A Multi-Attribute Decision-Making Method Using Belief-Based Probabilistic Linguistic Term Sets and Its Application in Emergency Decision-Making

Probabilistic linguistic term sets(PLTSs)are an effective tool for expressing subjective human cognition that offer advantages in the field ofmulti-attribute decision-making(MADM).However,studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers(DMs)in certain circumstances,such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete,thus affecting their role in the decision-making process.Belief function theory is a leading streamof thought in uncertainty processing that is suitable for dealing with the limitations of PLTS.Therefore,the purpose of this study is to extend PLTS to incorporate belief function theory.First,we provide the basic concepts of the extended PLTS(i.e.,belief-based PLTS)through case analyses.Second,the aggregation operator of belief-based PLTS is defined with the ordered weighted average(OWA)-based soft likelihood function,which is improved by considering the reliability of the information source.Third,to measure the magnitude of different belief-based PLTSs,the belief interval of singleton is calculated,and the comparison method of belief-based PLTS is constructed based on probabilities.On the basis of the preceding discussion,we further develop an emergency decision framework that includes several novel techniques,such as attribute weight determination and decision information aggregation.Finally,the usefulness of the framework is demonstrated through a case study,and its effectiveness is illustrated through a series of comparisons.

Strategic Renewable Energy Resource Selection Using a Fuzzy Decision-Making Method

Renewable energy is created by renewable natural resources such as geothermal heat,sunlight,tides,rain,and wind.Energy resources are vital for all countries in terms of their economies and politics.As a result,selecting the optimal option for any country is critical in terms of energy investments.Every country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global warming.In the present work,the authors suggest fuzzy multi-characteristic decision-making approaches for renew-able energy source selection,and fuzzy set theory is a valuable methodology for dealing with uncertainty in the presence of incomplete or ambiguous data.This study employed a hybrid method for order of preference by resemblance to an ideal solution based on fuzzy analytical network process-technique,which agrees with professional assessment scores to be linguistic phrases,fuzzy numbers,or crisp numbers.The hybrid methodology is based on fuzzy set ideologies,which calculate alternatives in accordance with professional functional requirements using objective or subjective characteristics.The best-suited renewable energy alternative is discovered using the approach presented.

Solution to multiple attribute group decision making problems with two decision makers

A kind of multiple attribute group decision making （MAGDM） problem is discussed from the perspective of statistic decision-making. Firstly, on the basis of the stability theory, a new idea is proposed to solve this kind of problem. Secondly, a con- crete method corresponding to this kind of problem is proposed. The main tool of our research is the technique o~ the jackknife method. The main advantage of the new method is that it can identify and determine the reliability degree of the existed decision making information. Finally, a traffic engineering example is given to show the effectiveness of the new method.

A prospect theory-based methodfor linguistic decision making under risk

Based on the prospect theory, a novel linguisticdecision method under risk is proposed. First, the alternativesunder each risk state are rated using linguistic terms, and＇ thelinguistic decision matrix is constructed. Secondly, thelinguistic terms are transformed into triangular fuzzy numbers,so that the linguistic evaluations can be changed into numericalforms. Thirdly, with the aid of the prospect theory, theprobability weight functions and the linguistic value functionscan be computed, based on which the prospective values of thealternatives are obtained. Finally, the alternatives are rankedwith respect to the prospective values combined of probabilityweight and linguistic value functions. Thus, the optimalchoice is made. The decision process takes the psychologicalpreferences of the decision maker into consideration. Thepracticality of the proposed method is illustrated through anapplication on stock selection problems.

