Lost Dominance Score Method Research Articles

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.

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.

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.

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.

An extended gained and lost dominance score method based risk prioritization for Fine-Kinney model with interval type-2 fuzzy information

The Fine-Kinney model is one of the most extensively adopted classical occupational risk analysis techniques which has been regarded as a helpful means for addressing the risk identification and evaluation problems in various fields. However, current extended Fine-Kinney models have limited capacity to cope with occupational risk evaluation and prioritization problems considering interactive relationships among multi-risk parameters under inter-uncertain and intra-uncertain environments. To overcome the limitations, this paper proposes a hybrid risk prioritization approach for the Fine-Kinney model by integrating GLDS (gained and lost dominance score) method, interval type-2 fuzzy sets (IT2FSs), and Maclaurin symmetric mean (MSM) operator. First, the trapezoidal interval type-2 fuzzy numbers (TrIT2FNs) are introduced to depict the inter-uncertain and intra-uncertain risk evaluation information. Then, an ordered weighted averaging (OWA) operator is constructed to aggregate the complex and uncertain risk evaluation information from heterogeneous decision-makers in which the similarity-measure-based weighting method is incorporated. Next, the GLDS method combined with the weighted MSM operator is developed to address the risk prioritization problem in the Fine-Kinney model by considering the inter-dependencies among multi-risk parameters. Finally, a numerical example of occupational risk evaluation for potential hazards in construction excavation is presented within IT2FSs to demonstrate the application and availability of the extended Fine-Kinney model. After that, the comparison and sensitivity analyses are conducted to further illustrate the superiority and rationality of the proposed approach.

A gained and lost dominance score method with conflict analysis for green economy development evaluation

A gained and lost dominance score method with conflict analysis for green economy development evaluation

A probabilistic interval-valued hesitant fuzzy gained and lost dominance score method based on regret theory

With the increasing complexity of the decision environment, the fuzzy theory has attracted the extensive attention of scholars. Probabilistic interval-valued hesitant fuzzy set, as a complex ambiguous information representation tool, dedicates to depict uncertain information as complete as possible. Thus, this paper proposes a probabilistic interval-valued hesitant fuzzy gained and lost dominance score method based on regret theory and interval evidential reasoning approach. In view of the uncertainties in evaluations, the interval evidential reasoning approach is an effective tool to deal with the fuzziness and ignorance, which is first applied in probabilistic interval-valued hesitant fuzzy information in this paper. Besides, an interval projection measure is proposed to consider both distance and direction simultaneously, so as to avoid the problem of information losses in the decision process. On this basis, a novel regret-rejoice function is developed. Then, an extended gained and lost dominance score method based on regret theory and interval evidential reasoning approach is developed, which considers the regret avoidance behavior of experts. Finally, a case study on supplier selection of epidemic prevention products is applied to elucidate and illustrate the application of the proposed method in this paper.

Probabilistic reliable linguistic term sets applied to investment project selection with the gained and lost dominance score method

Probabilistic linguistic term sets (PLTSs) are an effective tool in keeping with the habits of decision makers (DMs). However, in multi-criteria group decision making (MCGDM) problems, it is necessary to deal with the information reliability problem because of the difference of the DMs’ knowledge backgrounds and knowledge structures. Therefore, this paper proposes a novel concept called probabilistic reliable linguistic term sets. Based on which, some basic operations, comparison laws, distance measures, similarity measures and aggregation operators are defined. After that, we propose the probabilistic reliable linguistic gained and lost dominance score method to cope with MCGDM problems, and we further apply the proposed method to solve an investment project selection problem about lucky bag machine. Finally, we make some comparative analyses to verify the effectiveness and highlight the strength of the proposed method compared with four methods, i.e., the aggregation-based method, the TOPSIS method under probabilistic reliable linguistic environment, the gained and lost dominance score (GLDS) method with probabilistic linguistic information and the GLDS method with hesitant linguistic information.

Warhead power assessment based on double hierarchy hesitant fuzzy linguistic term sets theory and gained and lost dominance score method

Warhead power assessment of the anti-ship missile plays a vital role in determining the optimal design of missile, thus having important strategic research significance. However, in the assessment process, expert’s judgement will directly affect the assessment accuracy. In addition, there are many criteria involved in the missile design alternatives. Some criteria with poor performance may be compensated by other criteria with excellent performance, and then it is impossible to find the truly optimal alternative. Aimed at solving these problems, this paper proposes a synthetical assessment process based on fuzzy hesitant linguistic term set and the Gained and Lost Dominance Score (GLDS) method. In order to improve the assessment accuracy of experts and solve the problem that experts generate different opinions, combined with the advantages of fuzzy hesitant sets and linguistic term sets, the double hierarchy hesitant fuzzy linguistic term sets are used in this paper to improve the accuracy of expert’s judgement. In order to effectively combine expert’s experience with the data of criteria, the evidence theory and entropy weight method are used to transfer the expert’s judgement to the weight. In order to avoid selecting defective alternative of missile design, the GLDS is used to fuse expert information and criteria information. Sensitivity analysis shows that the assessment process has sensitivity to some extent. However, when the fluctuation of expert’s assessment makes the fluctuation of θ in the range of −5% to 5%, the impact on the results is not quite conspicuous. The analysis of calculation result and comparative analysis show that the assessment process proposed in this paper is accurate enough, has great advantage in selecting the current and potential optimal alternative of missile design, and avoids the alternatives with low criteria performance that cannot be compensated by other criteria being selected.

