Reflect Decision Makers Research Articles

Hierarchize Pareto Dominance in Multi-Objective Stochastic Linear Bandits

Multi-objective Stochastic Linear bandit (MOSLB) plays a critical role in the sequential decision-making paradigm, however, most existing methods focus on the Pareto dominance among different objectives without considering any priority. In this paper, we study bandit algorithms under mixed Pareto-lexicographic orders, which can reflect decision makers' preferences. We adopt the Grossone approach to deal with these orders and develop the notion of Pareto-lexicographic optimality to evaluate the learners' performance. Our work represents a first attempt to address these important and realistic orders in bandit algorithms. To design algorithms under these orders, the upper confidence bound (UCB) policy and the prior free lexicographical filter are adapted to approximate the optimal arms at each round. Moreover, the framework of the algorithms involves two stages in pursuit of the balance between exploration and exploitation. Theoretical analysis as well as numerical experiments demonstrate the effectiveness of our algorithms.

Consistency improvement under incomplete hesitant fuzzy linguistic preference relations in large-scale group decision-making

This paper studies a large-scale group decision-making method (LSGMD) based on incomplete hesitant fuzzy linguistic preference relations (IHFLPRs) and proposes an improved model for additive consistency of hesitant fuzzy linguistic preference relations (HFLPRs). Additionally, consistency control and fuzzy C-means (FCM) clustering are utilized to enhance efficiency and reliability. Firstly, a model is proposed to address the issues of missing elements in IHFLPRs and insufficient additive consistency in HFLPRs, aiming to more accurately reflect decision makers’ preference relationships towards candidate alternatives. Subsequently, the FCM method is employed to cluster decision experts’ preference information and obtain the overall preference information. Finally, the rationality and accuracy of our proposed method are demonstrated through a case study and comparative analysis.

User-Centric Innovation District Planning: Lessons from Brisbane’s Leading Innovation Districts

Innovation districts are widely known as an effective land use type for fostering and sustaining knowledge and innovation economy growth in cities. Knowledge workers and the public are among the main stakeholders and key drivers for the growth of innovation districts. However, these groups’ needs are often not well considered in the top-down implementation of innovation districts. This paper aims to explore the user preferences and decision makers’ perspectives in innovation district planning, design, and development. The study tackles the question of which characteristics fulfil the responsibility of innovation districts toward both societies (reflecting user preferences) and cities (reflecting decision makers’ perspectives). As for the methodology, a case study approach was employed to collect the required data from three innovation districts in Brisbane, Australia. The data are qualitatively and quantitatively analysed. The analysis findings highlighted the similarities between user preferences and decision makers’ perspectives—e.g., usefulness of decentralisation, urbanism, mixed-use development, street life, and social interactions in innovation districts—and the differences that need to be carefully factored into the planning, design, and development of innovation districts with a user-centric approach.

A multi-criteria decision-making with regret theory-based MULTIMOORA method under interval neutrosophic environment

The MULTIMOORA (multiple multi-objective optimization by ratio analysis) method is useful for multiple criteria decision-making method. It is based on expected utility theory and assumes that decision makers are completely rational. However, some studies show that human beings are usually bounded rational, and their regret aversion behaviors play an important role in the decision-making process. Interval neutrosophic sets can more flexibly depict uncertain, incomplete and inconsistent information than single-valued neutrosophic sets. Therefore, this paper improves the traditional MULTIMOORA method by combining the regret theory under interval neutrosophic sets. Firstly, the regret theory is used to calculate the utility value and regret-rejoice value of each alternatives. Secondly, the criteria weights optimization model based on the maximizing deviation is constructed to obtain the weight vector. Then, the MULTIMOORA method is used to determine the order of the alternatives. Finally, an illustrative example about school selection is provided to demonstrate the feasibility of the proposed method. Sensitivity analysis shows the validity of the regret theory in the proposed method, and the ranking order change with different regret avoidance parameter. Comparisons are made with existing approaches to illustrate the advantage of the proposed method in reflecting decision makers’ psychological preference.

