Risk Preferences Of Decision Makers Research Articles

Research on Utility Evaluation of Grid Investment considering Risk Preference of Decision-Makers

Effectiveness evaluations are one of the important ways to guide grid investment and to improve investment efficiency. Improving the effectiveness of grid investment evaluations is studied based on the optimization of the investment evaluation index system and the utility evaluation model. The index system is optimized by establishing an evaluation index system of grid investment effectiveness, considering the redundancy between the indices, and constructing an ISM-DEA model. The utility function model was introduced to fully consider the different risk appetites of decision-makers, and a utility evaluation model that takes risk appetite into account was established. An improved weight integration model based on multiobjective optimization was established by considering the minimum deviation and the trend-optimal objective function when setting the index weights. The calculation results show that the feasibility of the index system optimization model and utility evaluation model constructed in this study is verified under the premise of satisfying the assumptions. By adjusting the risk preference coefficient of decision-makers, the dynamic optimization of the grid investment utility evaluation results can be realized.

A novel three-way decision method in a hybrid information system with images and its application in medical diagnosis

Three-way decisions are effective and heuristic methods in information processing, moreover, it provides a trisecting-and-acting framework for complex problem solving. In this paper, combining with the practical application scenario, we propose a novel three-way decisions approach and apply it to medical diagnosis. First, we build an information system which is called a hybrid information system with images by considering the characteristics of the examination items of nephritis, including urinary color, urinary tuberculosis, pH, red blood cell count, urinary irritation, computed tomography, white blood cell count and so on. Second, to describe two objects of the conditional attribute set in a hybrid information system with images, we propose the hybrid distance based on Euclidean distance. Then, the tolerance relation induced by this system is constructed. In addition, considering that missing values exist in a hybrid information system with images, interval-valued numbers are used to obtain the loss function. Given different types of parameters can respond the level of the tolerance relation and the risk preference of decision makers, and the decision rules are shown in tabular forms. Finally, an illustration is showed to verify the feasibility and reasonability of the proposed method.

Spatial conservation planning under uncertainty using modern portfolio theory and Nash bargaining solution

In recent years, researchers from interdisciplinary teams involving ecologists, economists and operations researchers collaborated to provide decision support tools to address the challenges of preserving biodiversity by optimizing the design of reserves. The goal of this paper is to further advance this area of research and provide new solutions to solve complex Spatial Conservation Planning (SCP) problems under uncertainty that consider risk preferences of decision makers. Our approach employs modern portfolio theory to address uncertainties in SCP problems, and involves two conflicting objectives: maximizing return and minimizing risk. We apply concepts from game theory such as the Nash bargaining solution to directly compute a desirable Pareto-optimal solution for the proposed bi-objective optimization formulation in natural resource management problems. We demonstrate with numerical examples that by directly computing a Nash bargaining solution, a Binary Quadratically Constrained Quadratic Program (BQCQP) can be solved. We show that our approach (implementable with commercial solvers such as CPLEX) can effectively solve the proposed BQCQP for much larger problems than previous approaches published in the ecological literature. Optimal solutions for problems with less than 400 parcels can be computed within a minute. Near optimal solutions (within at most 0.2% gap from an optimal solution) for high-dimensional problems involving up to 800 parcels can be computed within 8 h on a standard computer. We have presented a new approach to solve SCP optimization problems while considering uncertainty and risk tolerance of decision makers. Our new approach expands considerably the applicability of such SCP optimization methods to address real conservation problems.

Interval based transmission contingency-constrained unit commitment for integrated energy systems with high renewable penetration

As the reliance on natural gas to meet electric generation requirements increases, additional operational measures and risks must be considered to better understand the implications of the complex interdependency between the natural gas system and the power system. This paper develops a novel interval optimization framework of contingency-constrained unit commitment for integrated energy systems. Uncertainties in contingencies are captured by interval numbers, which reduces the computational burden compared with other uncertainty representations. Moreover, we incorporate the risk preferences of decision makers in the framework to alleviate the conservativeness of solutions. It is observed that power-to-gas technology can reduce renewable curtailment and offer auxiliary services. Numerical results demonstrate the economic value of power-to-gas in integrated energy systems.

