Complementary Judgment Matrix Research Articles

Comprehensive Performance Evaluation of Earthquake Search and Rescue Robots Based on Improved FAHP and Radar Chart

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for an evaluation method that quantifies their performance and facilitates the selection of rescue robots with optimal overall capabilities. In this paper, twenty-two evaluation criteria are proposed based on a comprehensive review of existing evaluation criteria for rescue robots across various domains. The evaluation criteria are tested using the test modules developed by the National Earthquake Response support service, obtaining the corresponding values for each criterion. Then, the weights of the criterion layer and comprehensive evaluation index are determined based on the analytical hierarch process and trapezoidal fuzzy number complementary judgment matrix, and a new consistency test method is proposed. The qualitative evaluation and quantitative analysis are effectively combined to overcome the subjective influence of expert decision-making. Additionally, the performance of three earthquake search and rescue robots is comprehensively evaluated and ranked using the improved radar chart method as an empirical example. Finally, the robustness of the ranking results is examined using a weight sensitivity analysis. The results of the sensitivity analysis demonstrate the effectiveness and feasibility of the proposed method, thereby providing valuable insights for developing multi-objective optimization control strategies and structural designs for earthquake search and rescue robots.

The Customer Requirements Analysis Method of Engineering Products Based on Multiple Preference Information

To effectively evaluate the fuzziness of the market environment in product planning, a customer requirements analysis method based on multiple preference information is proposed. Firstly, decision-makers use a preferred information form to evaluate the importance of each customer requirement. Secondly, a transfer function is employed to unify various forms of preference information into a fuzzy complementary judgment matrix. The ranking vector is then calculated using row and normalization methods, and the initial importance of customer requirements is obtained by aggregating the weights of decision members. Finally, the correction coefficients of initial importance and each demand are synthesized, and the importance of customer requirements is determined through normalization. The development example of the PE jaw crusher demonstrates the effectiveness and feasibility of the proposed method.

Research on uncertain multiple attribute decision making based on improved possibility degree relation model and its application

In this paper, we mainly focus on the triangular fuzzy number-based uncertain multiple attribute decision making problem, which not only attribute weights are unknown but also preference for decision-making objects do not exist, and give a new decision-making approach and consistency test method based on the improved possibility relation model. This method fully considers the influence of the comparison possibility degree measurement information between the measured values of the indicator attribute itself in the course of processing uncertain reasoning such as fuzziness and randomness, reconstructs a new and more optimized triangular fuzzy number-based decision making object improved possibility degree relation model (TFN-DMOIPDRM) to determine the attribute weight vector, and creatively designs a general formula to test the consistency of the improved possibility degree relation matrix. Next the criterion of consistency test is given to solve the remaining problem of the lack of consistency test in the construction of fuzzy number possibility degree relation model and fuzzy complementary judgment matrix. Moreover, another major contribution of this paper is to present the improved possibility degree relation theory of triangular fuzzy numbers on the basis of defining the comparative possibility degree of triangular fuzzy numbers, and fully study a series of its relevant properties and conclusions, as well as its application in constructing model to determine the attribute weight. By aggregating the improved possibility degree information measured by pairwise comparing decision objects, the overall improved possibility degree value of each decision object is obtained, and a new algorithm of the improved possibility relation model for triangular fuzzy number multi-attribute decision object is given, which improves and expands the application of fuzzy number possibility theory in the field of fuzzy uncertainty reasoning and intelligent computing of artificial intelligence.

Rapid determination of fuzzy number in FAHP and assessment risk in coal mine roof fall

This study proposed a rapid approach to determine the fuzzy number to evaluate roof fall risk in the coal mine. In risk assessments by fuzzy theory, the determination of fuzzy numbers using triangular fuzzy numbers (TFN) incorporated into the analytic hierarchy process is a difficult and time-consuming task. A novel approach to determine fuzzy number in TFN was proposed. To reduce occurrence of roof fall accidents, it is necessary to identify the main risk factors in roof fall risk assessments. A nine-score table-type questionnaire was adopted to collected expert judgement, based on which language scales were determined, ranging from 0.1 to 0.9, with increments of 0.1. Each language scale corresponded to the selected value of the experts. Subsequently, a triangular fuzzy complementary judgement matrix and a consistent possibility judgement matrix were constructed. The effectiveness of this proposed method was verified via analysing a case of roof collapse at Chaidar coal mine, Qinghai Province, China. The final ranking results of the risk factors were consistent with those observed in the field.

