Risk Preferences Of Decision Makers Research Articles

Value Evaluation Model of Multi-Temporal Energy Storage for Flexibility Provision in Microgrids

With the advancement of distributed power generation technology and the deepening of the low-carbon transformation of energy structure, a high proportion of renewable energy has become an inevitable trend in future energy systems, especially for microgrids. However, the volatility and uncertainty associated with renewable energy pose significant challenges to the secure and stable operation of power systems, necessitating the exploration of the flexible regulation of resources. Energy storage, as a crucial flexible resource characterized by technological diversity and a variety of regulation capabilities, has been extensively studied and applied. Nonetheless, the high investment costs and limited returns of energy storage technology, coupled with the ambiguous utility in different scenarios under the current electricity market’s framework, complicate its broader application. To thoroughly analyze the utility of energy storage in facilitating flexible adjustments in microgrids, this study developed a composite weight-TODIM (an acronym in Portuguese for interactive and multi-criteria decision making) model for assessing the utility of energy storage that incorporates heterogeneity in the risk preferences. This model enabled a comparative analysis of the utility of energy storage technology across multiple scenarios, taking the risk preferences of decision-makers into account, thereby providing strategic insights for the application of multi-temporal energy storage in microgrids. The feasibility and effectiveness of the model were validated through a case study analysis.

A supplier selection method based on cobweb similarity and prospect theory for prefabricated components

Selecting suppliers for prefabricated components (PCs) involves a complex decision-making process, frequently relying on ambiguous information and subjective judgment. However, most existing methods use precise values to portray indicator information and overlook the uncertainty of weights and the subjective preferences of decision-makers (DMs). In order to address these limits, this paper proposes a novel approach to select suppliers of PCs. Initially, an evaluation index system for suppliers is established through literature analysis and a questionnaire survey. The system comprises six layers: product quality, price, service level, comprehensive ability, supply ability, and environmental sustainability. The group decision matrix is then constructed using the set-valued statistical method and the prospect theory. The index weights are determined by a combination weighting method. Next, the cobweb model is introduced to analyze the disparity between the alternative and ideal solutions, describing their similarities in terms of area and shape. Lastly, cobweb similarity is employed instead of comprehensive distance, combined with the minimum sum of squares criterion, to improve the closeness algorithm and contrast the alternatives. The results demonstrate that this method facilitates a comprehensive evaluation of the benefits and drawbacks of various alternatives from diverse perspectives. Furthermore, it allows flexible adjustments based on the risk preferences of DMs, ensuring accurate and reliable decision results.

Data-driven typhoon case retrieval method based on spatiotemporal trajectory similarity and risk preferences of decision-makers

The results of typhoon case retrieval are important for decision-making during emergency situations. Therefore, this study proposes a new typhoon case retrieval method to improve the accuracy of the retrieval results based on the spatiotemporal trajectory similarity and the risk preferences of decision-makers. First, a spatiotemporal trajectory similarity measurement is proposed to generate the path similarity case set. Next, attribute similarity and case similarity measurements are applied to obtain the set of similar cases. Subsequently, a group decision-making model based on fuzzy evidence reasoning is constructed to obtain the risk utility value according to the assessment information and risk preferences of the decision-makers. The risk utility and case similarity are aggregated to obtain the comprehensive utility value and the most appropriate historical typhoon case, thereby generating the emergency alternative. Finally, the effectiveness and feasibility of the proposed method are demonstrated using a typhoon disaster example. The advantages of the proposed method are illustrated through comparative analyses with other methods.

Research on the decision-making of work safety investment in industrial park enterprises: evidence from behavioral experiments.

