Lost Dominance Score Research Articles

Household solid waste processing plant location selection: interval-valued intuitionistic fuzzy information-based gained and lost dominance score approach

Household solid waste processing plant location selection: interval-valued intuitionistic fuzzy information-based gained and lost dominance score approach

Prioritization of renewable energy for offshore ship charging stations based on intuitionistic fuzzy GLDS method: A case of China

Offshore ship charging station (OSCS) projects can help to address the current demand for electric ships for ocean voyages to a large extent. The proper selection of the energy source for power generation is a key part of the OSCS project. To select the optimal renewable energy for OSCS with many difficulties such as the ambiguity of the decision-making environment, the differences in group assessment information, and the conflict and compensation between criteria, this paper proposed a fuzzy multi-criteria decision-making (MCDM) framework. First, a comprehensive criteria system was constructed. Second, the intuitionistic fuzzy set (IFS) was introduced to express experts’ fuzzy cognition. Third, based on the quality of evaluation, a novel expert weighting method was proposed, and the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator was used to aggregate the individual evaluations. Fourth, the criteria importance through intercriteria correlation (CRITIC) method, and the stepwise weight assessment ratio analysis II (SWARA II) method were introduced to determine the criteria weights. Fifth, considering criteria compensation, and individual and group ranks, the gained and lost dominance score (GLDS) method were used for ranking. Finally, to verify the applicability and reliability of the framework, a case study was conducted in Pingtan Island, Fujian Province. The results show that wind energy was the best alternative, followed by solar, wave and nuclear energy.

A linguistic Z-number-based sorting method for three-way multi-criterion decision-making in ecological function zoning

Traditional multi-criterion decision-making methods can help decision-makers decide whether to accept or reject an alternative, but they cannot ensure these results fully meet decision-makers’ requirements. Three-way decision methods overcome this limitation by dividing alternatives into three regions. However, traditional three-way decision methods rarely considered the reliability of evaluation information and individual regrets under some criteria. Moreover, most weighting methods in three-way decisions ignored the influence of abnormal values. To fill the above gaps, this paper develops a linguistic Z-number-based sorting method combining the three-way decision with the gain and lost dominance score (GLDS) method that reflects individual regrets under some criteria. Firstly, to avoid individual regrets, we introduce the linguistic Z-number-based GLDS sorting (LZ-GLDS-Sort) method, which obtains two states of alternatives that will be used in the three-way decision. Linguistic Z-numbers can express linguistic evaluation and credibility. Two parameters are introduced in the LZ-GLDS-Sorting method to reflect the relative importance of evaluation information and credibility. Then, a three-way decision method is introduced to divide alternatives into three regions, in which the variation-coefficient weighting method is introduced to avoid the effect of abnormal values. Finally, the ecological function zoning as an example is provided to validate the applicability of this model.

What makes a better hotel selection? A hybrid approach considering tourists’ preference and review helpfulness

ABSTRACT Hotel selection is crucial in planning trips. Several studies constructed the decision methods, which assumed that people are completely rational and neglected decision-maker’s (DM’s) behavioural aspects, based on text reviews or numerical ratings with the subjective or objective weights of criteria. Therefore, this study proposes a picture fuzzy gained and lost dominance score (PF-GLDS) method based on prospect theory, which integrates user-generated text reviews and multi-criteria ratings to facilitate hotel selection while considering tourists’ preference and review helpfulness. First, review helpfulness is analyzed to enhance data quality. Second, a decision matrix is constructed by simultaneously considering sentiment in helpful text reviews as well as multi-criteria ratings. Third, the criteria are derived by the maximising deviation and best worst methods (BWM), respectively, for tourists’ preferred objective and subjective weights. Finally, to address hotel selection problems, an extended GLDS method with picture fuzzy information, considering bounded rationality behaviour, is developed. Empirical evaluation on five hotels from the TripAdvisor website demonstrates the reasonableness and advantages of our proposed hybrid approach in achieving more objective and reliable decision making. Concurrently, this study can serve as a reference for tourists seeking well-suited hotels and tourism website optimisation.