Research on construction schedule risk management of power supply and distribution projects based on MCS-AHP model

In order to manage the construction schedule risk of power supply and distribution engineering, a construction schedule risk evaluation model, namely the Monte Carlo simulation method - Analytic Hierarchy Process (MCS-AHP) model, is proposed. In this model, the Monte Carlo simulation method is adopted to improve the analytic Hierarchy Process (AHP), and the normal distribution interval is used to replace the specific value when constructing the fuzzy complementary judgment matrix, to reduce the risk of fuzzy thinking and incomplete information or scattered data in the process of investigation and judgment and improve the scientific evaluation. This paper takes a power supply and distribution project in Guangdong Province as an example uses the MCS-AHP model to measure the key factors limiting the project progress, and uses the AHP method for comparative analysis, to verify the feasibility of the MCS-AHP model. The analysis shows that the key influencing factors are material and equipment procurement, production and arrival, installation of 10 kv high voltage switchboard, electrical acceptance and single machine commissioning, installation of low-voltage switchboard and DC switchboard, and foundation construction of power station equipment, etc., which are consistent with the actual situation. Therefore, it is feasible to construct the MCS-AHP model, which can provide a new way of thinking for schedule risk management analysis.

Integrating framework analysis, scenario design, and decision support system for sustainable healthy food system analysis

In the past two decades, a number of researchers have sought to capture the complex interactions of the food system's outcome through frameworks. In several international discourses and expert foresight studies, food system frameworks are frequently used to argue specific approaches and reforms that seek to reduce the multiple burdens of our food system on humans and the planet. To date, policymakers and other stakeholders have been forced to contend with many threats and uncertainties stemming from different dimensions of our food system. While many frameworks for evaluating food system objectives exist, making context-specific choices that reflect current or future situations accounting for various dimensions of the food system is daunting. Therefore, this study examines food system frameworks considering sustainability dimensions, constructs four-styled future food system scenarios using scenario design technique, and develops a decision support system that (1) determines relevant drivers and framework to evaluate predetermined future food system (2) allows the co-creation of the specific context food system and still identify relevant drivers and frameworks. Thus, providing a more selective use of food system frameworks which provides stakeholders with insights to take necessary pre-emptive actions or interventions and make strategic decisions toward potential shocks and uncertainties in such futures. To achieve this, the Complementary Judgment Matrix method was used to translate the preference of ten participants to weights of importance regarding sustainability dimensions, then propagated onto the Fuzzy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision model to evaluate frameworks for each dimension. Finally, the results were integrated using scenario design and digital intelligence to develop a novel decision support system. The findings showed that frameworks are skewed toward food and nutrition, with less economic consideration. Also, the scenarios presented provide an invaluable lens to the future of our food system. Furthermore, the proposed decision support system allows food system stakeholders to navigate the constructed scenarios and re-create plausible future scenarios under different contexts. This study provides an exciting opportunity for future policymakers and scholars at different spatio-temporal levels to design and implement policies that will strengthen and render the food system resilient in front of future adversities.

Risk Assessment Study on Mechanical Connecting Aisle Construction in the Bohai Mudstone Stratum with High Water Pressure

With the in-depth advancement of underground transportation in China, the connecting aisle is used as a link between different underground spaces to provide sufficient security and connection. The basic structural units of underground and aboveground spaces play an extremely important role. The use of the mechanical connecting aisle construction of over mucky strata is difficult, and it is easy to have an impact on the main tunnel. To ensure the safety of connecting aisle construction, studies should be performed on the construction technology, equipment, scheme, and related risk analysis of mechanical connecting aisle construction. In this study the work breakdown structure-risk breakdown structure combined with the expert investigation method is used to comprehensively identify the construction process, construct the risk index system of mechanical connecting aisle construction, determine the initial risk value, and comprehensively use the fuzzy-analytic hierarchical process to establish a fuzzy complementary judgment matrix, obtain the risk-weighted correction value, and quantitatively evaluate the risk source level of the mechanical connecting aisle construction. Based on the assessment method, the risk assessment of the mechanical connecting aisle construction project in Daqing section of Qingdao Metro Line 8 was performed, 113 potential risk sources were identified, and the comprehensive assessment obtained 7 major risk sources such as “improperly designed construction organization,” “instability of excavation surface,” and “easy leakage of lower river crossing” that were greater than the total risk average of 2.053. Corresponding risk response measures were proposed, which provided a reference for the risk assessment of similar projects in the construction of similar projects on mechanical connecting aisle construction in the Bohai mudstone stratum with high water pressure.