Due to the clustering of risk factors, industrial park safety accidents can easily trigger a domino effect. Work safety investment is the foundation of enterprise work safety in industrial parks. Therefore, increasing the work safety investment of enterprises in industrial parks is the key condition to prevent accidents. However, due to the typical negative externalities of industrial park work safety accidents, the decision-making process of work safety in park enterprises is influenced by other enterprises within the park, including imitation behaviors. This makes the decision-making of work safety in park enterprises very specific. In order to clarify the influencing factors and effects of work safety investment in industrial park enterprises, this study uses a behavioral experiment method and conducts decision-making experiments using the experimental platform O-Tree. The study recruits 76 participants who play the role of decision-makers in park enterprises. This study uses a lottery price experiment and a dictator experiment to measure the risk preference and altruism preference of the participants, respectively. The study introduces the real background of work safety investment in industrial park enterprises and collects data on work safety investment by the participants in different experimental scenarios. The research results show that the safety attitudes of decision-makers, altruism preference, accident experience, government work safety supervision, park management measures, and safety benefits positively influence work safety investment in park enterprises. The risk preference of decision-makers and the resource capability of work safety negatively influence work safety investment in park enterprises. Work safety investment in park enterprises is influenced by the work safety investment of other enterprises within the park.

Research on decision analysis with CVaR for supply chain finance based on partial credit guarantee and blockchain technology

AbstractConsidering the influence of decision maker's risk preference degree on decision making, this paper employs CVaR risk measure criterion to facilitate decision making. The optimal decision making of supply chain finance under partial credit guarantee and blockchain technology empowerment is investigated. In addition, the CVaR decision models for supply chain finance under two scenarios of partial credit guarantee and blockchain technology empowerment are constructed. The inverse solving is conducted with the help of the Stackelberg model to find out the optimal ordering quantity, wholesale price, and interest rate. The influencing factors of decision making are investigated. This study shows that, within a certain risk‐averse range, the ordering quantity of retailers under the partial credit guarantee model is larger than that under the blockchain technology‐enabled model. In contrast, the conditional risk value of suppliers under the partial credit guarantee model is much smaller than that under the blockchain technology‐enabled model. In conclusion, the blockchain technology is beneficial to reduce supply chain finance risks.

An investment decision framework for offshore wind-solar-seawater pumped storage power project under interval-valued Pythagorean fuzzy environment

Offshore wind-solar-seawater pumped storage (wind-PV-SPS) power system will be a very competitive offshore new energy project in the future because it can realize the complementarities of wind and photovoltaic resources in the dimensions of time and space, and reduce the waste of resources caused by voltage instability. In order to promote the sustainable development of offshore wind-PV-SPS system, it is particularly important to construct a scientific and reasonable investment decision-making framework. Firstly, a comprehensive criteria system is established in this paper, which includes economy, resource, environment, supporting conditions and market. Secondly, the interval-valued Pythagorean fuzzy set (IVPFS) is introduced to describe the uncertainty and fuzziness of information in the process of investment decision, and the expert weight correction model is established based on the similarity of IVPFSs. At the same time, IVPF-DEMATEL method is utilized to determine the weight of criteria, which takes the interaction between criteria into account. In addition, considering the decision-maker's risk preference psychology, we introduce the GOWUA-HARA operator into the PROMETHEE II method, and extend it to the interval-valued Pythagorean fuzzy environment to choose the optimal investment alternative. Finally, the proposed framework is applied to a case study. Then sensitivity analysis and comparative analysis are carried out to verify its feasibility and robustness.

A Three-Way Decision Methodology With Regret Theory via Triangular Fuzzy Numbers in Incomplete Multiscale Decision Information Systems

The three-way decision theory provides a three-way philosophical thinking to solve problems, and the regret theory quantifies the risk preferences of decision makers under different psychological behaviors. On the one hand, the combination of these two theories makes models more practical by considering the psychological behaviors of decision makers. On the other hand, we can effectively combine the advantages of the three-way decision theory with the regret theory to highlight the interpretability of decision making processes. In this paper, we propose a novel approximate estimation method for incomplete utility values via the regret theory and establish a wide sense of three-way decision model on incomplete multi-scale decision information systems. First, the degree of consistency for each scale is measured via using the dependency degree, then the optimal subsystem is selected by evaluating the scale selection cost. Further, the incomplete multi-scale evaluation information is transformed into triangular fuzzy numbers via linguistic term sets. Second, in light of fuzzy evaluation values and trade-off factors, an estimation method for incomplete fuzzy subsystems is constructed, which can be used to calculate the utility difference and regret-rejoicing values for pairwise comparisons. Finally, from the perspective of human cognition, the tri-partition and the corresponding decision rules are built by the tolerance degree, and the ranking of objects is calculated by the relative closeness degree. Additionally, multi-aspect comparative and experimental analyses are performed by extensive experiments, and the feasibility, validity and stability of the constructed model are shown by parametric analyses.