Analyzing the risk of the ammonia storage facility using extended FMEA model based on probabilistic linguistic GLDS method with consensus reaching

Due to the energy crisis, ammonia stands out as an efficient hydrogen energy carrier. However, liquid ammonia's nature of toxicity, corrosiveness, volatility, flammability, etc., make its storage highly risky. It is essential to analyze the risk of the ammonia storage facility for its safety improvement. Failure mode and effect analysis (FMEA) has been a popular risk analysis method due to its ease of implementation. However, traditional FMEA is insufficient to model the uncertainty of risk assessment information, enhance the consensus among experts, aggregate experts' risk evaluation information, and derive reliable risk analysis results. To address these limitations, we introduce a hybrid probabilistic linguistic FMEA framework to analyze failure risk in the ammonia storage facility. First, the probabilistic linguistic terms (PLTs) are introduced to express uncertain risk evaluation information of failure modes. Second, a risk acceptability-based consensus-reaching process is introduced to measure experts' consensus on each failure mode's risk and improve their consensus. Then, a consensus degree-based ordered weighted averaging operator is introduced to fuse experts' risk evaluation information, where experts' weight is determined by their consensus degree. Furthermore, the gained and lost dominance score method is improved on the basis of the expected value of the PLTs to prioritize failure modes' risk. Finally, a case study of the risk analysis for the ammonia storage facility is performed to show the efficiency of the extended FMEA. The case study indicated that condenser cooling water pump failure and failure of level transfer are the top two failure modes and have risk scores of 0.126 and 0.091, respectively.

Geographic information system-based multi-criteria decision-making analysis for investment assessment of wind-photovoltaic-shared energy storage power stations: a case study of Shanxi Province.

As the center of the development of power industry, wind-photovoltaic (PV)-shared energy storage project is the key tool for achieving energy transformation. This research seeks to construct a feasible model for investment appraisal of wind-PV-shared energy storage power stations by combining geographic information system (GIS) and multi-criteria decision-making (MCDM) method. Firstly, a comprehensive criteria system is established from the perspectives of orography, economy, resources, climate, and society, and the evaluation data is described using probabilistic linguistic term sets (PLTSs). Then, to avoid the weight deviation produced by the single weighting approach, a comprehensive weighting model including the best-worst method (BWM) and entropy weight method is provided to calculate the weights of criteria. Next, expert weights are calculated based on trust analysis. Finally, alternatives are ranked by the improved gained and lost dominance score (GLDS) method. To verify the validity of the model, an empirical investigation is carried out in Shanxi Province. The results show that the economy is the primary factor influencing the investment decision. Among all the projects approved by the government, alternative F4 located in Yanzhuang Town, Yuanping City is the best investment object. Furthermore, to illustrate the stability of the result, triple sensitivity analysis and comparative analysis are conducted in Shanxi Province. This study expands the application scope of GIS and MCDM method by first providing support for government and investors to identify optimal investment targets.

A case-driven emergency decision-making model based on probabilistic linguistic bidirectional projection

Historical cases have been accumulated to use the problem-solving experiences of similar types of historical cases to solve current problems. The existing studies has shown its potential to generate alternative accurately but also exposed some challenges, such as the precision of case information representation, the accuracy of case match, and the validity of the generated alternative. This paper proposed a case-driven emergency decision-making model (EDM) based on probabilistic linguistic bidirectional projection to overcome these challenges. A new attribute match measurement based on probabilistic linguistic bidirectional projection under probabilistic linguistic term sets (PLTSs) environment not only preserve the integrity of the case information but also facilitate valid retrieval results and guarantee an interpretable decision-making process. An aggregation model based on the best–worst method, and a distance-based optimization model considering the interactions between attribute combinations is then constructed to weigh all the attributes to determine similarities between the current and historical cases, which can make the match result more reasonable. Afterwards, a bidirectional projection–gained and lost dominance score (GLDS) method is developed and applied to improve the fairness and validity of the retrieval results. Finally, a case study of suspected cases of Coronavirus Disease 2019 (COVID-19) demonstrates the practicability of the proposed proposal, and comparative analysis illustrates its superiority over alternatives.

