Entropy Weight Approach Research Articles

How Does Agro-Tourism Integration Influence the Rebound Effect of China’s Agricultural Eco-Efficiency? An Economic Development Perspective

Agro-tourism integration is a breakthrough to flourish rural industries and fulfill rural revitalization. Also, agricultural eco-efficiency and agro-tourism integration are closely linked, so investigating the relationship between the two is significant for realizing high-quality agro-ecological development in China. However, existing studies have ignored the impact of agro-tourism integration on agricultural eco-efficiency. For this purpose, using a dataset of 30 provincial administrative regions in China from 2001 to 2019, this paper employs the Entropy weight approach and super efficient Slack-Based Measure (SBM`) approach to measure the agro-tourism integration level and agricultural eco-efficiency, respectively. The system Generalized Method of Moments (SYS-GMM) approach is applied to investigate the effect of agro-tourism integration on agricultural eco-efficiency. The statistical results reveal that agro-tourism integration significantly contributes to agricultural eco-efficiency, which remains valid after the robustness checks are executed. There is also significant path-dependence of agro-tourism integration. Finally, agro-tourism integration significantly contributes to agricultural eco-efficiency in the eastern region, while it significantly inhibits agricultural eco-efficiency in the central-western region. Our findings suggest that policymakers not only reinforced the deep integration of agriculture and tourism to stimulate the overall rural revitalization, but also formulated agro-tourism integration policies in a differentiated and green manner to contribute to agricultural eco-efficiency growth.

Properties analysis of refugee populations around the world: A visibility graph network approach

The refugee problem is one of the most important issues facing the international community today. It not only troubles the countries where refugees are generated but also has a great impact on the countries where refugees are influx. With the continuous development of globalization, the refugee problem is no longer a problem of a country or a region, but a global problem faced by the international community. To cope with the global refugee problem, this paper analyzes the number of refugees in 156 countries from 1990 to 2020 and transforms the refugee population data of these countries into a complex network through a time series visibility graph (VG) method. First, we categorize the income level of 156 countries and analyze the impact of income level on the increase of refugee numbers. Then, the evaluation index of the number of refugees is obtained through the VG method. Finally, a TOPSIS comprehensive evaluation method based on the entropy weight approach is employed to analyze the data. This paper includes two main contributions. First, the application of the VG method provides a new perspective for enriching the modeling of the global refugee population growth trend. Second, this paper shows that the TOPSIS evaluation method based on the entropy weight method is effective, which provides a new method for further research on the global refugee population growth trend.

The dynamic coupling nexus among inclusive green growth: a case study in Anhui province, China.

Inclusive green growth (IGG) offers an effective alternative to pursue sustainable development. The core of the IGG system lies in the coordination of inclusive, green, and growth subsystems. However, there is little quantitative assessment on IGG based on subsystem collaboration. This study proposes a holistic scheme of inclusive-green-growth nexus in Anhui province from 2009 to 2018 by using an integrated approach, namely, the entropy weight approach (EWA), coupling coordination degree model (CCDM), grey prediction model (GPM), and obstacle factor diagnostic model (OFDM). The results show that: (1) The proposed integrated approach could be viable to measure the synergistic interactions among internal IGG subsystems; (2) At the provincial level, a relatively high IGG performance but a low coupling coordination degree (CCD) of the IGG nexus are seen. Although the predicted value of CCD will show an upward trend, it will not be able to cross the start stage. The obstacle factors on the coordinated development of IGG can be divided into two stages: (3) At the prefectural level, the cities in which CCD is rising outnumber those it is falling. However, the CCD is also low, and the gap between cities is getting wider. The obstacles that affect the CCD of cities see a dynamic evolution trend from “inclusive obstacle type” to “inclusive and growth obstacle type” then to “green obstacle type” over the decade.