Comprehensive performance assessment of energy storage systems for various application scenarios based on fuzzy group multi criteria decision making considering risk preferences

The energy storage (ES) is an indispensable flexible resource for green and low-carbon transformation of energy system. However, ES application scenarios are complex. Therefore, scientifically assessing the applicability of different energy storage systems in various scenarios is prominent for the development of ES industry. This paper proposes an integrated fuzzy-GMCDM (group multi criteria decision making) method for achieving the above objective. In the proposed method, the comprehensive performance assessment index system consists of 10 indicators from the perspectives of technology, economy, efficiency and environment based on fuzzy Delphi model. The optimal aggregation weights can reflect the total judgments of all experts are determined by Bayesian Best-Worst method. Fuzzy cumulative prospect theory is employed to maximize the use of objective data, and prioritize the rankings of alternatives under various scenarios considering risk preferences of decision-makers. The results show that this method can provide decision-makers with the optimal solutions for ES selection in various scenarios such as renewable energy consumption and ancillary services. Furthermore, risk preferences with respect to the 4 aspects, affect the optimal choice and the rankings of ES systems, especially for the flywheel ES, sodium‑sulfur battery and vanadium redox flow battery.

A comparative study on a firm’s optimal production decision considering risk aversion and carbon policies

To cope with global warming and achieve the goal of carbon emission reduction, various carbon policies have been implemented in many countries and regions. Some studies have indicated that with the popularity of low carbon, a small proportion of consumers show a green purchasing tendency. However, for firms, it is still difficult to predict the green purchase preferences of most consumers, and firms may face greater market demand uncertainty, which will bring profit risks. We assume that a firm’s decision is subject to its behavioral considerations, that is, its risk attitude. Considering the risk preferences of decision-makers and carbon emission policies, this paper addresses decision optimization in operations management. Our purpose is to analyze the optimal production decisions of risk-averse firms under carbon cap and carbon tax policies. Adopting the CVaR (conditional value-at-risk) criterion, we construct an optimization model with the constraints of carbon policies and obtain the analytic formula of optimal solutions. Then, we compare the optimal operational decisions for risk-averse firms and risk-neutral firms and further explore the impact of risk aversion on production and the parameters of carbon policy. Finally, some numerical studies are presented to analyze the carbon policies and risk aversion on firms’ operational decisions and give some managerial insights based on the analysis results.

Cognitive Mapping and Framing Bias on Decision Making

The purpose of this study is to test and provide empirical evidence regarding causal cognitive mapping techniques in reducing framing bias in decision making. In this study, the bias that occurs can be measured through the risk preferences of decision makers, namely the tendency to be risk averse or risk seeking. This study used a 2x2 between subjects’ experimental design. The results of the research show that both hypotheses are supported. When information is presented in positive framing, the decision maker's risk preference tends to be risk averse, whereas if information is presented in negative framing, the decision maker's risk preference tends to be risk seeking. The study shows that the causal cognitive mapping technique's ability to reduce or reduce bias due to framing (information framing) of available decision alternatives. The implication is that decision makers should think harder in processing the available information. Causal cognitive mapping technique can assist decision makers in connecting various factors that are considered relevant in a problem.

Hesitant fuzzy β-covering ( T , I ) rough set models: An application to multi-attribute decision-making

Covering rough sets have been successfully applied to decision analysis because of the strong representing capability for uncertain information. As a research hotspot in decision analysis, hesitant fuzzy multi-attribute decision-making (HFMADM) has received increasing attention. However, the existing covering rough sets cannot handle hesitant fuzzy information, which limits its application. To tackle this problem, we set forth hesitant fuzzy β-covering rough set models and discuss their application to HFMADM. Specifically, we first construct four types of hesitant fuzzy β-covering ( T , I ) rough set models via hesitant fuzzy logic operators and hesitant fuzzy β-neighborhoods, which can handle hesitant fuzzy information without requiring any prior knowledge other than the data sets. Then, some intriguing properties of these models and their relationships are also discussed. In addition, we design a new method to deal with HFMADM problems by combining the merits of the proposed models and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. In this method, we not only consider the risk preferences of decision-makers, but also present a new hesitant fuzzy similarity measure expressed by hesitant fuzzy elements to measure the degree of closeness between two alternatives. Finally, an enterprise project investment problem is applied to illustrate the feasibility of our proposed method. Meanwhile, the stability and effectiveness of our proposed method are also verified by sensitivity and comparative analyses.