A heterogeneous multi-attribute case retrieval method for generating emergency response alternatives based on interval evidential reasoning and gained and lost dominance score method: An application to the COVID-19 emergency situation

Heterogeneity exists in the case information of an emergency, and case retrieval plays a crucial role in generating an emergency response alternative. Despite advancements, fusing heterogeneous case information and considering decision maker preferences remain challenging issues to be solved. To address this, a novel heterogeneous multi-attribute case retrieval method is proposed and applied to COVID-19 emergency decision-making in this study. To overcome uncertainties and heterogeneities in case information, a new interval evidential reasoning (IER)-based case similarity measurement is developed to better protect data intergrity and improve case retrieval accuracy. Next, the best-worst method and information entropy are used to obtain subjective and objective weights of evaluated attributes, respectively, and subsequently these weights are integrated. Furthermore, a novel interval gained and lost dominance score (GLDS) method based on TODIM (Portuguese acronym for interactive multi-criteria decision-making) is developed to derive the comprehensive utilities of the similar historical case. This method simultaneously considers the fairness of the decision-making process and the psychological behavior of decision makers. Finally, a simulation example with large-scale data and two illustrated examples of generating emergency response altenatives are provided to demonstrate the applicability of the proposed method. Sensitivity and comparative analysis are conducted to verify the validity of the method.

Assessing the impact of healthcare service risks on healthcare demand under evolving economic and social structures: An improved GLDS decision making method considering risk attitudes

Faced with backward and uneven economic conditions and an irrational and increasingly inequitable social structure, healthcare disparities are widening and the healthcare inequalities are expanding. This can lead to an increasing public demand for healthcare services, posing unprecedented risk, such as resource shortages, quality deterioration and rising healthcare costs. By identifying, managing, and optimizing the aforementioned risks within the healthcare system, it helps the healthcare system better address the challenges of expanding healthcare demands under evolving economic and social structure. To achieve this, this paper employs failure mode and effects analysis (FMEA) to identify severe failure modes in healthcare services. However, traditional FMEA methods possess limitations in assessing the dynamics of trust relationships and interaction effects among experts within social networks. Therefore, this paper proposes an improved gained and lost dominance score (GLDS) based social network decision-making method considering risk attitudes for FMEA to prioritize failure modes in healthcare services. First, transform linguistic risk assessment information from an interdisciplinary team of experts based on probabilistic linguistic term sets handle uncertain efficiently. Meanwhile, experts’ influence is reflected by an extended PageRank algorithm with trust degrees based on social networks analysis. Next, an improved GLDS method considering the differentiated risk attitudes of individuals and groups is developed to prioritize failure modes. Especially, to solve the problems of soft preference and incomparability relation among failure modes, which are ignored by the traditional GLDS method, a conflict analysis is established to determine the preference, indifference, and incomparability relations. Finally, the practicality of the proposed method is illustrated by the healthcare service risk case in the emergency department and the effectiveness of the developed method is validated by comparison and sensitivity experiments. The results suggest that failure to update labels showing patient severity promptly is the most dangerous. In addition, it would be more economically efficient to analyze the non-comparable failure modes facing different risk factors.

A Fermatean fuzzy GLDS approach for ranking potential risk in the Fine-Kinney framework

