–Legendre prioritized averaging operator (PIOGPA) is defined based on the Gauss–Legendre algorithm, and various excellent properties of this operator are explored. This operator considers the priority relationships between attributes and their importance level, making it more capable of handling uncertainty. Finally, a new MADM method is constructed based on the PIOGPA operator using probability intervals and employs the arithmetic–geometric mean (AGM) algorithm to compute the weight of each attribute. The feasibility and soundness of the proposed method are confirmed through a numerical example and comparative analysis. The MADM method introduced in this paper assigns higher weights to higher-priority attributes to establish fixed attribute weights, and it reduces the impact of other attributes on decision-making results. It also utilizes the Gauss AGM algorithm to streamline the computational complexity and enhance the decision-making effectiveness.

Site suitability analysis using MacBeth and COPRAS in a case study of the cement industry under SCM implementation

In the past two years, 2020-2022, the developing construction industry has been a huge issue according to the negative effect of Covid-19 with the increasing pandemic situation in cities and areas. In Covid-19 pandemic situation, the cement manufacturing industry has been crucial and needed more scrutiny. As cement is the second significant component after water in concrete and construction industry. Meanwhile, locating a cement plant in a special area of the city is challenging and affecting more by local communities and other involved environmental factors. The location selection decisions need to grow by environmental, economic, technical and social attributes. This study aims to present the site suitability decisions through a case study of locating a new manufacturing plant for cement production in Tehran surrounding, Iran. In this process, some required technical and tactical criteria are deserved for evaluating and suitability of the plant through decision-makers for cement manufacturing. All the feasible industrial alternative locations were evaluated under various criteria and regarding the Covid-19 pandemic’s negative impact to identify the most appropriate location for the cement industry. The authors proposed two Multi-Criteria Decision Attributes (MCDA) methods of MacBeth and COmplex PRoportional ASsessment (COPRAS) to evaluate and select the most suitable location for site suitability of the cement plant in this problem. Though the MacBeth method does not need to calculate weights of the Geographical Information System (GIS) criteria, the COPRAS method determined and used BWM (Best-Worst Method) as the weighing method. In sum, the comparison of the two methods was obtained according to the given results and ranks of volunteer cement suppliers for site suitability of the cement plant.

Rough set approach to predict biochar stability and pH from pyrolysis conditions and feedstock characteristics

Biochar has gained increasing attention as a potential adsorbent for the sequestration of carbon dioxide (CO2) and enhancement of soil fertility. Biochar pH and stability are important adsorbent properties as they indicate the affinity of biochar for CO2 and its potential to be applied in soil for long periods. These attributes are influenced by the feedstock composition and pyrolysis conditions. Therefore, it is important to develop a model that can elucidate the underlying trends and inherent relationships between feedstock composition and pyrolysis operating parameters on biochar pH and stability. In this work, rough set machine learning (RSML) tools have been used to develop a model to quantify this relationship because of the interpretable nature of RSML. RSML is a rule-based prediction model that categorizes the biochar properties by utilizing ‘if. then’ rules to conditional attributes. In this study, the feedstock properties such as elemental (carbon, hydrogen, oxygen and nitrogen) composition, fixed carbon, ash content, volatile matter, and operating conditions such as residence time, heating rate, and temperature were the conditional attributes while pH, ash content and O/C ratio of biochar were considered the decision attributes. The rules generated from RSML were validated and evaluated for scientific coherency. Thus, this approach provided a model which could reflect on the physical phenomena at varying process conditions. As a result, it was recommended that the pyrolysis temperature is in between 375 and 475 °C, the ash content in the feedstock is between 2.59 and 3.55 wt% and volatile matter in the feedstock is in between 68.9 and 73.8 wt% to obtain biochar with minimum ash content (0–5 wt%), minimum O/C ratio (0–0.2), and high pH (9−11).