An improved HK model-driven consensus reaching for group decision making under interval-valued fuzzy preference relations with self-confidence

Self-confidence actually affects group decision making (GDM) because it can somehow reflect decision maker (DM)’s expertise and cognitive level. This paper proposes a new form of preference relation named interval-valued fuzzy preference relation with self-confidence (IFPR-SC). IFPR-SC requires DMs to express self-confidence levels when offering interval-valued preferences. Some operation laws regarding IFPR-SC are presented. Moreover, this paper uses an induced-ordered weighted aggregation operator to aggregate individual IFPRs-SC, then presents a novel feedback adjustment mechanism called improved Hegselmann-Kraus (IHK) model to update DMs’ IFPRs-SC. Finally, this paper offers an illustrative example and several comparisons to verify the feasibility and efficiency of IHK model.

Two-level consensus modeling with utility and cost constraints

There exist many two-level group consensus problems with different psychological behaviors of decision makers. To deal with these group consensus problems and reach a stable consensus, based on the principles and methods of grey system, utility theory and group consensus, we use grey utility function to describe and reflect decision makers' opinion preferences in different subgroups and different levels, and then we construct a two-level group consensus method with a moderator, and exploit it to solve the negotiation problems of the natural gas subsidy.

Intuitionistic fuzzy social network hybrid MCDM model for an assessment of digital reforms of manufacturing industry in China

• A new intuitionistic fuzzy aggregation method is proposed, called the IF hybrid average and geometric operator. • A multi-dimensional trust propagation model is built to reflect decision makers’ social relationships. • A social network MCDM model is proposed under an IFS environment. • The MCDM model is used to evaluate the effects of digital reform in the communication equipment manufacturing enterprises in China. • The influence of the parameters in the proposed method on the result is considered to ensure the robustness. Digital reform requires enterprises to use digital technology to create a deep integration between their production, management, and operational processes, and generate a data chain for the entire process, thereby meeting the personalized requirements and expectations of customers. The achievements of digital reform in manufacturing enterprises need to be evaluated scientifically, which can help the enterprises adjust their development strategies for a digital reform in a timely manner. We therefore propose a multi-criteria model based on a social network for assessing a digital reform under an intuitionistic fuzzy environment, wherein an intuitionistic fuzzy hybrid average and geometric operator is proposed to aggregate evaluation information more effectively than with existing methods. In addition, because the trust relationships between experts can affect their decisions, a social network is introduced to determine the weights assigned to these experts. Finally, a case study of four manufacturing enterprises is presented to verify the effectiveness of the proposed method.

PT-MARCOS multi-attribute decision-making method under neutrosophic cubic environment

Neutrosophic cubic set (NCS) can process complex information by combining interval neutrosophic set and single-valued neutrosophic set. It can simultaneously describe the uncertain and certain part of information. Prospect theory (PT) is based on bounded rationality and can reflect decision maker’s different risk attitudes to gains and losses. Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) method can measure and rank the alternatives according to compromise solution. Considering the bounded rationality of decision makers and compromise solution of alternatives, this paper combines the PT with MARCOS method to neutrosophic cubic environment to solve multi-attribute decision-making problem. First, the theoretical basis of NCS is introduced. Second, the PT and MARCOS method are combined. To reflect subjective views of decision makers and the objectivity of decision-making information, this paper uses geometric average method to combine subjective weights (calculated by the best-worst method) and objective weights (calculated ed by the entropy method). Then, the PT-MARCOS method is applied to a decision-making problem. Further, a sensitivity analysis is conducted to study the influence of different attenuation factor values and different expectation coefficient on the ranking; and through comparative analysis to illustrate the superiority of the PT-MARCOS method. Finally is the conclusion.

Q-Rung orthopair fuzzy frank power point aggregation operators with new multi-parametric distance measures

The recently proposed q-rung orthopair fuzzy set (q-ROFS) whose main feature is that the qth power of membership degree (MD) and the qth power of non-membership degree (NMD) is equal to or less than 1, is a powerful tool to describe uncertainty. The major contribution of this paper lies to investigate power point average (PPA) aggregation operators with q-rung orthopair fuzzy information based on Frank t-conorm and t-norm. Since the existing power average (PA) operators all rely on the traditional distance measures to measure support degree between the input values, it cannot reflect decision makers’ attitude. In response, this paper introduces firstly a series of distance measures for q-rung orthopair fuzzy numbers (q-ROFNs) based on point operators, from which the corresponding support measures can be obtained. Secondly, based on the proposed point distance measures, new Frank power point average aggregation operators are proposed to aggregate q-rung orthopair fuzzy information. Finally, a novel multiple attribute decision making (MADM) technique is presented based on the proposed Frank power point average aggregation operators. The developed MADM method not only can get more objective information, but also avoid the influence of unduly high or low attribute values on the decision result, providing a new way for decision makers (DMs) under q-rung orthopair fuzzy environment.