The Fine-Kinney (F-K) model has been broadly employed for evaluating and ranking risk in various fields. The risk scoring information expression and priority ranking are two significant operations for its application. Numerous approaches have been extended to the two operations to improve the performance of conventional Fine-Kinney for risk analysis. Nevertheless, current literature on the F-K framework seldom considers the collective and individual risk attitudes in ranking potential hazards, especially with Fermatean fuzzy-based -risk scoring information. This paper generates a new ranking approach for risk prioritization in F-K to fulfill this gap by integrating the Fermatean fuzzy sets with the GLDS (gained and lost dominance score) method. First, the Fermatean fuzzy sets-based risk scale is introduced to acquire risk scores. Then, a new collective risk scoring matrix establishment approach based on Fermatean fuzzy Bonferroni mean (BM) operator is built for considering the interactive effects between experts. Next, an extended Fermatean fuzzy GLDS method with CRITIC (Criteria Importance Through Inter-criteria Correlation)is proposed to rank the potential hazards, in which the Fermatean fuzzy CRITIC method is adopted to determine the weights. Especially, this developed weighting method can depict the inter-correlation among risk parameters. Finally, this paper presents a risk evaluation case of professional hazards for construction operations to display the application and advantages of this improved hybrid risk ranking model in the F-K framework. The result reveals that the enhanced framework can effectively rank potential hazards with complex risk information.

A generalized linguistic gained and lost dominance score method for landslide hazard treatment

Reducing the risk of landslide hazards necessitates a prompt emergency decision regarding landslides, particularly when a hasty investigation lacks abundant data. Thus, the experience and opinions of experts, which are typically expressed in linguistic terms rather than exact values, can be utilized to the fullest extent to facilitate this process. Here I extend the gained and lost dominance score (GLDS) method to a generalized linguistic setting for modeling collective cognition in the selection of landslide treatment schemes for an open-pit mine. To do so, a generalized qualitative scale considering two cognitive bias arguments and one granularity parameter of linguistic term sets (LTS), which constitutes a generalized LTS that allows experts to express their evaluations in a flexible way, is presented to capture precise semantics of linguistic evaluations. Furthermore, several aggregation operators are given to deal with the generalized LTS for information fusion. To promote the application of the GLDS method within the context of multi-expert heterogeneous linguistic environment, I develop the framework of a generalized GLDS method by improving three aspects (i.e., the construction of dominance flow, the computing of lost dominance scores and the establishment of final aggregation function) of the classical one. Lastly, the effectiveness of the proposed method is examined by solving the multiple criteria decision-making problem we encountered as well as comparing with other outranking methods. The lattice anchored technique coupled with real-time monitoring is picked out to treat the landslide, which mitigates the production pressure in this high-slope open-pit mine.

Intuitionistic fuzzy gained and lost dominance score based on symmetric point criterion to prioritize zero-carbon measures for sustainable urban transportation

PurposeAchieving a zero-carbon city requires a long-term strategic perspective. The authors propose a decision-making model which would take into account the economic, environmental and social impacts for prioritizing the zero-carbon measures for sustainable urban transportation.Design/methodology/approachAn integrated intuitionistic fuzzy gained and lost dominance score (IF-GLDS) model is introduced based on intuitionistic fuzzy Yager weighted aggregation (IFYWA) operators and proposed weight-determining IF-SPC procedure. In addition, a weighting tool is presented to obtain the weights of decision experts. Further, the feasibility and efficacy of developed IF-SPC-GLDS model is implemented on a multi-criteria investment company selection problem under IFS context.FindingsThe results of the developed model, “introducing zero-emission zones” should be considered as the first measure to implement. The preference of this initiative offers sustainable transport in India to achieve a zero-carbon transport by having the greatest impact on the modal shift from cars to sustainable mobility modes with a lower operational and implementation cost as well as having greater public support. The developed model utilized can be relocated to other smart cities which aim to achieve a zero-carbon transport. Sensitivity and comparative analyses are discussed to reveal the robustness of obtained result. The outcomes show the feasibility of the developed methodology which yields second company as the suitable choice, when compared to and validated using the other MCDA methods from the literature, including TOPSIS, COPRAS, WASPAS and CoCoSo with intuitionistic fuzzy information.Originality/valueA new intuitionistic fuzzy symmetry point of criterion (IF-SPC) approach is presented to find weights of criteria under IFSs setting. Then, an IF-GLDS model is introduced using IFYWA operators to rank the options in the realistic multi-criteria decision analysis (MCDA) procedure. For this purpose, the IFYWA operators and their properties are developed to combine the IFNs. These operators can offer a flexible way to deal with the realistic MCDA problems with IFS context.