Prospect Theory Based Hesitant Fuzzy Multi-Criteria Decision Making for Low Sulphur Fuel of Maritime Transportation

The environmental impact of maritime transport has now become a relevant issue in sustainable policy formulation and has attracted increasing interest from academia.For the sustainable development of maritime transport,International Maritime Organization stipulates that the sulfur content of ship emissions will reach 0.5 from 2020.With the approaching of the stipulated implementation date,shipowners need to adopt scientific methods to make decision on low sulfur fuel.In this study,we applied a prospect theory based hesitant fuzzy multi-criteria decision-making model to obtain the optimal decision of low Sulphur marine fuel.For this purpose,the hesitant fuzzy decision matrix is established to collect expert opinions,the maximizing deviation method is adopted to determine criteria weights.According to calculate the Euclidean distance from the reference points,we obtain the comprehensive prospect values of alternatives.Lastly,a case study is carried out to illustrate the significance and effectiveness of the proposed methodology.The innovation of this study is that it is the first-time adopting prospect theory and hesitate fuzzy sets to multi-criteria decision making for low Sulphur marine fuel,which provides an effective decision model for shipping companies under Low Sulphur regulations,and can also be extended to other industries.

Complexity Decision Making and General Systems Theory： An Educational Perspective

The aim of this paper is to look at some important educational aspects of complexity decision making m a mummsc^pnnary manner from the perspective of General Systems Theory （GST）. First, the major issues involved in complexity management and decision making are summarized as they are viewed in literature, and a review of GST and Systems Thinking is given. The discussion in the paper is developed within the context of GST in general, but concentrated on decision making in the three trends of GST： Operations Research, Cybernetics, and Managerial Cybernetics. Here, the role of Cybernetics in complexity decision making is particularly emphasized. The discussion is then extended to the latest developments in complexity decision making in Science of Complexity and Soft Systems Thinking. The study also includes a framework which is expected to guide instructors who are planning to offer contemporary courses on decision making. The framework provides some clues for assessing the level of complexity for a given situation and selecting the appropriate methodology for solution development.

Decision-Making Model for Farmer Choosing Land Use Based upon Dynamic Optimization Theory

Using the dynamic optimization theory, we described a decision-making model for farmer choosing land use when there are several different kinds of uses for land. To obtain an empirical model that could be easily applied, decision rules for farmer with a single static expectation were given.

Study on group air to ground attack-defends hierarchical dynamic decision-making

As to oppositional, multi-objective and hierarchical characteristic of air formation to ground attackdefends campaign, and using dynamic space state model of military campaign, this article establishes a principal and subordinate hierarchical interactive decision-making way, the Nash-Stackelberg-Nash model, to solve the problems in military operation, and find out the associated best strategy in hierarchical dynamic decision-making. The simulating result indicate that when applying the model to air formation to ground attack-defends decision-making system, it can solve the problems of two hierarchies, dynamic oppositional decision-making favorably, and reach preferable effect in battle. It proves that the model can provide an effective way for analyzing a battle,

Dual worth trade-off method and its application for solving multiple criteria decision making problems

To overcome the limitations of the traditional surrogate worth trade-off （SWT） method and solve the multiple criteria decision making problem more efficiently and interactively, a new method labeled dual worth tradeoff （DWT） method is proposed. The DWT method dynamically uses the duality theory related to the multiple criteria decision making problem and analytic hierarchy process technique to obtain the decision maker＇s solution preference information and finally find the satisfactory compromise solution of the decision maker. Through the interactive process between the analyst and the decision maker, trade-off information is solicited and treated properly, the representative subset of efficient solutions and the satisfactory solution to the problem are found. The implementation procedure for the DWT method is presented. The effectiveness and applicability of the DWT method are shown by a practical case study in the field of production scheduling.

New Explorations for Decision Trees

Traditionally, the decision tree method is defined and used for finding the optimal solution of a Bayesian decision problem. And it is difficult to use the decision tree method to find the sub-optimal solution, not to mention to rank alternatives. This paper discusses how to use the decision tree method for the alternative selecting and ranking. A practical case study is given to illustrate the applicability.

Game Theory Based Model for Predictive Analytics Using Distributed Position Function

This paper presents a game theory-based method for predicting the outcomes of negotiation and group decision-making problems. We propose an extension to the BDM model to address problems where actors’ positions are distributed over a position spectrum. We generalize the concept of position in the model to incorporate continuous positions for the actors, enabling them to have more flexibility in defining their targets. We explore different possible functions to study the role of the position function and discuss appropriate distance measures for computing the distance between the positions of actors. To validate the proposed extension, we demonstrate the trustworthiness of our model’s performance and interpretation by replicating the results based on data used in earlier studies.