Capturing attitudinal characteristics of decision-makers in group decision making: application to select policy recommendations to enhance supply chain resilience under COVID-19 outbreak

The impact of COVID-19 on the global outbreak of supply chain is enormous. It is crucial for governments to take policy recommendations to enhance the supply chain resilience to mitigate the negative impact of COVID-19. For such a major issue, it is a common occurrence that a large number of decision-makers (DMs) are invited to participate in the decision-making process so as to ensure the comprehensiveness and reliability of decision results. Since the attitudinal characteristics of DMs are important factors affecting decision results, this study focuses on capturing the attitudinal characteristics of DMs in the large-scale group decision making process. The capturing process combines the ordinal k-means clustering algorithm, gained and lost dominance score method and personalized quantifiers. To enable DMs to express their cognitions in depth, we use the probabilistic linguistic term set to express the evaluation information of DMs. A case study on selecting the optimal policy recommendation for improving the integration capability of supply chain is given to illustrate the applicability of the proposed process. The superiority of the proposed algorithm is highlighted through sensitive analysis and comparative analysis.

Consensus-based robust decision making methods under a novel study of probabilistic uncertain linguistic information and their application in Forex investment

The probabilistic uncertain linguistic terms set (PULTS) is an effective tool to depict uncertain linguistic opinions of individuals or groups in the procedure of decision making. Motivated by the power of PULTS and the linguistic scale function, this study aims to propose robust techniques to solve multi-attribute group decision making problems with uncertain linguistic evaluations. To enrich calculation and enhance the flexibility of PULTS, we first generalize the aggregation formula to fuse opinions of decision makers represented as PULTSs and secondly derive adjusting rule of probability to adjust the probability distribution of two or more than two probabilistic uncertain elements (PULEs) into the same probability distribution. Novel operations of PULTSs are designed based on the adjusting rule of probability distribution and linguistic scale function for the semantics of linguistic terms. Many related properties of these operations are also discussed. New score function and deviation degree of PULEs are also developed to compare PULEs. Two aggregation operators i.e., probabilistic uncertain linguistic averaging (PULWA) operator and probabilistic uncertain linguistic geometric (PULWG) operator are also redefined in terms of novel operations. In addition, a series of distance measure is defined to overcome the shortcomings of existing ones. After defining a correlation measure, the probabilistic uncertain linguistic (PUL)-consensus reaching method (in which two specific consensus approaches are described separately) is put forward to refine the consensus level of a group. To suit the needs of different semantics, two robust decision making methods named as consensus-based PUL-gained and lost dominance score method and consensus-based PUL-aggregation method are proposed. Finally, a case study concerning the selection of the best commodity for investment in Forex is conducted to illustrate the practicality of the proposed methods. Lastly, a detailed comparative analysis is done with the existing technique to highlight the improvements and advantages of proposed work.

Third-Party Cold Chain Medicine Logistics Provider Selection by a Rough Set-Based Gained and Lost Dominance Score Method

Choosing third-party cold chain logistics suppliers can be regarded as a multiple criteria group decision-making problem since multiple aspects of suppliers are required to be evaluated by multiple experts. This paper aims to address the problem of selecting the optimal third-party cold chain medicine logistics provider considering the uncertainties caused by the qualitative criteria that are difficult to accurately evaluate and the limitation of experts’ knowledge and cognition. We propose a rough set-based gained and lost dominance score method in which experts are supposed to use linguistic terms to express their information. First, we combine rough numbers with linguistic scale functions to depict the semantics of linguistic terms and convert the formal expression information of linguistic terms into numerical information, which has an advantage of expressing imprecision and subjective judgments of experts. In addition, given that different experts often have different emphases on different attributes, we investigate a rough set-based gained and lost dominance score method in which experts have different weights for different criteria. Finally, an illustrative example of selecting the optimal third-party cold chain medicine logistics is given with comparative analysis, showing the efficiency of the proposed method. This method provides a new way to solve the selection problem of third-party logistics suppliers.

Managing patient satisfaction in a blood-collection room by the probabilistic linguistic gained and lost dominance score method integrated with the best-worst method

Evaluating patient satisfaction on medical services in terms of multiple aspects is critical for hospital management. To settle this problem, a criteria system for medical service evaluation is first structured through a survey on experts. The probabilistic linguistic representation model is used to portray the qualitative evaluations that are unquantifiable in nature or unable to be accurately measured. Then, the best-worst method is extended to the probabilistic linguistic context to determine the weights of criteria information. Afterwards, we propose a comprehensive method by combing the probabilistic linguistic best-worst method with the gained and lost dominance score method to measure both the collective and worst performances of alternatives. Finally, we investigate a practical case about managing patient satisfaction in the blood collection room of a Chinese hospital which undergoes a reformation. The results show that the patient satisfaction has greatly improved after the reformation.