A developed gained and lost dominance score method for risk prioritization in FMEA with Fermatean fuzzy information

Failure mode and effect analysis (FMEA) is one of the most effective means for potential systematic risk assessment in a real work environment. Nevertheless, the traditional FMEA approach has been extensively criticized for many deficiencies in coping with risk evaluation and prioritization problems under inter-uncertain environments. To overcome the limitations, in this paper, a synthesized risk priority calculation framework is proposed for FMEA by combining the gained and lost dominance score (GLDS) method, the combination ordered weighted averaging (C-OWA) operator, and Fermatean fuzzy set (FFS). Firstly, we use FFS to express the experts’ uncertain risk evaluation information which can depict the fuzziness and ambiguity of the information. Secondly, the C-OWA operator combined with FFS is introduced to build the group risk matrix which can provide a more reasonable risk analysis result. Then, the developed GLDS method with FFS is presented to calculate the risk priority of each failure mode which takes both individual and group risk attitudes into consideration. Finally, a medical device risk analysis case is introduced to demonstrate the proposed FMEA framework. We also perform comparison analyses to confirm the effectiveness and rationality of the hybrid risk prioritization framework for FMEA under a complex and uncertain situation.

Consensus-based failure mode and effect analysis in group decision-making with incomplete weights of risk factors: Case study of stereotactic body radiation therapy for lung cancer

Failure mode and effect analysis (FMEA) is a widely used management technique to identify potential failure modes in products, processes, systems, and services to assign limited resources for implementing improvement efforts. For the FMEA with a number of experts, the experts may take different influences on the decision-making process due to the difference between individual rationality and cognition and the influence of social relations. Besides, incomplete weights of risk factors are vital to capturing the fuzziness of experts’ evaluation. Therefore, this paper proposes a consensus-based FMEA method to explore that how cooperative relationship network of experts, the consensus process, and incomplete weight information of risk factors in FMEA influence the decision-making process. First, a consensus reaching process considering cooperative relationships between experts, the self-confidence of experts, and minimum adjustment of expert opinion is proposed to derive a collective evaluation of group. Afterward, the stochastic multicriteria acceptability analysis method is integrated with the gained and lost dominance score method to deal with incomplete weights of risk factors in the FMEA. The proposed method is employed in a case study about ranking the failure modes of stereotactic body radiation therapy for lung cancer patients. The robustness and feasibility of the proposed method are validated by comparative analysis.

Failure Mode and Effect Analysis Based on Probabilistic Linguistic Preference Relations and Gained and Lost Dominance Score Method.

Failure mode and effect analysis (FMEA) is a widely used reliability management technology to evaluate the risk of potential failures in a system, product, or service. Nevertheless, the normal risk priority number (RPN) method has been extensively criticized for many deficiencies in practical applications. To overcome the drawbacks of traditional FMEA, plenty of methods have been suggested in previous studies. But majority of them evaluated the risk factors of each failure mode directly and cannot take group and individual risk attitudes into account. In this article, we put forward a new FMEA approach integrating probabilistic linguistic preference relations (PLPRs) and gained and lost dominance score (GLDS) method. The PLPRs are adopted to describe the risk evaluations of experts by pairwise comparison of failure modes. An extended GLDS method is introduced to derive the risk ranking of failure modes considering both group and individual risk attitudes. Moreover, a two-step optimization model is proposed to determine the weights of risk factors when their weighing information is unknown. Finally, a load-haul-dumper machine risk analysis case is presented to demonstrate the proposed FMEA. It is shown that the approach being proposed in this study provides a practical and effective way for risk evaluation in FMEA.