Optimization on Decision Making Driven by Digitalization

The content of this paper refers to the global trend of digitalization and its implications on decision making. The question： will digitalization improving decisions in organizations be a relevant topic for all organizations7 Digitalization is currently the key driver for change in business and organizations. Affected is everything, from market structures to customer behaviour over supply chain and production itself. Internal processes have opportunities for a new design, and production and logistics are possible to redesign. This massive game changing opportunity has to be steered by management and hence management decisions are a must while this transition phase; furthermore, decisions will influence this transition but decisions itself are also affected. The paper is based on a theoretical research, analysing different decision models. In the first part of the paper, typical decision models will be discussed; a rational model with first ideas of the neoclassical economists （e.g. Adam Smith or Max Weber） and scientific approaches of Pascal and de Fermat or Bernoulli, mainly focused on agents which maximize their utility. Further developed by von Neumann and Morgenstern （2004）, economic decisions seem to be a strong rational and mathematical process to find utility maximization. This rational model is illustrated on the one hand to explain the traditional way and has a view on the model of homo oeconomicus. On the other hand, a strong emotional influence on decision making is obvious, because human beeings do not follow strict rational rules. In the second part of the paper, digitalization as one part of globalization will be analysed. Digitalization will change completely the business environment and the setup of organizations. New market structures, customer behaviour, and processes will change the entire operations of businesses. Then, the main discussion of the paper, the impacts of digitalization on decision making will show the trend toward the well-known model of homo oeconomicus, which is a common model in economics; but known with a lot of limitations. Automatization of processes will affect the decision process in organizations. This new decision making processes will be mainly automated in the future, hence an algorithm logic is required and enables the model of homo oeconomicus a revival, driven by machines. As a conclusion based on the above mentioned result, automated decisions will improve the result of decisions, because human emotions will not affect the decision making process anymore.

The Impact of Personality Traits and Problem Structures on Management Decision-Making Outcomes

Since decision-making behavior has been in the focus both from a scientific and a professional position, there seems to be a dispute whether rational or intuitive decision making leads to better outcomes. By now, scholars have agreed that effective organizations do not have the luxury to choose between the ＂applications＂ of intuitive or rational decision making. Instead, they try to understand how different factors like personality traits and problem characteristics influence the decision-making process. Reviewing the literature reveals that personality pre-determination and the structure of problems （e.g., well-structured problems （WSPs） versus ill-structured problems （ISPs）） seem to have a significant impact on the decision-making efficiency. Further, the review also shows that there is a lack of application-oriented empirical studies in this area of research. Therefore, the aim of this research paper is to propose a framework for an empirical study on how personality traits and problem structure influence the decision-making process. First, hypotheses are derived from the literature on how personality pre-determination and behavioral patterns in the decision-making process lead to higher socioeconomic efficiency within certain problem categories. Second, a causal model and a setup for a laboratory experiment are proposed to allow testing the hypotheses. Finally, the conclusions provide an outlook on how this research could support organizations in their decision-making processes.

A Method for Risky Multiple Attribute Decision Making with Four - dimensional Reference Point

A method based on cumulative prospect theory was proposed to solve risky multiple attribute decision making problems with Four -dimensional reference points. Considering the influence of different learning processes and corresponding features on decision-making, a new reference-learning behavior is added, and a risk-based multiple-attribute decision-making method based on four-dimensional reference point cumulative prospect theory is proposed. Firstly, according to the cumulative prospect theory, the prospect value and the decision function value of the four reference points of learning, time, evaluation value and expected value are calculated respectively, and the cumulative prospect value matrix of each program dynamic is formed. Secondly,according to the WAA operalor, Maximize the stage weighting model to obtain the integrated cumulative prospect value. Finally, on the basis of this, the alternatives are sorted according to the size of the total cumulative prospect value, and compared with other methods, the validity and scientific of the proposed method are proved.