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

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

Aggregating the DEA prospect cross-efficiency with an application to state key laboratories in China

With growing investments in the state key laboratories (SKLs) of China, it is essential to assess the efficiency of the administrative departments regarding managing the SKLs of China. However, few studies have been conducted about the evaluation of the performance of Chinese administrative departments in managing SKLs. To fill this gap, this paper investigates the performance within 22 SKL administrative departments in China, based on a data envelopment analysis cross-efficiency prospect aggregation approach incorporating the risk preference of decision maker. The empirical results show the major findings: (a) serious imbalance exists regarding the investment of 22 SKL administrative departments; (b) there are great differences among the performance of 22 SKL administrative departments; (c) some SKL administrative departments face an insufficient situation regarding the outputs; (d) different risk attitudes of decision maker have the significant impact on the evaluation results of the 22 SKL administrative departments. Based on these findings, we provide several policy suggestions for the development of SKLs in China.

Fuzzy belief propagation in constrained Bayesian networks with application to maintenance decisions

Bayesian networks have been widely applied to domains such as medical diagnosis, fault analysis, and preventative maintenance. In some applications, because of insufficient data and the complexity of the system, fuzzy parameters and additional constraints derived from expert knowledge can be used to enhance the Bayesian reasoning process. However, very few methods are capable of handling the belief propagation in constrained fuzzy Bayesian networks (CFBNs). This paper therefore develops an improved approach which addresses the inference problem through a max-min programming model. The proposed approach yields more reasonable inference results and with less computational effort. By integrating the probabilistic inference drawn from diverse sources of information with decision analysis considering a decision-maker's risk preference, a CFBN-based decision framework is presented for seeking optimal maintenance decisions in a risk-based environment. The effectiveness of the proposed framework is validated based on an application to a gas compressor maintenance decision problem.

The Extension of TOPSIS Method for Multi-Attribute Decision-Making With q-Rung Orthopair Hesitant Fuzzy Sets

As a combination of q-rung orthopair fuzzy sets (q-ROFSs) and hesitant fuzzy sets (HFSs), q-rung orthopair hesitant fuzzy sets (q-ROHFSs) are more effective, powerful, and meaningful in solving the complexity, ambiguity, and expert hesitancy of membership and non-membership in multi-attribute decision-making (MADM) problems. And so, based on the advantages of q-ROHFSs, we herein propose an improved TOPSIS model in the q-rung orthopair hesitant fuzzy environment. This model can provide more accuracy in expressing fuzzy and ambiguous information. At first, we propose the distance and similarity measures of q-ROHFSs and the properties related to the distance and similarity measures of q-ROHFSs, and secondly, the axiomatized definition and formula for the entropy of q-ROHFSs. Then, for the case where the attribute weights are totally unknown, a combination of subjective and objective attribute weighting model is proposed. This model not only considers the expert's decision preference, but also the objective situation of the attributes. In addition to the above-mentioned outcomes, this paper also improves the relative closeness formula, increases the preference coefficient, and considers the risk-preference of decision makers. Finally, the proposed model is compared with other methods and used to evaluate the effectiveness of military aircraft overhaul. The method is verified to be scientific, reliable and effective for solving MADM problems.

Bi-objective optimization of water management in shale gas exploration with uncertainty: A case study from Sichuan, China

Shale gas exploration relies heavily on freshwater inputs while generating large amounts of wastewater. With the quick development of shale gas, water management during exploration has increasingly become a hotspot from both environmental and economic perspectives. This study investigated all the key water-consuming phases in exploration and optimized the freshwater use and flowback water control using a bi-objective programming model with the consideration of uncertainties in each phase, aiming at the optimal trade-offs between economic and environmental objectives. The model conducted a case study of shale gas projects in Sichuan, China. The results show that (a) Tolerance of the uncertainty in water demand for hydraulic fracturing can effectively reduce both economic and environment cost, thereby reducing total system cost. (b) System costs depends on risk preferences of decision-makers. (c) Direct reuse is the best strategy to treat wastewater at the early phase of hydraulic fracturing; while more constraints other than onsite treatment costs should be considered when treating produced water during the production phase. (d) There exists an “economics of scale” in the water management during shale gas exploration.