Optimal Location of Emergency Facility Sites for Railway Dangerous Goods Transportation under Uncertain Conditions

Railroad accidents involving dangerous goods (DG) need to be rescued quickly due to their hazardous nature. This paper proposes an emergency facility location model for the railway dangerous-goods transportation problem (RDGT-EFLP, abbreviated as EFLP). The EFLP model is based on an ellipsoidal robust model that introduces a robust control safety parameter Ω to measure the risk preferences of decision makers and limits the range of uncertain demand, the range of uncertain service and the range of safety parameters to find the solution for siting emergency facilities, when the time and location of emergency events are unknown. The model is solved using a genetic algorithm (GA) and real data after abstraction. Finally, a comprehensive analysis of the solution results under different maximum overcoverages illustrates the feasibility and effectiveness of the model.

MULTIMOORA Method-Based Schweizer–Sklar Operations for CO2 Geological Storage Site Selection Under Pythagorean Fuzzy Environment

The site selection of CO2 geological storage facilities is essential for the development of safe and efficient carbon capture, utilization, and storage (CCUS) projects. Normally, CO2 geological storage site selection can be regarded as a complex multi-criteria decision-making (MCDM) problem. The aim of this paper is to present an integrated decision-making method for solving the site selection problem for CO2 geological storage. To achieve this goal, this method is based on multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method and prioritized aggregation operators in Pythagorean fuzzy environment. The academic contributions of this study include: first, some Pythagorean fuzzy Schweizer–Sklar prioritized aggregation (PFSSPA) operators are proposed, which take into account the priority levels of criteria and the risk preferences of decision makers. The excellent properties of these operators are given. Then this study extends the classical MULTIMOORA method based on the developed aggregation operators (named PFSSPA-MULTIMOORA), and the calculation process of this method is described in detail. Subsequently, on the basis of the constructed criteria system, the PFSSPA-MULTIMOORA method is applied to rank the alternatives. Finally, we successfully utilized the PFSSPA-MULTIMOORA method to solve the site selection problem of CO2 geological storage in China. A comparative analysis of existing methods verifies the effectiveness and robustness of the proposed method. This work can provide advanced decision support for researchers and practitioners.

A Choquet integral-based TODIM method for q-rung trapezoidal fuzzy numbers and its application in group decision-making

PurposeThe purpose of this paper is to develop a multi-attribute group decision-making (MAGDM) method under the q-rung orthopair trapezoidal fuzzy environment, which calculates the interaction between the criteria depending on the proposed q-rung orthopair trapezoidal fuzzy aggregation Choquet integral (q-ROTrFACI) and employ TODIM (an acronym in Portuguese of Interactive and Multi-criteria Decision Making) to consider the risk psychology of decision-makers, to determine the optimal ranking of alternatives.Design/methodology/approachIn MAGDM, q-rung orthopair trapezoidal fuzzy numbers (q-ROTrFNs) are efficient to indicate the quantitative vagueness of decision-makers. The q-ROTrFACI operator is defined and some properties are proved. Then, a novel similarity measure is developed by fusing the area and coordinates of the q-rung orthopair trapezoidal fuzzy function. Based on the above, a Choquet integral-based TODIM (CI-TODIM) method to consider the risk psychology of decision-makers is proposed and two cases are provided to prove superiority of the method.FindingsThe paper investigates q-ROTrFACI operator to productively solve problems with interdependent criteria. Then, an approach is proposed to determine the center point of q--ROTrFNs and a q-rung orthopair trapezoidal fuzzy similarity is constructed. Furthermore, CI-TODIM method is devised based on the proposed q-ROTrFACI operator and similarity in q-rung orthopair trapezoidal fuzzy context. The illustration example of business models' solutions and hypertension health management are given to demonstrate the effectiveness and superiority of proposed method.Originality/valueThe paper develops a novel CI-TODIM method that effectively solves the MAGDM problems under the premise of fully considering the priority of criteria and the risk preference of decision-makers, which provides guiding advantages for practical decision-making and enriches the application of decision-making theory.