Quantum Model of Decision-Making in Economics

The paper designs a quantum model of decision-making (QMDM) that utilizes neuroscientific evidence. The new model provides both normative and positive implications to economics. First, it enhances the study of decision-making which is an extension of the expected utility theory (EUT) in mathematical economics. Second, we demonstrate how the quantum model mitigates drawbacks of the expected utility theory of today.

Matrix-based method for solving decision domains of neighbourhood multigranulation decision-theoretic rough sets

It is more and more important to analyse and process complex data for gaining more valuable knowledge and making more accurate decisions.The multigranulation decision theory based on conditional probability and cost loss has the advantage of processing decision-making problems from multi-levels and multi-angles,and the neighbourhood rough set model(NRS)can facilitate the analysis and processing of numerical or mixed type data,and can address the limitation of multigranulation decision-theoretic rough sets(MG-DTRS),which is not easy to cope with complex data.Based on the in-depth study of hybrid-valued decision systems and MG-DTRS models,this study analysed neigh-bourhood MG-DTRS(NMG-DTRS)deeply by fusing MG-DTRS and NRS;a matrix-based approach for approximation sets of NMG-DTRS model was proposed on the basis of the matrix representations of concepts;the positive,boundary and negative domains were constructed from the matrix perspective,and the concept of positive decision recognition rate was introduced.Furthermore,the authors explored the related properties of NMG-DTRS model,and designed and described the corresponding solving algorithms in detail.Finally,some experimental results that were employed not only verified the effectiveness and feasibility of the proposed algorithm,but also showed the relationship between the decision recognition rate and the granularity and threshold.

Decision-making of Aircraft Optimum Configuration Utilizing Multidimensional Game Theory

As multi-discipline coupling and components interference often affect the aircraft configuration decision-making and analysis during conceptual design process, this article presents an approach of multidimensional game theory based on aircraft compo- nents to deal with this problem. The idea is that the configuration decision-making process is regarded as the game for different disciplines and technologies, and the aircraft components are players. The payoff function with highest total gain means that ac- cording to the game protocols and multidimensional theory, the optimal aircraft configuration within the strategy set will be cho- sen. The decision-making model is applied to conceptual design process of the high altitude long endurance （HALE） unmanned aerial vehicle （UAV） based on the assessment of technological risk. The obtained optimum configuration is quite consistent with the current HALE UAV development trends. Thus, taking into account the coupling and interference factors, the multidimensional gaming model based on aircraft components will be an effective analysis method in the decision-making process of aircraft optimum configuration.

Should a Rational Person With Risk-Averse Attitude Gamble？ A Case Study of Mark Six in Hong Kong

Contrary to the conventional wisdom which suggests that a perfectly rational subject with risk-averse attitude should never gamble, this paper provides a vivid example in our real economy for people to expect a positive return to gamble Within the existing literature, scholars have proposed different reasons to explain why an original risk-averse person would start to gamble, with the implicit assumption that the expected payoff of a bet is always lower than its cost. In contrast, our paper discusses when and why a risk-averse person could bet strategically with a positive expected return; in particular, we used field data in Hong Kong to illustrate a vivid scenario for positive expected return in gambling.

基于三级养护标准的沥青路面预养护方案多目标加权灰靶决策

针对沥青路面最佳预防性养护策略选择问题,结合关于沥青路面预防性养护指标及规范标准,建立了基于判断性指标、综合性指标和控制性指标的三级预防性养护判断标准。在此标准下,以多年度项目级养护规划为研究对象,结合GM(1,1)预测模型确定预防性养护时段,并引入多目标智能加权灰靶决策模型对适用性预防性养护方案进行优化决策。在此过程中,确定了养护费用、耐久性等8个养护目标并利用博弈论组合赋权法确定权重,选取G30连霍高速甘肃省境内某路段进行决策分析。结果表明:该路段最早需要开展预防性养护的时间为第3年,在第6年底之后不宜再实施预防性养护,在该预防性养护时段内,方案4的综合效益值最大,其值为0.735 8,即应在第4年采取超薄层罩面预养护措施。该方法简单实用,可成为沥青路面预防性养护策略选择的一种新方法。