Assessment of groundwater sustainability and management plan formulations through the integration of hydrogeological, environmental, social, economic and policy indices

To protect aquifers, it is necessary to assess their sustainability in the face of current and future events and stresses. In this study, technical, environmental, social, economic, and groundwater sustainability policy indexes were integrated and used to assess the sustainability of aquifers in the Salt Lake catchment. This 92,884.2 km2 catchment, one of the most important second-grade watersheds in Iran in terms of the importance of water resources, is located in Tehran, Markazi, Qazvin, Zanjan, Hamedan, Isfahan, Qom, and Alborz provinces, and comprises 36 aquifers in the basin. Seven quantitative hydrogeological indicators, three qualitative hydrogeology indicators, six environmental indicators, three social indicators, two economic indicators, and one policy indicator were considered. Final sustainability in each dimension (hydrogeological, environmental, social, economic, and policy) and overall sustainability were evaluated with the analytical hierarchy process and Shannon information entropy weighting approach to integrate expert opinion. The final results of sustainability analysis show that in the Salt Lake catchment, eastern aquifers are completely unstable and western aquifers show poor stability. Management scenarios with a 40% and 80% reduction in groundwater abstraction in the framework of the driving force-pressure-status-impact-response model show that a 40% reduction plays no a significant role in improving the quantitative sustainability of aquifers, whereas an 80% reduction in abstraction leads to an improvement of about 25% in quantitative sustainability.

A NOVEL DECISION-MAKING FRAMEWORK BASED ON PROBABILISTIC LINGUISTIC TERM SET FOR SELECTING SUSTAINABLE SUPPLIER CONSIDERING SOCIAL CREDIT

Sustainable supplier selection (SSS) has become an essential task for decision-makers in competitive environments. We construct a new decision-making framework for SSS. First, classical SSS usually includes fixed factors in environmental, social and economic dimensions. Differently, we adopt new social factors from credit perspective with corporate social credit system being promoted vigorously by the Chinese government. Next, we employ probabilistic linguistic term sets (PLTSs) to collect experts’ judgments about interactive influence between factors. Third, we combine PLTSs with Decision Making Trial and Evaluation Laboratory (DEMATEL) method to identify critical success factors (CSFs) for improving decision-making efficiency. And we also give definition to relative importance degree, standard relative importance degree, deviation of importance degree and influence degree to reflect the interactive influence between factors. To eliminate subjective influence, we combine entropy weighting approach and DEMATEL to compute weights. Fourthly, we redefine dominance degree and apply it into TODIM method for SSS. Finally, the proposed decision-making framework’s effectiveness is verified by using the case study of a new energy vehicle (NEV) company. Based on this, sensitivity analysis and comparison of methods are conducted. The results verify that the decision-making framework is valid and effective for SSS.

An analytical approach for big social data analysis for customer decision-making in eco-friendly hotels

Sustainable tourism is an emerging trend around the world. Eco-friendly (green) hotels are environmentally friendly properties that are becoming more popular among green travellers. Electronic Word-of-Mouth (e-WOM) is a method of communicating with customers to share their experiences and is a powerful marketing tool for hotel marketing. This paper investigates the role of online reviews of eco-friendly hotels for preference learning using multi-criteria decision-making and machine learning techniques. We develop a new method using multi-criteria decision making, supervised and unsupervised learning techniques. The Expectation-Maximization (EM) algorithm is used as an unsupervised learning technique to cluster travellers’ online reviews. We use the Higher-Order Singular-Value Decomposition technique along with a similarity measure to find the most similar customers based on their preference. To predict travellers’ preference for eco-friendly hotels, we employ a neuro-fuzzy system, the Adaptive Neuro-Fuzzy Inference System, as a supervised learning technique. To select the most important criteria, we use the entropy-weight approach in each segment. Several experiments were performed on the collected data from the Czech Republic's eco-friendly hotels on the TripAdvisor platform. The results demonstrated that the hybrid approach is effective for customers’ segmentation, and preference learning and prediction in eco-friendly hotels.