Synergetic management of water-energy-food nexus system and GHG emissions under multiple uncertainties: An inexact fractional fuzzy chance constraint programming method

Management of water-food-energy nexus (WEFN) is of great importance to achieve the Sustainable Development Goals. The development of WEFN management strategies is challenged by extensive uncertainties in different system components. Also, agricultural activities would contribute a large portion of the total GHG emissions in many countries, which are affecting the promised carbon neutrality in future. In this study, an inexact fractional fuzzy chance constraint programming method was developed towards planning the water-food-energy nexus system under consideration of both uncertainties and greenhouse gases (GHG) emission. An inexact fractional fuzzy chance constraint programming-based water-energy-food nexus (IFFCCP-WEFN) model has been established under consideration of various restrictions and GHG emissions. Solutions of the planting areas for different crops in different periods have been generated. These results imply that the corn cultivation would be prioritized to satisfy cereal demand due to its relatively lower GHG emission intensity. But the residual resources, after satisfying cereal demand, would tend to be allocated to vegetable planting. Comparison has been conducted among the IFFCCP-WEFN model and WEFN models based the inexact fuzzy chance constraint programming approach with and without GHG emissions. The results indicate that, the results from IFFCCP-WEFN model would achieve a highest unit benefit and lowest total GHG emissions. The total GHG emissions can be 11% less at most than GHG emissions from the resulting crop structures of the other two comparable models. Consequently, the developed IFFCCP-WEFN model can help decision-makers identify the desirable planting structure for crops with a priority of low GHG emission rate. The major contributions in this study include (i) the inexact fractional fuzzy chance constraint programming method to deal with interval and fuzzy parameters, reflect decision makers’ preferences and handle conflicts among contradictory objectives, (ii) the IFFCCP-WEFN model to achieve a maximized unit benefit with respect GHG emissions

Robust control in a rough environment

This paper studies robust decision problems in a rough environment that is described by Volterra processes. Various alternative dominated models are introduced to reflect decision makers' concerns regarding model uncertainty. Using a functional Itô calculus approach, we characterize the robust optimal strategy by a path-dependent Hamilton–Jacobi–Bellman–Isaacs equation. Explicit strategies are derived for robust power and exponential utility maximization with the Volterra Heston model and an example from a robust linear-quadratic control problem. Numerical study shows that it becomes harder to distinguish two probability measures in a rougher environment. Robust optimal investment strategies can reduce losses from ignoring volatility roughness and model uncertainty and can improve the stability of portfolio performance.

A novel portfolio selection with prospect value constraint and distance measure of IFSs based on the improved entropy-weighted method

The purpose of this paper is to solve the portfolio selection problem when historical data are unavailable. In this paper, the problem is viewed as a multi-criteria decision making (MCDM) problem under intuitionistic fuzzy circumstances, and the prospect theory is utilized to reflect decision makers’ psychological state, which is always bounded rational. Therefore, a new approach to solve MCDM problems is presented based on the following improvements. (a) The entropy-weighted method with extreme data resistance is proposed instead of weight function to deal with the weight of criteria, because weight stands for the decision maker’s preference of criteria rather than objective probability and should not be distorted. (b) A new entropy-weighted method with confidence degree is presented, which can not only describe the uncertainty of information each criterion provides but also reflect the decision maker’s confidence in the information. (c) To reduce the interference from extreme data, the median is selected as reference point instead of mean or extreme value. (d) Based on the distance measure, the intuitionistic fuzzy prospect value function is presented to capture decision makers’ psychological state. Finally, a novel model with prospect value constraint and risk preference is constructed to allocate investment ratios. For our proposed method and model, two numerical applications are given to verify their validity and the sensitivity analysis is carried out to illustrate their practical significance.