Comprehensive assessment for battery energy storage systems based on fuzzy-MCDM considering risk preferences

Under the context of low-carbon economy development, the utilization of renewable energy is deemed as an effective way for energy conservation and emission reduction. Considering about the intermittent and volatile characteristics of renewable energy, the selection of the optimal energy storage system (ESS) among various kinds of alternatives is of critical significance for giving impetus to the development of renewable energy. Therefore, it is essential to develop a comprehensive assessment technique for prioritizing various battery energy storage systems and selecting the optimal one. This paper proposed an integrated fuzzy-MCDM (multi-criteria decision making) model combining Fuzzy-Delphi approach, the Best-Worst method (BWM), and fuzzy-cumulative prospect theory (CPT) for the comprehensive assessment of battery energy storage systems. The comprehensive assessment index system consists of 15 sub-criteria from the perspectives of technology, economy, environment, performance, and sociality based on Fuzzy-Delphi method. The optimal weights are determined by the BWM based on experts'judgments which emphasized the importance of technology and environment impacts. Fuzzy theory is employed to convert interval values and crisp values to triangular fuzzy numbers (TFNs) to maximize the use of objective data information, and then the CPT model is utilized to prioritize the rankings of various alternatives considering risk preferences of decision makers and investors. The empirical result shows that the Li-ion battery is the priority selection for micro-grid demonstration projects, followed by NaS battery and NiMH battery. Sensitivity analysis discusses the influences of risk preferences on alternatives rankings. Results demonstrate that even if decision makers and investors have various risk preferences, considering about the technological, environmental, economic, social, and performance criteria, the Li-ion battery is still the optimal, followed by NaS battery.

Robust goal programming for multi-objective optimization of data-driven problems: A use case for the United States transportation command's liner rate setting problem

Robust goal programming (RGP) is a recently developed, powerful new optimization modeling technique that conjoins two widely accepted operations research disciplines: robust optimization (RO) and goal programming (GP). In lieu of applying a probability distribution over possible outcomes, an approach considered by stochastic programming, RO utilizes uncertainty sets to account for data uncertainty. This characteristic of RO is an important attribute because identifying such a probability distribution is challenging, at best. Given this RO context, RGP additionally incorporates GP, traditionally a deterministic procedure, to address optimization problems having multiple objectives. As such, RGP has potential to help address a wide array of data-driven applications, ranging from financial management to engineering design.As a motivating use case for the utility of an RGP approach, this paper demonstrates the applicability of RGP by way of the data-driven United States Transportation Command (USTRANSCOM) liner rate setting problem. USTRANSCOM is responsible for the technical direction and supervision of over $7 billion [1] of annual passenger, cargo, mobility, and personal property movements in support of the Department of Defense (DoD). Transporting people and material with both organic and contracted assets, USTRANSCOM supports DoD organizations and agencies on a reimbursable basis, annually setting and charging rates for air and liner (i.e., sea) transport for their customers and reimbursing the transportation providers accordingly. The Cost Recovery Branch within TCJ8, the Financial Management and Program Analysis staff organization for USTRANSCOM, annually sets liner shipping rates specific to each combination of origin, destination, commodity type, booking terms, and container size for the upcoming fiscal year (FY). As a government entity, USTRANSCOM seeks to neither make a profit nor operate at a loss in any given FY. The current rate setting methodology assumes existing data is deterministic, resulting in process inaccuracies that contribute to unexpected surpluses or deficits each FY. Moreover, the current method fails to consider an additional USTRANSCOM objective: meeting customer's expectations that liner rates will change annually in accordance with industry-specific inflation. Considering the different goals and inherent parametric variance, the use case herein incorporates a decision maker's risk preference regarding parametric variability via a priori analysis to inform RGP techniques and improve the USTRANSCOM liner rate setting process.