The impact of nature documentaries on public environmental preferences and willingness to pay: entropy balancing and the blue planet II effect

In this study, the discrete choice experiment approach was employed in a survey of the Scottish general public to analyze how respondents make tradeoffs between blue growth potential and marine ecosystem service delivery associated with the Mingulay cold water reef complex. Results indicate a higher willingness to pay for management options associated with the highest possible levels of marine litter control followed by the highest possible levels of fish health. Using entropy balancing, a multivariate reweighting method to produce balanced samples in observational studies, we also test the impact that having watched the BBC Blue Planet II documentary series may have had on individuals’ willingness to support marine conservation activity. Whether or not respondents had seen the BBC Blue Planet II series was found to have a significant impact on people’s preferences. Despite this, the willingness to pay (WTP) does not differ between the two groups, suggesting that such documentaries may impact preferences but not the final action of WTP. It is argued that the entropy weighting approach can be a useful tool in discrete choice modeling when the researcher is concerned with estimating differences in preferences between a group of interest and a comparison group.

Multi-criteria decision making problem for evaluating ERP system using entropy weighting approach and q-rung orthopair fuzzy TODIM

The q-rung orthopair fuzzy set theory is a flexible tool for dealing with the ambiguity of human judgements information as compared to the fuzzy, Atanassov intuitionistic fuzzy and Pythagorean fuzzy models. However, there is very few investigation for the entropy measure of q-rung orthopair fuzzy sets. Therefore, this paper firstly proposed a new entropy measure for q-rung orthopair fuzzy set along with their elegant properties. By changing the parameter q, $$q\ge 1$$ , q-rung orthopair fuzzy set can adjust the range of indication of decision information. Based on the proposed entropy measure, we proposed a new method to deal with MCDM problems under the q-rung orthopair fuzzy environment where the information about criteria weights is are partially known. We extend the TODIM (an Acronym in Portuguese of interactive and multicriteria decision making) approach for solving the multi-criteria decision-making problems (MCDM) where the behaviour of specialists are taken into account and elements of a set are interdependent. In addition, a numerical example is provided in solving real-life problems to illustrate the highlight of this study. The experimental and comparative results show the effectiveness and flexibility of the developed approach in solving real-life problems.

How to Select the Optimal Electrochemical Energy Storage Planning Program? A Hybrid MCDM Method

Electrochemical energy storage (EES) is a promising kind of energy storage and has developed rapidly in recent years in many countries. EES planning is an important topic that can impact the earnings of EES investors and sustainable industrial development. Current studies only consider the profit or cost of the EES planning program, without considering other economic criteria such as payback period and return on investment (ROI), which are also important for determining an optimal EES planning program. In this paper, a new hybrid multi-criteria decision-making (MCDM) method integrating the Bayesian best-worst method (BBWM), the entropy weighting approach, and grey cumulative prospect theory is proposed for the optimal EES planning program selection with the consideration of multiple economic criteria. The BBWM and entropy weighting approach are jointly employed for determining the weightings of criteria, and the grey cumulative prospect theory was utilized for the performance rankings of different EES planning programs. Five EES planning programs were selected for empirical analysis, including 9MW PbC battery EES, 2MW LiFePO lithium ion battery EES, 3MW LiFePO lithium ion battery EES, 2MW vanadium redox flow battery EES, and 3MW vanadium redox flow battery EES. The empirical results indicate the 2MW LiFePO lithium ion battery EES is the optimal one. The sensitivity analysis related to different risk preferences of decision-makers also shows the 2MW LiFePO lithium ion battery EES is always the optimal EES planning program. The proposed MCDM method for the optimal EES planning program selection in this paper is effective and robust, and can provide certain references for EES investors and decision-makers.

Product redesign evaluation: An improved quality function deployment model based on failure modes and effects analysis and 2-tuple linguistic

An improved quality function deployment model based on integration of design failure mode and effects analysis and 2-tuple linguistic is proposed to guide the design scheme selection in the product redesign process. By adopting the entropy weight approach, the risk priority number of the risk assessment index is improved. Meanwhile, the improved results are fed back in quality function deployment to reassess the target values. In addition, the accuracy of the model on the basis of 2-tuple linguistic is improved in the process of information evaluation and data processing. Finally, the model is applied in the case of gear reducer to demonstrate its applicability. This model can not only improve the product’s reliability, but also enhance the quality of product by taking the customers’ needs and product’s risk priority number into consideration simultaneously and therefore improve the market competitiveness of the products.