Robust Multiobjective Decision Making in the Acquisition of Energy Assets

In asset management for energy portfolios, quantitative methodologies are typically employed. In Brazil, the NEWAVE computational model is universally used to generate scenarios of hydraulic production and future prices, which result in revenue distributions. These distributions are then used to estimate the portfolio’s revenue and assess its risk. Although this is a well-established analysis, it has some shortcomings that are not always considered. The validity of the revenue series constructed by NEWAVE, especially in long-term analysis, is a real problem for agents concerning the acquisition of assets such as power plants. Another issue is the disregard for other objectives that are important for the operationality of the management task and are often ignored, such as operational risk. To address these limitations, this work combines the areas of multicriteria decision making under uncertainty and risk management and presents a methodology for evaluating the acquisition of long-term energy assets, as well as a practical application of the proposed method. Investment alternatives are evaluated in multiple developed scenarios, so it is possible to measure how robust a given option is. By analyzing several scenarios simultaneously, a larger region of uncertainties can be covered, and therefore, decision making becomes more secure. The proposed methodology includes six objectives, designed to address a wider range of stakeholder needs. This approach is applied to an illustrative portfolio, producing results that allow for a more comprehensive understanding of decision attributes. Therefore, this work not only addresses the current limitations in the field but also adds an original contribution by considering simultaneously several scenarios and integrating multiple objectives in a robust and secure decision-making framework.

No Simple Way to Say Goodbye! Untangling the Heterogeneity of Social Venture Founder Exit Intention

Using three theory-based performance criteria as decision attributes, we conducted a conjoint analysis experiment with 105 social venture founder-CEOs to examine their decisions to exit their firms voluntarily. Multilevel regression analysis of founders’ choices revealed that various exit preferences were chosen that did not support theoretical prescriptions. While achieving desired social impact was the main influence on founder exit choices, the heterogeneity of exit preferences led us to parcel them into four distinct groups: idealists, traditionalists, realists, and pragmatists. We discuss our contributions to the entrepreneurial exit and social entrepreneurship research literature and list the implications of our results for practice.

Hierarchical Q-learning-enabled neutrosophic AHP scheme in candidate relay set size adaption in vehicular networks

Cooperative candidate relay sets (CRS) in opportunistic broadcast paradigms can significantly improve the performance efficiency of traditional broadcast schemes by simultaneously scheduling multiple vehicles in the data packet transmission process. Nonetheless, multiple relay synchronization may cause enormous data redundancy and high delays in data delivery, resulting in a high cooperative transmission cost. High vehicle velocities and vehicular topology restrictions, including intersections, exacerbate the cooperative transmission cost. Motivated by a trade-off between delay and delivery ratio via reducing duplicate data packets and redundant relays, we address this gap in this paper. To this end, we propose an adaptive candidate relay set (ACRS) size scheme based on the Neutrosophic Set Analytic Hierarchy Process (NS-AHP) technique assisted by a Q-learning algorithm. The NS-AHP pair-wise comparison matrix includes; redundancy, cooperative-based efficiency, and relay set stability as the decision-making attributes. Meanwhile, we use a hierarchical Q-learning approach based on the time-varying vehicular environment to adjust the relative preference of the attributes and alternatives (i.e., various CRS sizes) over each other. To optimally learn the relative preference values of the attributes and alternatives over each other, reward functions are set dynamically at both levels. Comprehensive simulations via NS-2 and SUMO are carried out to assess the efficacy of our ACRS scheme compared to three commonly used baseline techniques of fixed size, threshold-based, and probability-based techniques. Overall, the average yields obtained from the experimental outcomes in the urban and highway scenarios show that the ACRS scheme, on average, improves the packet delivery ratio by 3.77%, 5.36%, and 1.35%; reduces the single-hop delay by 34.78%, 42.085%, and 19.39%; reduces the end-to-end delay by 34.76%, 42.72%, and 21.35%; improves the transmission throughput by 34.24%, 41.54%, and 19.305%; and reduces the redundancy ratio by 43.69%, 50.66%, and 24.32% compared to the fixed size, threshold-based, and probability-based techniques, respectively.