Intuitionistic Unbalanced Linguistic Generalized Multiple Attribute Group Decision Making and Its Application to Green Products Selection

In many countries, green products play a critical role in energy recycling and environment protection. The selection of green products can be regarded as a multiple attribute decision making (MADM) problem. Due to the complexity and uncertainty of the problem, decision makers may give their personal preference values to different attributes of alternatives by intuitionistic unbalanced linguistic term sets. The main purpose of this paper is to put forward a new generalized multiple attribute group decision making (GMAGDM) approach based on the intuitionistic unbalanced linguistic dependent weighted generalized Heronian mean (IULDWGHM) operator and the intuitionistic unbalanced linguistic dependent weighted generalized geometric Heronian mean (IULDWGGHM) operator. The proposed method can not only relieve the influence of unfair assessments, but also consider the interaction effects of attributes. Furthermore, the appropriate parameter values and operators can be selected to meet the different risk preference of decision makers and actual requirements. Finally, a green products selection case is given to illustrate the effectiveness and universality of the developed approach.

Decision-Maker’s Risk Preference Based Intuitionistic Fuzzy Multiattribute Decision-Making and Its Application in Robot Enterprises Investment

At present, the utilization of hesitation information of intuitionistic fuzzy numbers is insufficient in many methods which were proposed to solve the intuitionistic fuzzy multiple attribute decision-making problems. And also there exist some flaws in the intuitionistic fuzzy weight vector constructions in many research papers. In order to solve these insufficiencies, this paper defined three construction equations of weight vectors based on the risk preferences of decision-makers. Then we developed an intuitionistic fuzzy dependent hybrid weighted operator (IFDHW) and proposed an intuitionistic fuzzy multiattribute decision-making method. Finally, the effectiveness of this method is verified by a robot manufacturing investment example.

Evidential reasoning approach with multiple kinds of attributes and entropy-based weight assignment

Multiple attribute decision making (MADM) problems often consists of quantitative and qualitative attributes which can be assessed by numerical values and subjective judgments. Subjective judgments can be evaluated by linguistic variables, and both numerical values and subjective judgments can be accurate or uncertain. The evidential reasoning (ER) approach provides a process for dealing with MADM problems of both a quantitative and qualitative nature under uncertainty. The existing ER approach considers both benefit and cost attributes in the evidence combination process. In this paper, deviated interval and fixed interval attributes are introduced into ER based MADM approach and the frames of discernment for representing these two kinds of attributes are given. The transformation rules from the assessment values of deviated interval attributes to belief degrees in the ER structure are then studied. An ave-entropy based weight assignment method considering the risk preference of decision maker is also shown to deal with uncertain assessment situation, such as belief distribution with qualitative attribute and uncertain utility function. Some programming models to generate interval weights and utilities are constructed. The rationality and efficiency of the methods in supporting MADM problems are discussed. Two case studies are provided to demonstrate the applicability and validity of the proposed approaches and the potential in supporting MADM under uncertainty.

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

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

Planning a sustainable regional irrigated production and forest protection under land and water stresses with multiple uncertainties

Abstract This study developed a hybrid stochastic-fuzzy model with Green-Z scenario (HFSG) for coordinating regional irrigated production and forest protection (IPFP) under stresses of water and land resources with multiple informations. The novelty of proposed HFSG is that the improvement of handling capacity for various uncertainties can facilitate to generate a practical landscape scale planning and water resources management (land-water plan) with a more robust manner. It not only has advantage of reflecting the meteorological impact (e.g., precipitation) presented as probability distribution, but also identifies natural or artificial vagueness in the process of decision-making. Moreover, Green-Z scenario analysis associated with the risk-averse attitude, threshold of acceptability and coefficient of robustness can discern the tradeoff between expected targets and outcomes under different risk preferences of decision makers. Through the application of proposed HFSG model in a practical land-water plan issue, numbers of results associated with optimized planting scales (including cultivation withdraw and forest recovery), stresses of water and land resources, pollutant mitigations, ecological effects and system benefits under varied Green-Z scenarios can be obtained. The results can support policymaker to produce cleaner strategies for alleviating conflict between irrigated activities and environmental protection, achieving socio-economic and eco-environmental sustainability.