Selecting the Optimal Micro-Grid Planning Program Using a Novel Multi-Criteria Decision Making Model Based on Grey Cumulative Prospect Theory

As useful supplements and effective support for large-scale electric power networks, micro-grid systems are the development tendency of future electric power systems. The planning performance of a micro-grid not only affects its security, reliability and economy, but also has a profound influence on the stable operation of large-scale electric power networks with the increasing penetration of micro-grids. Hence, studies related to micro-grid planning program evaluation are of great significance. This paper established a novel multi-criteria decision making (MCDM) model combining the best-worst method (BWM), the entropy weighting approach, and grey cumulative prospect theory for optimum selection of micro-grid planning programs. Firstly, an evaluation index system containing 18 sub-criteria was built from the perspectives of economy, electricity supply reliability and environmental protection. Secondly, the weights of sub-criteria were calculated integrating the subjective weights judged by the BWM and the objective weights computed by the entropy weighting method. Then, the cumulative prospect theory (CPT) combined with grey theory was employed to select the optimal micro-grid planning program. The empirical result indicates that the program with 100 kWp photovoltaic power generation unit, 200 kW wind power generation unit and 600 kWh NaS battery energy storage system is the optimal micro-grid planning program. To verify the robustness of obtained result, a sensitivity analysis related to values change of parameters under different risk preferences was conducted, and the result indicates that the selected optimal micro-grid planning program will not be influenced by various risk preferences of decision makers (DMs) and investors. The novel MCDM proposed in this paper is applicable and feasible in the micro-grid planning programs evaluation and selection.

Multi-hierarchical Framework – Fuzzy Entropy Weight Approach for Auditing Technology in PT X

PT X is an Indonesia leading company in ship repair field. With the growth of shipyard industries in Indonesia, company is expected to compete within tight competition. Problems arise when the reparation processes can’t be finished on schedule. Respect to high vessel repairing demands, it can be a serious problem for company as they have many of ships waiting to be repaired. Management of technology might be the first step to solve the problem. Repair process within company is supported due to good management of technology policies. That’s why technology assessment is necessary in this company. This research is proposed to be used as recommendation to PT X on how to measure and assess the technological capabilities of company. Technology Audit Model (TAM) is used as basic technology assessment model. While multi-hierarchical framework - Fuzzy Entropy Weight Approach (FEWA) are tools used to process the multi-criteria of assessment model. Based on the assessment, it can be known the weight and rank of all criteria, by which are be utilized as input for SWOT analysis. Based on this process, the the improvement strategies could be generated. The proposed strategies are knowledge sharing program, environmental impact evaluation, and project network.

Ranking maintenance strategies for sustainable maintenance plan in manufacturing systems using fuzzy axiomatic design principle and fuzzy-TOPSIS

PurposeMaintenance plans are programmes, which follow maintenance appraisals, contain information of what to do and the time approximates for accomplishments. They also deal with how to carry out maintenance jobs. In contemporary period, curiosity has proliferated about how sustainability affects manufacturing plans. The purpose of this paper is to offer a comprehensive notion of maintenance sustainability in maintenance planning. The literature has downplayed maintenance sustainability but may support in understanding how to crack the present company-community conflicts about the negative influence of manufacturing on the environment.Design/methodology/approachThis study develops the idea of selecting the proper maintenance strategy based on integrated fuzzy axiomatic design (FAD) principle and fuzzy-TOPSIS. This work suggests that the maintenance function is an uncertain, activity-oriented system. To fully appreciate the proposed framework, the work employs data from a cement manufacturing plant to test the structure. This study offers 20 influential factors on which it build the fundamental structure of maintenance system sustainability for manufacturing concerns. A novel literature contribution that departs from existing conceptions is the classical determination of weights of each sustainability factor, employing fuzzy entropy weighting approach. Furthermore, work innovatively determines the ranking of some important tenets of sustainability in maintenance and optimises the maintenance consumables employing the FAD principle.FindingsInterestingly, the output of the investigation revealed differences as the work adopts fuzzy-TOPSIS in comparison with FAD principle.Originality/valueCase examination of a real-life manufacturing venture validated the claims, showing maintenance workforce training as a top-echelon strategy for maintenance system sustainability.