Evaluating Internet hospitals by a linguistic Z-number-based gained and lost dominance score method considering different risk preferences of experts

Internet hospitals are platforms that provide medical information, prescription, and treatment by combining online and offline medical resources. Well-development Internet hospitals can reduce operational pressure of offline hospitals and unnecessary waste of medical resources. Evaluating the performance of Internet hospitals is helpful for their development. In this paper, we introduce a linguistic Z-number-based gained and lost dominance score (GLDS) method to evaluate Internet hospitals. Firstly, to ensure the reliability of evaluation information, we extend the GLDS method to the linguistic Z-number environment. To model the risk preference of an expert, we depict the negative performance of an alternative over other alternatives under each criterion by a lost dominance score function, and represent the individual regret of a expert to the worst alternative by a net lost dominance score function. A weighting method that combines the best worst method and correlation coefficient method is developed to determine the weights of criteria. To verify the validity and practicability of the proposed method, a case study of ranking Internet hospitals is provided. Comparative analysis shows that the solutions of the proposed method reflect the preferences of experts.

Novel Gained and Lost Dominance Score Method Based on Cumulative Prospect Theory for Group Decision-Making Problems in Probabilistic Hesitant Fuzzy Environment

Novel Gained and Lost Dominance Score Method Based on Cumulative Prospect Theory for Group Decision-Making Problems in Probabilistic Hesitant Fuzzy Environment

An intuitionistic fuzzy entropy-based gained and lost dominance score decision-making method to select and assess sustainable supplier selection

<abstract> <p>Sustainable supplier selection (SSS) is recognized as a prime aim in supply chain because of its impression on profitability, adorability, and agility of the organization. This work introduces a multi-phase intuitionistic fuzzy preference-based model with which decision experts are authorized to choose the suitable supplier using the sustainability "triple bottom line (TBL)" attributes. To solve this issue, an intuitionistic fuzzy gained and lost dominance score (IF-GLDS) approach is proposed using the developed IF-entropy. To make better use of experts' knowledge and fully represent the uncertain information, the evaluations of SSS are characterized in the form of intuitionistic fuzzy set (IFS). To better distinguish fuzziness of IFSs, new entropy for assessing criteria weights is proposed with the help of an improved score function. By considering the developed entropy and improved score function, a weight-determining process for considered criterion is presented. A case study concerning the iron and steel industry in India for assessing and ranking the SSS is taken to demonstrate the practicability of the developed model. The efficacy of the developed model is certified with the comparison by diverse extant models.</p> </abstract>

Multicriteria decision making based on novel score function of Fermatean fuzzy numbers, the CRITIC method, and the GLDS method

In this paper, we propose a new score function of Fermatean fuzzy numbers (FFNs). The proposed score function of FFNs can conquer the drawbacks of the existing score function of FFNs. We develop a new multicriteria decision making (MCDM) method based on the proposed score function of FFNs, the criteria importance through intercriteria correlation (CRITIC) method and the gained and lost dominance score (GLDS) method with Fermatean fuzzy information. The developed MCDM method can conquer the shortcomings of the existing MCDM methods for MCDM in the environments of FFNs. It provides us a very useful way for MCDM in the context of FFNs.

Optimal site selection study of wind-photovoltaic-shared energy storage power stations based on GIS and multi-criteria decision making: A two-stage framework

Wind-photovoltaic-shared energy storage system can improve the utilization efficiency of renewable energy resources while reducing the idle rate of energy storage resources. Using the geographic information system (GIS) and the multi-criteria decision-making (MCDM) method, a two-stage evaluation model is first developed for site selection of wind-photovoltaic-shared energy storage projects. In the first stage, infeasible alternatives are ruled out due to veto identification. In the second stage, the fuzzy MCDM method is applied to further merit selection. Firstly, the comprehensive criteria system is built, and evaluation data is collected using probabilistic linguistic term sets (PLTSs) to ensure the comprehensiveness and scientificity of decision. Then, the new score function based on compromise theory are proposed. Thirdly, the criteria weights are calculated using the improved Decision-Making and Trial Evaluation Laboratory (DEMATEL) approach. Finally, the improved gained and lost dominance score (GLDS) method is used to rank alternatives. To test model's feasibility, an empirical study is conducted in Chifeng City. The alternative A1 appears to be extremely prominent in the results. The model's stability and reliability are demonstrated by sensitivity and comparative analysis. This study aids in the layout of wind-photovoltaic-shared energy storage projects and broadens the application scopes of GIS and MCDM method.