A dynamic failure mode and effects analysis for train systems failures risk assessment using FCM and prospect theory

Improving the reliability of railway train systems and preventing potential failures in the train operation process is one of the most significant tasks. The failure mode and effects analysis (FMEA) is the most effective and widely applied technique for identification, evaluation, and prevention risk of potential failures in diverse fields. Nevertheless, current risk prioritization approaches for FMEA overlook the transfer of decision makers’ risk preferences under different risk states of potential failures. In addition, little attrition has been paid to addressing the risk prioritization problems in FMEA under a dynamic environment. In order to bridge these research gaps, this paper proposes a dynamic prioritization approach for FMEA by integrating the Fuzzy Cognitive Map (FCM) and the prospect theory. First, improved weighted arithmetic averaging (WAA) operator based on the similarity measure is constructed to aggregate each decision maker’s evaluation information. Then, the FCM is applied to obtain the risk matrix and interaction relationships among failures under different risk states. Next, the dynamic prospect theory is built to determine the risk priority of each failure by considering the risk preference of decision makers, in which the dynamic weight functions are derived based on the risk matrix under different risk states. Finally, the proposed dynamic risk prioritization approach for FMEA is tested by the failures risk analysis of the railway train bogie system in the railway train systems. The comparison study is conducted to demonstrate the reliability and rationality of the proposed risk prioritization approach.

Site selection for underground pumped storage plant using abandoned coal mine through a hybrid multi-criteria decision-making framework under the fuzzy environment: A case in China

The development of underground pumped storage plant using abandoned coal mine (UPSP-ACM) has a significance to abandoned coal mine resources utilization and energy storage industry. The article studies on site selection of UPSP-ACM and proposes a decision framework to determine the optimal location based on the theory of multi-criteria decision making (MCDM). Firstly, 15 evaluation criteria from resources and environment (R&E), investment and benefits (I&B), construction and technology (C&T), society and planning (S&P) four aspects are identified in the evaluation index system. Then, triangular intuitionistic fuzzy number (TIFN) is used to transform the information of qualitative index into the quantitative, and the game combined weight is used to analyze criteria’ importance. The result shows that average gross head with 0.149 weight is the most important among criteria, and the combined weight method can avoid the bias of single weight method. Further, TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) is used to rank the alternatives, which takes the decision maker's risk preference into account. Finally, the feasibility of decision framework testified through a case study in Shanxi province, and its stability and applicability are verified by sensitivity analysis and comparative analysis. Lastly, some suggestions to help the UPSP-ACM site selection are put forward based on the evaluation criteria. The research of article can provide a reference to person who is engaged in management decision research and UPSP-ACM site selection.

A hybrid multi-criteria decision-making framework for offshore wind turbine selection: A case study in China

The proper selection of offshore wind turbines is crucial for the long-term development of offshore wind farms. It also contributes to achieving the long-term sustainability goals of offshore wind power. To select the optimal offshore wind turbine with massive difficulties lying in diversity of evaluation criteria, uncertainties in the decision environment and different risk preferences of decision makers, a hybrid multi-criteria decision-making (MCDM) framework is proposed. This framework integrates the interval 2-tuple linguistic (I2TL) model, power weighted average (PWA) operator, stepwise weighted assessment ratio analysis II (SWARA II), method based on the removal effects of criteria (MEREC), cumulative prospect theory (CPT) and combined compromise solution (CoCoSo) method. First, a more realistic and holistic evaluation criteria system is developed for wind turbines based on a two-phase procedure. Then, the I2TL variables are employed to transform the subjective judgments of experts into quantitative evaluation information, and the individual evaluations are fused by the similarity measure-based PWA operator. After that, a new integrated weighing method combined with SWARA II and MEREC is adopted to acquire the criterion weights. Next, the CoCoSo method based on CPT is applied to rank alternatives. An actual case study in China is conducted to demonstrate the applicability of the proposed framework. Finally, the robustness and reliability of the framework are validated through sensitivity and comparison analysis. The hybrid MCDM framework can provide a beneficial reference for decision makers to analyze and select the optimal wind turbine.