Mineral Potential Mapping Via TOPSIS with Hybrid AHP–Shannon Entropy Weighting of Evidence: A Case Study for Porphyry-Cu, Farmahin Area, Markazi Province, Iran

This paper presents an AHP–Shannon Entropy weighting approach as a new hybrid method for assigning evidential weights in mineral potential mapping. For demonstrating the proposed method, a case study was selected for porphyry-Cu potential mapping in Markazi Province, Iran. Then, geo-datasets were gathered, and evidence layers were generated for integration by TOPSIS method (via combination of AHP–Shannon Entropy weighting). Finally, the output mineral potential map was evaluated by field checking and chemical analysis of samples. Two outcrops with evidence of a porphyry system were encountered in areas with high potential values. In addition, there was good correlation between high potential values and Cu content of samples taken from the field. Hence, the usefulness of the AHP–Shannon Entropy weighting of evidence for MPM was demonstrated.

Study on Gray Correlation Analysis Model for Anti-Fatigue Entropy-AHP Weight Parts

In this paper, we set up a comprehensive assessment model to choose “best all time college coach” in the last century by combining Analytic Hierarchy Process with Gray Correlation Analysis. Taking the effect of time line horizon into consideration, we introduce Entropy weight approach into our model by Least Square Method and Linear Programming.

Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest

The crucial problem for integrating geospatial data is finding the corresponding objects (the counterpart) from different sources. Most current studies focus on object matching with individual attributes such as spatial, name, or other attributes, which avoids the difficulty of integrating those attributes, but at the cost of an ineffective matching. In this study, we propose an approach for matching instances by integrating heterogeneous attributes with the allocation of suitable attribute weights via information entropy. First, a normalized similarity formula is developed, which can simplify the calculation of spatial attribute similarity. Second, sound-based and word segmentation-based methods are adopted to eliminate the semantic ambiguity when there is a lack of a normative coding standard in geospatial data to express the name attribute. Third, category mapping is established to address the heterogeneity among different classifications. Finally, to address the non-linear characteristic of attribute similarity, the weights of the attributes are calculated by the entropy of the attributes. Experiments demonstrate that the Entropy-Weighted Approach (EWA) has good performance both in terms of precision and recall for instance matching from different data sets.

Entropy measures for linguistic information and its application to decision making

The aim of this paper is to develop some measures of entropy for linguistic information and uncertain linguistic information. Based on some given functions with required monotonicity and some kinds of distance measures, we propose two groups of entropies of linguistic fuzzy sets and uncertain linguistic fuzzy sets, respectively. Then, as an application of the proposed entropy measures, we put forward two algorithms for obtaining the objective weight vector of attributes in multiple attributes decision making by utilizing the entropy weight approach and the maximum entropy method. Finally, two cases of subjective evaluation of the building environment are illustrated to show the application of entropies for linguistic information in real world.

Sustainability assessment of machining process based on extension theory and entropy weight approach

The development of sustainability assessment for machining process is considered as one of the crucial strategies to realize sustainable design and manufacturing, while existing methods focusing on environmental assessment have limits due to neglect economic and social impacts. This paper proposes an assessment approach for evaluating sustainability performance of machining process with respect to a consistent set of environmental, economic, and social criteria in order to select a machining strategy from sustainable manufacturing viewpoint. Sustainability assessment criteria are identified to compare different machining parameter alternatives; cutting noise, especially, is defined as an important criterion of social impact, and energy, lubricant oil, and cutting fluids consumption are identified as criteria of environmental impact. Additionally, the weights of criteria are determined using entropy weight approach with the advantage of objective evaluation by exploiting the useful information of data to a maximum extent. Furthermore, sustainability performance of machining process is calculated based on extension theory through utilizing correlation function between evaluated matter-element and classical field matter-element. Face milling test conducted on CNC vertical machining center not only illustrates validity of the proposed method for assisting the engineers or designers to define sustainability performance of machining process, but also proves the relationship between cutting parameters and cutting noise.