A new three-way multi-criteria decision-making method with fuzzy complementary preference relations based on additive consistency

The optimal selection, ranking and classification of objects are important aspects in the multi-criteria decision-making (MCDM) problem. In this paper, we try to propose a three-way MCDM method with fuzzy complementary preference relation based on additive consistency to select the optimal object, rank objects and classify objects at the same time, which will help decision-makers better make decisions. Firstly, we give the fuzzy complementary judgment matrix based on additive consistency. Then, based on this matrix, we construct the concept of ξ-similarity class, and define a new relative loss function. Subsequently, we describe the detailed decision-making processes of the newly proposed three-way MCDM method. Further, through the trainer selection problem, we verify the feasibility of the method. Comparative analyses show that our method has stronger decision-making function than some existing methods. Experimental analyses show that our method is stable in ranking the considered objects and selecting the optimal object. Moreover, the proposed method can not only provide reasonable ranking decision suggestion for decision-makers, but also can meet the decision-makers’ preference in the classification decision.

A Consensus-Reaching Approach to the Evaluation of Product Design Alternatives with Multiple Preference Structures.

With interdisciplinarity being an important characteristic of contemporary product design, the evaluation of design alternatives also involves multiple disciplines, and the evaluator group usually consists of evaluators from different fields and with obvious heterogeneous characteristics. To effectively satisfy the heterogeneous needs of evaluators and improve the credibility of evaluation results, the paper introduces a consensus-reaching approach that incorporates multiple preferences to the evaluation of product design alternatives. First, in order to obtain individual preference information, each evaluator is asked to evaluate all the design alternatives using a preference structure that he/she is familiar with. Second, we use a transfer function to uniform the evaluation information obtained from various preference structures into a complementary judgment matrix. Then, we use the Hybrid Weighted Averaging (HWA) operator weight determination model to aggregate the preference information and obtain the group preference information. Then, we measure the consensus degree between individual evaluators and the group using a consensus measurement method. After that, we use the feedback mechanism to instruct individual evaluators to modify their preferences until a consensus is achieved. We explain the application steps and the feasibility of this approach through the evaluation of the design alternatives of multichannel fluorescence immunochromatography analyzers (MFIAs).

Cloud Trust-Driven Hierarchical Sharing Method of Internet of Things Information Resources

Short information sharing time is one of the problems to be solved in the traditional Internet. Therefore, this paper proposes a hierarchical simulation of the Internet of Things sharing structure framework that trusts the cloud to drive Internet information resource sharing. By setting thresholds and iterative adjustment parameters, a complementary judgment matrix is constructed to obtain the minimum nonnegative deviation value and the optimal weight vector. This paper sorts according to the value of the Internet information security model, obtains the optimal model to avoid human tampering, and designs the information resource acquisition process to ensure the reliability of the source data. We use radio frequency identification (RFID) equipment in the preprocessing of massive heterogeneous data in the Internet of Things. In a network environment where resources are limited and heterogeneous are fully considered, the trust-based adaptive detection algorithm is used to evaluate the credibility of the trust-driven algorithm for hierarchical information resource sharing services in the cloud environment of the Internet of Things. We propose a cloud trust-driven hierarchical information resource sharing Internet information resource model. Firstly, the key characteristics of hierarchical information resource sharing are analyzed. Then, a hierarchical information resource sharing model was established by using specific constraints, trust steepness function, cloud trust evaluation criteria, and trust constraint coefficient. Finally, an example of IoT system is designed to verify the effectiveness of the model. Experimental results show that, compared with the traditional model or algorithm, this model has a good hierarchical sharing effect of the underlying resource information.

Evaluation of the intelligent logistics eco-index: Evidence from China

The evaluation of the intelligent logistics eco-index (ILEI) can comprehensively and objectively reflect the development status and the potential problems of the intelligent logistics ecological chain (ILEC). It can provide a reference for the optimization and improvement of the ecological chain. By analyzing and summarizing the relevant literature of the ILEC from 2010 to 2019, this paper constructs an evaluation system that consists of 3 level-1 indicators, 10 level-2 indicators and 24 level-3 indicators. Using the method based on hybrid numerical decision-making to evaluate the ILEI, it takes into account the situation in which the original data contain both interval and fixed-value data, objectively obtains the weight vector by using the improved interval deviation degree and scheme attribute deviation maximization thought, and finally establishes the possibility decision matrix and calculates the ILEI based on the sorting theory of fuzzy complementary judgment matrix. This method has significant advantages over the traditional TOPSIS method. Based on the real case from Qingdao Goodaymart Logistics Company (Goodaymart Logistics), this paper compares the ILEI values among three months in 2019, summarizes the development status, and offers suggestions to Goodaymart Logistics. This study provides an approach for constructing the ILEI to realize an objective evaluation of the development of the ILEC, so as to facilitate the intelligent logistics enterprises in improving the level of ecological development and promoting the healthy development of the ecological chain.