Multi-stage stochastic fuzzy random programming for food-water-energy nexus management under uncertainties

A hybrid inexact optimization model is developed for food-water-energy nexus system management with the consideration of complex uncertainties and decision makers’ risk tolerance. A multi-stage stochastic fuzzy random programming (MSFRP) model is tailored to tackle variables with deeper uncertainties, a mixture of fuzzy and random fuzzy characteristics. Allowing to reflect decision makers’ subjective opinion and risk preference, it can provide decision makers the tradeoff information between system benefit and risk attitude. The proposed model was applied to an agricultural area Shandong Province, China with the aim of maximum total system benefits. The valuable managerial insights on optimal cultivated land distribution, water resource allocation, and energy supply strategies are provided for decision makers under uncertainties. Meanwhile, the pesticide and fertilizer consumption for crop planting, and the carbon emission embodied in per unit crop supply are also quantitatively estimated. Moreover, by setting different water resource availability scenarios, the impacts of future water resource conditions on optimal management strategies under climate change are evaluated and discussed. The results suggested that rice would be the critical crop with the largest planting area for food security during the planning horizon. Under scarcer water resource conditions, the system benefits would reduce due to more desalination water consumption and planting strategy adjustment. However, it would lead to less carbon emission embodied in per unit crop supply and relieve local carbon emission control pressure. Compared to the conventional multi-stage stochastic programming, the developed MSFRP can be more effective to reflect the optimistic and pessimistic attitude of decision makers and deal with future scenario information with deeper uncertainties.

Risk Aversion Based Inexact Stochastic Dynamic Programming Approach for Water Resources Management Planning under Uncertainty

In this study, a dual interval robust stochastic dynamic programming (DIRSDP) method is developed for planning water resources management systems under uncertainty. As an extension of the existing interval stochastic dynamic programming (ISDP) method, DIRSDP can deal with two-stage stochastic programming (TSP)-based planning problems associated with dynamic features, input uncertainties, and multistage concerns. Compared with other optimization methods dealing with uncertainties, the developed DIRSDP method has advantages in addressing uncertainties with complex presentations and reflecting decision makers’ risk-aversion attitudes within its optimization process. Parameters in the DIRSDP model can be represented as probability distributions as well as single and/or dual intervals. Decision makers’ risk-aversion attitudes can be reflected through restricting the deviation of the recourse costs to a tolerance level. Water-allocation plans can then be developed based on the analysis of tradeoffs between the system benefit and solution robustness. The developed method is applied to a case of water resources management planning. The solutions are reasonable, indicating applicability of the developed methodology.

A projection-based regret theory method for multi-attribute decision making under interval type-2 fuzzy sets environment

Interval type-2 fuzzy sets (IT2FSs) can provide more flexibility than type-1 fuzzy sets (T1FSs) for depicting uncertain information, and multi-attribute decision making (MADM) problems with interval type-2 fuzzy information have received increasing attention. A new projection-based regret theory method is proposed to solve MADM problems under IT2FSs environments. First, a projection model of IT2FSs is defined that takes both the distance and angle information into consideration. Second, integrating the proposed projection model with regret theory, new utility and regret-rejoice functions are developed, respectively. Finally, a case study is provided to demonstrate the effectiveness of the proposed method. Sensitivity analysis shows the stability of the proposed method, and the ranking order does not change with different parameters. Comparisons are made with existing approaches to illustrate the advantage of the proposed method in reflecting decision makers’ psychological factors.

Site selection decision framework using fuzzy ANP-VIKOR for large commercial rooftop PV system based on sustainability perspective

The Large Commercial Rooftop Photovoltaic System (LCRPS) projects have been rapidly proposed in China due to policy promotion towards sustainability. Site selection immensely determines the life cycle of LCRPS projects based on sustainability perspective, in need of considering the impact on environment and society. However, inadequacies still exist in the decision for LCRPS site selection. Firstly, uncertainties of information cannot be described integrally. Secondly, consideration of the correlation among criteria from sustainability perspective is lacking. Thirdly, the ranking methods in previous studies cannot reflect decision makers’ subjective preferences. Therefore, this study establishes a decision framework for LCRPS site selection utilizing Fuzzy Analytic Network Process (Fuzzy ANP) method and Fuzzy Vlsekriterijumska Optimizacija IKompromisno Resenje (Fuzzy VIKOR) method. First, the triangular intuitionistic fuzzy numbers (TIFNs) are adopted to describe indeterminate information. Second, fuzzy ANP is introduced to reflect the correlation among criteria based on sustainability perspective. Third, fuzzy VIKOR is employed to rank the options on the basis of decision makers’ subjective preferences. Finally, to validate the effectiveness of the proposed framework, a case study of Guangdong province with sensitive analysis and comparative analysis is conducted. The result shows the LCRPS site located in Dongguan should be selected as the optimal site.