A Choquet integral-based hesitant fuzzy gained and lost dominance score method for multi-criteria group decision making considering the risk preferences of experts: Case study of higher business education evaluation

With the rapid development of higher business education, higher business education evaluation has attracted considerable attention of researchers and practitioners. The higher business education evaluation is an essential part of the development of a business school, which has a direct impact on its resource distribution. The higher business education evaluation can be considered as a multiple criteria group decision making (MCGDM) problem that involves a group of experts. Due to the complexity of the decision-making problem, decision criteria are not fully independent to each other, and the assumption of complete rationality of experts is usually invalid in many situations. In this paper, we propose a Choquet integral-based hesitant fuzzy gained and lost dominance score method to address the two important issues regarding the interactions among criteria and the behavior preference characteristics of experts in MCGDM problems. Firstly, a comprehensive distance measure of hesitant fuzzy sets is introduced by considering the relative importance of two separations. Then, a Choquet integral-based hesitant fuzzy gained and lost dominance score method based on the prospect theory is proposed to address the MCGDM problems in which experts make decision with the risk preference psychology. Finally, an illustrative example of higher business education evaluation is provided to demonstrate the applicability of the proposed method, and the sensitivity and comparative analysis are also completed to verify the validity of the proposed method.

Failure Mode and Effect Analysis for Machine Tool Risk Analysis Using Extended Gained and Lost Dominance Score Method

Enhancing the reliability of machine tools and preventing the potential failures in the manufacturing process is one of the most important tasks for the development of industry. The failure mode and effects analysis (FMEA) is the well-known and widely utilized approach to identify and evaluate potential failures for preventing risk in various enterprises. It is a group-oriented technique ordinarily conducted by a group of experts from related fields. Obviously, an effective approach should be developed, which is used to integrate risk evaluation information from multiexperts by considering group and individual risk attitudes. This article proposes a hybrid FMEA framework for addressing machine tool risk analysis problem by integrating cloud model, Choquet integral, and gained and lost dominance score (GLDS) method. In this framework, an improved Shapley cloud-Choquet weighting averaging operator is defined to fuse random and uncertain risk information by considering the correlations among experts. An extended GLDS method with developed Choquet integral based on distance measure of normal clouds is presented to prioritize the risk priority of each failures, in which the group and individual risk attitude and risk interactions are considered simultaneously. Finally, a real risk analysis of machine tool in a machine tool industry is introduced to illustrate the application and feasibility of the proposed approach, and comparison and sensitivity studies are also conducted to validate the effectiveness of the hybrid framework.

Life satisfaction evaluation in earthquake-hit area by the probabilistic linguistic GLDS method integrated with the logarithm-multiplicative analytic hierarchy process

Occurred on May 12, 2008, the Wenchuan earthquake in China has received worldwide attention for its incredible destructive power. In June of the same year, the State Council issued a draft regulation on post-quake restoration and reconstruction. To find the effect of post-earthquake reconstruction, this paper measures the residents' life satisfaction in Wenchuan by multiple criteria decision-making methods under probabilistic linguistic environment. Based on field surveys and domains of life satisfaction, an indicator system is proposed as a framework to collect resident's evaluations. To retain as much original information as possible, an effective weighting method with probabilistic linguistic information is developed to deduce the indicator's importance. It extends the logarithm-multiplicative analytic hierarchy process to handle the probabilistic linguistic evaluations and derive the criteria weights in interval forms. In this way, the inherent uncertainty of the evaluations on the importance of life satisfaction indicators can be reflected. Furthermore, a novel outranking method named the probabilistic linguistic gain and lost dominance score method, is used to obtain the overall life satisfaction degrees of the residents in Wenchuan. We apply this method to measure the life satisfaction degrees of residents in two areas. The calculation results show that the life satisfaction degrees of the residents in the earthquake-hit area are higher than those of the residents in the non-earthquake area.

A consensus-based probabilistic linguistic gained and lost dominance score method

This paper proposes a comprehensive Multiple Criteria Group Decision Making (MCGDM) method with probabilistic linguistic information based on a new consensus measure and a novel outranking method, Gained and Lost Dominance Score (GLDS). Firstly, new operations of the probabilistic linguistic term sets are introduced based on the adjusted rules of probabilistic linguistic term sets and the linguistic scale functions for semantics of linguistic terms. After defining a new consensus measure based on the correlation degree between probabilistic linguistic term sets, we develop a consensus reaching method to improve the consensus degree of a group. To rank alternatives reasonably, we further propose the GLDS method which considers both the “group utility” and the “individual regret” values. The core of the GLDS is to calculate the gained and lost dominance scores that the optimal solution dominates all other alternatives in terms of the net gained dominance flow and the net lost dominance flow. Then, we integrate the GLDS ranking method with the consensus reaching process and develop a consensus-based PL-GLDS method to solve the MCGDM problems with probabilistic linguistic information. Finally, the proposed method is validated by a case study of selecting optimal green enterprises. Some comparative analyses are given to show the efficiency of the proposed method.