Risk Analysis and Crisis Management of Novel Coronavirus COVID-19

After the outbreak of new coronavirus pneumonia, all regions have responded to take measures. Emergency decision-making plays an important role, directly related to the safety of the people, and more deeply will affect the future of the country. In this paper, we try to use the wisdom of large groups which are distributed in the social network to deal with the relevant indicators of the group big data decision-making model under the maximum likelihood estimation of the complementary judgment matrix, to establish the epidemic risk assessment system of infectious diseases, and to realize the group decision-making and risk analysis based on the big data.

A novel outranking method for multiple criteria decision making with interval-valued Pythagorean fuzzy linguistic information

This paper aim to propose a novel outranking method based on interval-valued Pythagorean fuzzy linguistic set (IVPFLS) to estimate alternatives for decision makers. With the rapid increase of the complexity, the challenges of a group of decision makers, a high degree of uncertainty and conflicting criteria are associated with multiple criteria decision making (MCDM) problems and few studies for these. In this regard, the outranking-VIKOR (O-VIKOR) method with the construction of compromise solution considers “group utility” and “individual regret” in pairwise alternatives. To fully show the compromise outranking relation, the O-VIKOR method allows decision makers reach consensus by mutual concessions when the dominant position from alternatives is extant. Subsequently, we define the credibility formula that constitutes a complementary judgment matrix for devoting to ranking the alternatives more precision. When it is necessary to process the evaluation information of multiple decision makers, the interval-value Pythagorean fuzzy linguistic entropic induced ordered weighted averaging (IVPFLEIOWA) operator is proposed with entropic order-inducing variable which includes a new interval-value Pythagorean fuzzy linguistic entropy for measuring uncertainty to capture the interrelationship among the primary preference of decision makers. Finally, a case study on site selection problem for a manufacturer is presented to illustrate the efficiency of our proposed method and make comparative analysis with other methods.

Complementary Judgment Matrix Method with Imprecise Information for Multicriteria Decision-Making

The complementary judgment matrix (CJM) method is an MCDA (multicriteria decision aiding) method based on pairwise comparisons. As in AHP, the decision-maker (DM) can specify his/her preferences using pairwise comparisons, both between different criteria and between different alternatives with respect to each criterion. The DM specifies his/her preferences by allocating two nonnegative comparison values so that their sum is 1. We measure and pinpoint possible inconsistency by inconsistency errors. We also compare the consistency of CJM and AHP trough simulation. Because preference judgments are always more or less imprecise or uncertain, we introduce a way to represent the uncertainty through stochastic distributions, and a computational method to treat the uncertainty. As in Stochastic Multicriteria Acceptability Analysis (SMAA), we consider different uncertainty levels: precise comparisons, imprecise comparisons with a stochastic distribution, and missing comparisons between criteria. We compute rank acceptability indices for the alternatives, describing the probability of an alternative to obtain a given rank considering the level of uncertainty and study the influence of the uncertainty on the SMAA-CJM results.

Multi-criteria decision-making method based on dominance degree and BWM with probabilistic hesitant fuzzy information

In this paper, multi-criteria decision-making (MCDM) methods with probabilistic hesitant fuzzy information are proposed based on the dominance degree of probabilistic hesitant fuzzy elements (PHFEs) and best worst method (BWM). First, we discuss the probabilistic distribution function of PHFE and the dominance degree matrix between two PHFEs. The dominance degree matrix is constructed based on the probabilistic distribution function of PHFE, which can be characterized as a fuzzy complementary judgment matrix. Second, BWM is extended to fuzzy preference relations based on the constructed dominance degree matrix. Subsequently, an algorithm is designed for selecting the best and worst weight vectors, and then two models are developed based on additive consistency and multiplicative consistency of fuzzy preference relations to derive the criteria weights. In addition, an algorithm is presented to improve the consistency of the dominance degree matrix when a desired consistency level is not achieved. Finally, the selection of best investment company is provided as an example to demonstrate the feasibility and effectiveness of the proposed methods.