IoT-based generalized multi-granulation sequential three-way decisions

In the context of the Internet of Things (IoT), decision models and GRC (Governance, Risk, and Compliance) can be used for data processing and decision-making processes. The IoT involves a large number of sensors and devices, some of which enable “device-free” functionality, meaning that specific devices or sensors are not required to obtain the necessary information. This technology utilizes the signals present in the environment to determine the location of individuals without them carrying any specific devices. By incorporating “device-free” sensor data into decision models, we can process IoT data more efficiently and achieve accurate location positioning. The prevailing hierarchical sequential three-way decision models (STDM) follow a common pattern of constructing a hierarchical tree of conditional attributes to acquire knowledge. However, these models fail to meet the diverse needs of users when it comes to rule mining complex hierarchical data with multiple levels and views in real-world applications. Additionally, the pursuit of simplified rules remains a significant topic of interest. To address these challenges, we propose a novel approach that integrates multi-granularity, device-free localization, and STDM. By leveraging IoT and device-free localization, we collect data from various devices and sensors to accurately determine the positions of individuals. Using this data, we construct a generalized hierarchical decision table by creating a concept hierarchical tree with decision attributes. This framework offers a comprehensive solution for overcoming the challenges of device-free localization and IoT. Subsequently, we establish a granular structure by partitioning the complete set of attributes. In this paper, our proposed approach improves the efficiency and accuracy of data processing in machine learning models and enables users to better process and understand complex datasets. Finally, the performance is experimentally validated for the models in this paper. Such models greatly enrich the theoretical framework of three-way decision making. Overall, the abstract provides a concise and succinct overview, outlining the motivation behind the research and presenting the contributions made.

Hubungan Kematangan Emosi dalam Pengambilan Keputusan Karir Siswa

The purpose of this study was to determine the relationship between emotional maturity in decision making, the level of emotional maturity and decision making. The hypothesis of this research is that there is a positive relationship between emotional maturity and decision making. The subjects of this study were 119 students of SMK Negeri 9 Padang. The sampling technique used in this research is simple random sampling. The method uses a quantitative approach with measuring tools of emotional maturity scale and decision making scale. The data analysis technique used in this research is non-test using a psychological scale. Meanwhile, the tools used to collect data are emotional maturity scale and career decision-making scale which were developed based on theoretical studies. The scale contains several statements that aim to determine the psychological attributes of the respondents, namely the attributes of emotional maturity and career decision-making attributes. The scale used in this study is the Likert scale model. Based on the results of data analysis obtained a correlation coefficient of 0.878 with a significance level of 0.000 compared with a probability of 0.05 then 0.000 <0.05. The analysis was carried out using SPSS 20

Penerapan Metode Multi Objective Optimization on the Basis of Ratio Analysis (MOORA) dalam Penerimaan Siswa Baru

Acceptance of new students is a school activity that occurs every year, even in the middle of the teaching year. This activity is the first step to improve the quality of education, namely choosing the quality of students. In general, the process of admitting new students is still done manually, namely by recording student data and the student selection exam schedule to the student data that has been accepted. This is one of the problems every year that can hinder the work of teachers and even school staff. Therefore, we need the right system to be used in schools so that it can assist in the acceptance of new students. One system that is able to solve these problems is the Decision Support System (SPK). . SPK aims to provide information, guide, provide predictions and direct users of information so they can make better decisions. In implementing the SPK, ranking methods are used so that the results obtained are more effective, such as the Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) Method. The Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method where it is expected that the normalization process in calculating the value of new student admissions. The Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method is a method which has a calculation level using minimum calculations and is very simple and practical for applied to the system. The Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method has a high level of flexibility and convenience on separating the subjective part that comes from an assessment process into the decision weighting criteria using several decision-making attributes. The final result showed that Angga Rifai Zul Fiqri ranked first with a final preference score of 1.0522, which was the highest among all candidates.