A prospect theory-based method for fusing the individual preference-approval structures in group decision making

Abstract Preference-approval structure is a typical preference structure that can not only reflect decision makers’ preference orderings for alternatives but also distinguish the satisfied alternatives from the dissatisfied alternatives. The aim of this paper is to propose a method to fuse the individual preference-approval structures in group decision making (GDM), in which the weight of decision makers can be represented as the exact number (i.e., exact weight), the interval number (i.e., interval weight) and the rankings (i.e., ranking weight). In the method, based on prospect theory, the prospect values of all the alternatives associated with each decision maker are calculated. In addition, the expected values of the weights of decision makers are calculated. Then, the relative-position indicators of the alternatives are determined. In addition, two algorithms are proposed to determine the reading sequences of the alternatives. Based on the reading sequences, an algorithm is proposed to construct the fused preference-approval structure. Next, some desirable properties of the proposed method are discussed. Finally, an example is given to illustrate the use of the proposed method, and the versatility, consistency, efficiency and computational complexity of the proposed method are discussed.

Composite indicators as generalized benefit-of-the-doubt weighted averages

Composite indicators (CIs) are usually computed as arithmetic (weighted) averages of (often normalized) sub-indicators. Several studies criticized this procedure for implying requirements and properties that are often hard to maintain in practical applications. Recent studies explored the multiplicative aggregation and more specifically the geometric (weighted) average as aggregator in CI-building. This paper takes this exploration one step further by considering other members of the family of generalized (weighted) averages as aggregator in the construction of CIs. It is argued that the choice for a particular version of the generalized weighted average enables to reflect decision makers’ attitudes in the evaluation. For the Human Development Index, results show that the choice for a specific version of the generalized (weighted) average as aggregator in CI-construction impacts country rankings.

An Integrated Risk Analysis Method for Planning Water Resource Systems to Support Sustainable Development of An Arid Region

An interval-based two-stage risk analysis (ITRA) method is developed for planning water resource systems associated with uncertainties presented in terms of probability distributions and interval values. Risk measures are employed to assess the impacts of degrees of the preference of decision makers on the tradeoff between system benefits and expected economic losses. ITRA is applied to a case study of the Kaidu-kongque watershed located in an arid region of northwestern China. A series of scenarios are examined based on different risk measures, results of which reflect decision makers‟ attitudes toward risk aversion and options for water-resource allocation under system-reliability levels. Results disclose that both uncertainties of system components and risk attitudes of decision makers have significant effects on water-allocation patterns and economic benefits. Model outputs link the pre-regulated water-allocation targets in decision making with various scales of regionalization policies (due to existence of uncertainties of meeting target flows). Results reveal that the competitiveness can exacerbate the ecological water shortage when limited water resources are available for multiple users in the arid region. The methodology and findings can help managers to gain scientific understanding of the consequences of water allocation decisions when planning in a fast-growing economic development and extremely arid region.

Consensus Measure with Multi-stage Fluctuation Utility Based on China’s Urban Demolition Negotiation

Utility functions are often used to reflect decision makers’ (DMs’) preferences. They have the following two merits: one refers to the representation of the DM’s utility (satisfaction) level, the other one to the measuring of the consensus level in a negotiation process. Taking the background of China’s urban house demolition, a new kind of consensus model is established by using different types of multi-stage fluctuation utility functions, such as concave, convex, S-shaped, reversed S-shaped, reversed U-shaped as well as their combinations, to reveal negotiators’ dynamic physiological preferences and consensus level. Moreover, the effects of the decision-making budget and the individual compensation tolerance on the consensus level are also discussed in this paper. Compared with previous research, the proposed model takes both the negotiation cost and DM’s preference structure into consideration, and most importantly, it is computational less complex.