Developing a multicriteria decision support framework for CHP based combined district heating systems

CHP based combined district heating (DH) systems with gas-fired boilers for peak load shaving have higher energy and environmental efficiencies compared to DH systems supplied heat by heat only boilers. However, proper multicriteria decision making method is lacking for them. This paper is dedicated to develop a decision support framework from economy, energy, technology and environment viewpoints, in order to facilitate the planning/retrofitting of the combined DH systems. Firstly, the installation strategy of gas-fired boilers is introduced, and then combined heating alternatives to be addressed are constructed by choosing different base load ratios of CHP. Secondly, a criterion aggregation system is developed, based on which weights can be elicited using complementary judgment matrix (CJM) plus feasible weight space (FWS) methods. Thirdly, an application-oriented, multicriteria decision support framework is demonstrated in a real-life DH system in Daqing, China. Stochastic multicriteria acceptability analysis (SMAA) is implemented to synthetically handle the decision problem, which is characterized by incommensurable measurements, conflicting preferences, uncertainties and imprecise information. The results indicate that the developed framework works well in the multicriteria decision making for the combined district heating systems. The optimal base load ratio in the demonstration case is between 0.66 and 0.77 with high confidence.

The Application of FAHP in Decisions of Pavement Maintenance

In this paper, a method of building the fuzzy complementary judgement matrix and checking consistency is introduced based on the knowledge of the basic theory of FAHP and the procedure to establish the mathematical model corresponded. The scope and the advantages in the problems of multi-objective decisions have also been discussed. The availability of its use in the management system in pavement maintenance is demonstrated by analyzing the optimization for maintenance. Meanwhile, the faulty is also pointed out.

Evaluation of Restoration Sequence of DG in Black-Start

With the rapid expansion of the grid, how to solve the security and stability of power system has become a hot problem. As a power system after a serious accident the necessary security measures, black-start programs are expected to play an extremely important role in reducing the loss of equipment and user outage and decreasing the outage time. The present paper proposes a series of advantages about distributed generator (DG) and raises evaluation indexes of DG in black-start. The fuzzy hierarchical model is built based on the general steps of fuzzy decision theory. According to the concept of triangular fuzzy number, considering the indexes of which the weight value is judged by means of trapezoidal fuzzy number complementary judgment matrix. Finally, by actually case anglicizing, the paper demonstrates the scientific nature, effective and easy to operate of the method. And a new decision-making method for the optimal DG start-up sequences based on fuzzy AHP is proposed.

Stochastic Multicriteria Acceptability Analysis for Evaluation of Combined Heat and Power Units

Combined heat and power (CHP) is a promising technology that can contribute to energy efficiency and environmental protection. More CHP-based energy systems are planned for the future. This makes the evaluation and selection of CHP systems very important. In this paper, 16 CHP units representing different technologies are taken into account for multicriteria evaluation with respect to the end users’ requirements. These CHP technologies cover a wide range of power outputs and fuel types. They are evaluated from the energy, economy and environment (3E) points of view, specifically including the criteria of efficiency, investment cost, electricity cost, heat cost, CO2 production and footprint. Uncertainties and imprecision are common both in criteria measurements and weights, therefore the stochastic multicriteria acceptability analysis (SMAA) model is used in aiding this decision making problem. These uncertainties are treated better using a probability distribution function and Monte Carlo simulation in the model. Moreover, the idea of “feasible weight space (FWS)” which represents the union of all preference information from decision makers (DMs) is proposed. A complementary judgment matrix (CJM) is introduced to determine the FWS. It can be found that the idea of FWS plus CJM is well compatible with SMAA and thus make the evaluation reliable.

Network Security Risk Assessment Based on Fuzzy Analytical Hierarchy Process

This paper proposes an evaluation method based on fuzzy analytic hierarchy process (FAHP) for network security risk assessment. The paper analyzes the network security risk factors. By paired comparison of the importance of factors, the interval number fuzzy complementary judgment matrix is established to get the weight vectors of interval numbers of the factors. Then, by comparing the weight vectors of interval numbers, a possibility degree matrix is established, and then the weight coefficient of each factor are obtained. Finally the sorting result of the network security risk factors is presented. Instances show that the method is feasible and effective and it provides a reliable basis for security risk control strategy of network security staff.