Interval-valued fuzzy discernibility pair approach for attribute reduction in incomplete interval-valued information systems

Interval-valued information systems provide rich semantic interpretation and greater flexibility compared to real-valued information systems. Meanwhile, incomplete interval-valued information systems containing missing values exist widely in reality. One of the key issues in handling incomplete interval-valued information systems is attribute reduction. Unfortunately, there are few studies on attribute reduction for incomplete interval-valued information systems. Moreover, most of the existing studies use fuzzy similarity relations to measure the similarity of objects. Due to the complexity of incomplete interval-valued information systems, traditional fuzzy similarity relations cannot adequately represent the information contained within incomplete interval-valued information systems. To address this issue, this study proposes interval-valued fuzzy min-max similarity relations for incomplete interval-valued information systems, along with two attribute reduction algorithms based on interval-valued fuzzy discernibility pairs model for incomplete interval-valued information systems. Firstly, this study defines interval-valued fuzzy min-max similarity relations using distance-based extended restricted equivalence functions. Secondly, this study presents the interval-valued fuzzy discernibility pairs and discerning ability measure for attribute sets. Furthermore, an attribute reduction algorithm is proposed for incomplete interval-valued information systems. Thirdly, taking into account decision attributes, the relative interval-valued fuzzy discernibility pairs and an attribute reduction algorithm for incomplete interval-valued decision information systems are proposed. Finally, the effectiveness of the proposed method is compared to four representative attribute reduction algorithms in experiments. The experimental results illustrate that our method is effective and has advantages over the compared algorithms.

Several similarity measures of probabilistic dual hesitant fuzzy sets and their applications to new energy vehicle charging station location

Probabilistic dual hesitant fuzzy set (PDHFS) can better reflect the hesitant attitude and probability information of decision-makers than existing fuzzy sets. Therefore, it is a more powerful and important tool to express and deal with uncertain information. The score function of fuzzy number plays a very important role in comparing two fuzzy numbers. Entropy measure is a very effective tool for weighting decision attributes in multi-attribute group decision-making problems. Firstly, depending on the detailed analysis of the already existed score function of probabilistic dual hesitant fuzzy element (PDHFE), we built a novel score function for it, it is more effective and convenient when comparing two elements. Secondly, probabilistic dual hesitant fuzzy entropy was built to weight decision attributes. Thirdly, a combined weighting method was proposed which can reflect both subjective and objective information for decision attribute weighting based on the principle of minimum discriminant information. Fourthly, we extended the classical Dice and Jaccard similarity measures to the probabilistic dual hesitant fuzzy environment, and an all-new multi-attribute group decision-making technique to solve the problem that the decision attribute information is probabilistic dual hesitant fuzzy element. Finally, the applicability and effectiveness of this technique are proved by numerical examples and comparative analysis.

From purchase to pantry – exploring archetypes and strategies in the context of e-grocery fulfilment

ABSTRACT Fulfilment is a key driver for the digitalisation of the grocery sector and thus a major source of value creation. Although value creation is contingent on a multiplicity of contextual factors, extant research has mainly explored fulfilment in the generic realms of e-commerce and does not provide empirically grounded insights into the collective interplay of fulfilment attributes. Because e-grocery features unique exigencies and trade-offs that require non-generic theorisation, a functional and cross-domain perspective is required to comprehend the role of fulfilment in creating value in this sector. Following a multi-method approach, this paper combines a literature review with an empirical investigation of 111 e-grocers to produce three main outcomes. First, a conceptually-grounded and empirically-refined taxonomy to capture important decision attributes of e-grocery fulfilment. Second, six archetypes to indicate common combinations of these attributes. Third, a contingency model to disclose value relationships between attributes and outcome performance. Our results contribute to the body of knowledge on e-grocery value creation as well as the explanatory understanding of operational configurations and digital transformation conditions in this industry.