Explicit cost-risk tradeoff for optimal energy management in CCHP microgrid system under fuzzy-risk preferences

In this paper, a fuzzy risk-explicit interval parameter programming (FREIPP) approach was provided for multiple energy supply and demand management in microgrid system under uncertainties. The FREIPP method integrates risk-explicit interval linear programming and fuzzy theory within a general framework. It can tackle fuzzy and interval uncertainties in terms of various cost coefficients, forecasted load demand, decision maker's risk attitude and other uncertainties in microgrid system management. Compared with traditional interval parameter programming, the proposed method has distinct advantages in minimizing the system cost and risk simultaneously and providing more risk explicit solutions with the regard of obscure risk preference of decision maker. The FREIPP approach was successfully applied in a microgrid system with combined cooling, heating and power (CCHP) generation for three types of decision maker (i.e. defensive, neutral and aggressive). The obtained results indicated that the proposed FREIPP approach could provide optimal operation strategies with explicit cost-risk tradeoff information for decision maker when facing multiple complex uncertainties. Furthermore, it could help decision maker with different risk tolerance select desired optimal risk-aversion strategies, which is more realistic in real-world decision making process.

Suitable Aggregation Models Based on Risk Preferences for Supplier Selection and Order Allocation Problem

In this paper, we propose (a) fuzzy multiple objective linear programming models for the Supplier Selection and Order Allocation (SSOA) problem under fuzzy demand and volume/quantity discount environments, and (b) an analysis of how to select the suitable aggregation operator based on the risk preferences of decision makers. The aggregation operators under consideration are additive, maximin, and augmented operators while the risk preferences are classified as risk-averse, risk-taking, and risk-neutral ones. The suitabilities of aggregation operators and risk preferences of decision makers are analyzed by a statistical technique, considering the average and the lowest satisfaction levels of the supplier selection criteria, based on numerical examples. Analysis results reveal that decision makers with different risk preferences will prefer only some aggregation operators and models. Moreover, a particular aggregation operator and model may generate a dominated solution for some situations. Thus, it should be applied with caution.

Soft computing based on a selection index method with risk preferences under uncertainty: applications to construction industry

Decision making in construction industry is a dynamic procedure which is concerned with choosing a reasonable strategy to accomplish the stated objectives. In this paper, a new developed hesitant fuzzy preference selection index (HFPSI) method in view of another soft computing approach with risk preferences of decision makers (DMs) is proposed to manage multi-criteria decision making (MCDM) issues in construction industry while applying hesitant fuzzy sets (HFSs) to represent the uncertain information under hesitant uncertainty. The proposed technique notwithstanding considering subjective surveying criteria, respects the DMs' troubles in deciding the membership of an element into a set. The proposed HFPSI approach is actualised by utilising two real case studies in the construction industry and the results are examined. The studied cases for the best construction project and the best contractor also demonstrate the effectiveness of applying the proposed method while considering a group of the DMs' ideas and hesitancy.

Estimation of Downside Risks in Project Portfolio Selection

In project portfolio selection, the aim is to choose projects which are expected to offer most value and satisfy relevant risk and other constraints. In this study, we show that uncertainties about how much value the projects will offer, combined with the fact that only a subset of the proposed projects will be selected, lead to inaccurate risk estimates about the aggregate value provided by the selected project portfolio. In particular, when downside risks are measured in terms of lower percentiles of the distribution of portfolio value, these risk estimates will exhibit a systematic bias. For deriving unbiased risk estimates, we present a calibration framework in which the required calibration can be presented in closed‐form in some cases or, more generally, derived by using Monte Carlo simulation to study a large number of project selection decisions. We also show that when the decision must comply with risk constraints, the introduction of tighter (more demanding) risk constraints can counterintuitively aggravate the underestimation of risks. Finally, we present how the calibrated risk estimates can be employed to align the portfolio with the decision maker's risk preferences while eliminating systematic biases in risk estimates.