Optimizing Joint Decisions of Dynamic Pricing and Ticket Allocation for High-Speed Railway with Operators’ Risk Preference

High-speed trains powered by electricity have low-carbon emissions and are the important intercity travel conveyances conducive to sustainable social development. The sustainable development of high-speed railway needs to improve the operating revenue and the resource utilization. Joint optimization of dynamic pricing and ticket allocation are often used to improve high-speed railway revenue and seat utilization in the literature. However, the uncertainty of demand makes joint decision-making difficult to be accurate. The risk preference of decision-makers also deeply affects the effect of joint decision-making. To cope with the uncertain demand and test the effect of operator risk preference on joint decision-making, this paper used chance constrained programming theory to optimize dynamic pricing and ticket allocation simultaneously. The presale period is divided into several stages. In each stage, ticket demand follows the normal distribution and changes with the price elasticity. Passengers choose tickets according to the multinomial logit model. The chance constrains reflect operators’ risk preference. The chance constrained stochastic nonlinear programming is proposed for joint decision of dynamic pricing and ticket allocation. Then, the combination algorithm of particle swarm optimization algorithm and mixed-integer linear programming was designed to solve the model. Finally, the numerical experiments according to actual operating scale were design to validate the model and algorithm. The results indicate that under different confidence levels, the proposed model and algorithm increase the total revenue by 11.84%-13.40% compared with the ticket allocation under the single fixed fare. The model can help the high-speed railway operators understand the impact of risk preference on joint decision-making, and provide decision support for them.

A Hybrid Cleaning Scheduling Framework for Operations and Maintenance of Photovoltaic Systems

Dust deposition on the surface of photovoltaic (PV) modules is a nonnegligible factor that reduces a PV system’s efficiency and reliability. Cleaning can remove dust, and the effect of cleaning on PV performance resembles that of maintenance. In this article, we propose a hybrid cleaning scheduling policy with periodic planning and dynamic adjustment for refining the operations and maintenance of PV systems. Specifically, the periodic planning stage aims for medium-term scheduling while the dynamic adjustment stage is tailed for short-term fine-tuning. In the former stage, we show that when the number of cleaning actions is fixed, a periodic cleaning strategy is optimal. Moreover, we derive the optimality condition under which the optimal cleaning interval can be determined. In the latter stage, based on the determined cleaning interval, we dynamically adjust the cleaning schedule with the forecast of meteorological parameters, PV power generation, and dust deposition in order to further minimize economic losses. In addition, we take the forecasting uncertainty into account and propose a new custom parameter called risk-taking tendency (RTT), which is able to quantify the risk preference of decision makers and analyze its influence on the scheduling policy. A case study is provided to illustrate the proposed strategy.

Interval-valued intuitionistic fuzzy multi-attribute group decision-making method considering risk preference of decision-makers and its application

An improved interval-valued intuitionistic fuzzy multi-attribute group decision-making method considering the risk preference of decision-makers is proposed to solve the multi-attribute group decision-making problem with interval-valued intuitionistic fuzzy numbers and the condition that the attribute weight information is completely unknown. Firstly, the decision-maker weight of each attribute is determined by combining similarity and proximity. In order to consider the influence of the decision-maker's risk preference on the decision result and avoid the asymptotic behavior of interval-valued intuitionistic fuzzy matrix, the risk aversion coefficient of the decision-maker is introduced and combined with the determined decision-maker's weight aggregation to form a group decision matrix. Then, the information of group decision matrix is mined, and the interval-valued intuitionistic fuzzy entropy is used to determine the attribute weight and relative weight. Based on the interval-valued intuitionistic fuzzy distance measure formula and the TODIM method, the overall superiority of each scheme relative to other schemes is obtained by calculating the superiority between schemes, and the optimal scheme is determined by comparing and sequencing. Finally, the rationality and effectiveness of the proposed method are verified by an example of mechanical assembly supplier selection decision.