Multi-attribute Decision Theory Research Articles

Safety evaluation of genetically modified crops

This study focuses on the application of genetically modified (GM) crops in modern agricultural production, delving into the assessment of their safety and consumer acceptance issues, while analyzing the mechanisms through which these factors influence market dynamics. The background highlights that, despite the potential of genetic modification technology to enhance the overall performance of crops, public concerns regarding their safety significantly affect consumer acceptance and, consequently, market performance. An evaluation of existing literature on the safety evaluation methods for GM crops is first conducted, identifying shortcomings in integrating consumer acceptance, and market dynamics. To address this gap, an evaluation system that incorporates consumer acceptance into the safety evaluation of GM crops was developed, utilizing the FuzzyID3 algorithm. Furthermore, employing multi-attribute decision theory, a decision model for assessing the stances of market stakeholders towards GM crops was established. This model, through the calculation and weighting of the distances from positive and negative ideal solutions across various modules, offers a novel perspective for market analysis. The methodology employed herein provides a robust tool for the safety evaluation and market forecasting of GM crops, holding practical value for guiding policy formulation and industry development.

A novel routing method for dynamic control in distributed computing power networks

Driven by diverse intelligent applications, computing capability is moving from the central cloud to the edge of the network in the form of small cloud nodes, forming a distributed computing power network. Tasked with both packet transmission and data processing, it requires joint optimization of communications and computing. Considering the diverse requirements of applications, we develop a dynamic control policy of routing to determine both paths and computing nodes in a distributed computing power network. Different from traditional routing protocols, additional metrics related to computing are taken into consideration in the proposed policy. Based on the multi-attribute decision theory and the fuzzy logic theory, we propose two routing selection algorithms, the Fuzzy Logic-Based Routing (FLBR) algorithm and the low-complexity Pairwise Multi-Attribute Decision-Making (lPMADM) algorithm. Simulation results show that the proposed policy could achieve better performance in average processing delay, user satisfaction, and load balancing compared with existing works.

An Application of Multi-Attribute Decision Theory to Teaching Differences in Middle Schools

An Application of Multi-Attribute Decision Theory to Teaching Differences in Middle Schools

Evaluation of digital economy development level based on multi-attribute decision theory.

The maturity and commercialization of emerging digital technologies represented by artificial intelligence, cloud computing, block chain and virtual reality are giving birth to a new and higher economic form, that is, digital economy. Digital economy is different from the traditional industrial economy. It is clean, efficient, green and recyclable. It represents and promotes the future direction of global economic development, especially in the context of the sudden COVID-19 pandemic as a continuing disaster. Therefore, it is essential to establish the comprehensive evaluation model of digital economy development scientifically and reasonably. In this paper, first on the basis of literature analysis, the relevant indicators of digital economy development are collected manually and then screened by the grey dynamic clustering and rough set reduction theory. The evaluation index system of digital economy development is constructed from four dimensions: digital innovation impetus support, digital infrastructure construction support, national economic environment and digital policy guarantee, digital integration and application. Next the subjective weight and objective weight are calculated by the group FAHP method, entropy method and improved CRITIC method, and the combined weight is integrated with the thought of maximum variance. The grey correlation analysis and improved VIKOR model are combined to systematically evaluate the digital economy development level of 31 provinces and cities in China from 2013 to 2019. The results of empirical analysis show that the overall development of China’s digital economy shows a trend of superposition and rise, and the development of digital economy in the four major economic zones is unbalanced. Finally, we put forward targeted opinions on the construction of China’s provincial digital economy.

Research on the Selection of Path Planning Algorithm: A Case Study in Leeds

Path planning algorithms are the core of path planning technology, and different path planning algorithms have different performances in terms of search efficiency and planning efficiency in different application areas. The traditional path planning algorithm selection mainly relies on personal knowledge and experience and takes a qualitative analysis method. With the variety of path planning algorithms and the increase of environmental complexity, the limitations of this empirical method are more and more obvious, and it is difficult to meet the actual needs of path planning and achieve the ideal path planning effect. The selection of path planning algorithms is an evaluation problem involving multiple factors and requires comprehensive algorithm selection and analysis. This paper addresses the problems of the selection method of the path planning algorithm, applies uncertainty theory and multi-attribute decision theory, constructs an evaluation system for path planning algorithm selection, proposes a multi-criteria EV-VIKOR decision model and decision method for path planning algorithm selection, and combines the results show the effectiveness of the decision-making method proposed in this paper.

Exploring destination image through online reviews: an augmented mining model using latent Dirichlet allocation combined with probabilistic hesitant fuzzy algorithm

PurposeIn the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.Design/methodology/approachThe study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.FindingsIn the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.Originality/valuePrevious research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

Optimization design of gear reducer based on multi-attribute decision making and reliability sensitivity

In order to solve the complexity and implicitness of lightweight optimization of the gear reducer, this paper constructs a multi-objective reliability optimization mathematical model of the gear reducer. and to obtain multi-objective Pareto solution, artificial intelligence algorithm is used to optimize structural parameters. Then, through repeatedly changing the structural parameters of the reducer, the experimental simulation analysis is carried out to obtain the maximum working stress. Based on the idea of the weight reduction and miniaturization, the stress state and reliability index values under different structural parameters are compared and analyzed. In order to determine the optimal solution of structural parameters, multi-attribute decision theory is used to determine the solution. Finally, in order to effectively reveal the inherent variation of attribute values and decision results and quantitatively reflect the impact of various structural parameters on system reliability, reliability sensitivity analysis of the gear reducer is carried out by the response surface-Monte Carlo and DPS methods. Thereby, an optimized structure that meets the safety reliability and the appropriate economy is obtained, and this study produces a balanced and improved optimized design that is coordinated with cost, quality, volume, safety and stability.

Normalized solutions for Choquard equations with general nonlinearities

<p style="text-indent:20px;">In this paper, we prove the existence of positive solutions with prescribed <inline-formula><tex-math id="M1">$ L^{2} $</tex-math></inline-formula>-norm to the following Choquard equation:</p><p style="text-indent:20px;"><disp-formula> <label></label> <tex-math id="FE1"> $ \begin{equation*} -\Delta u-\lambda u = (I_{\alpha}*F(u))f(u), \ \ \ \ x\in \mathbb{R}^3, \end{equation*} $ </tex-math></disp-formula></p><p style="text-indent:20px;">where <inline-formula><tex-math id="M2">$ \lambda\in \mathbb{R}, \alpha\in (0,3) $</tex-math></inline-formula> and <inline-formula><tex-math id="M3">$ I_{\alpha}: \mathbb{R}^3\rightarrow \mathbb{R} $</tex-math></inline-formula> is the Riesz potential. Under the weaker conditions, by using a minimax procedure and some new analytical techniques, we show that for any <inline-formula><tex-math id="M4">$ c>0 $</tex-math></inline-formula>, the above equation possesses at least a couple of weak solution <inline-formula><tex-math id="M5">$ (\bar{u}_c, \bar{ \lambda}_c)\in \mathcal{S}_{c}\times \mathbb{R}^- $</tex-math></inline-formula> such that <inline-formula><tex-math id="M6">$ \|\bar{u}_c\|_{2}^{2} = c $</tex-math></inline-formula>.</p>

Adaptive Multiservice Heterogeneous Network Selection Scheme in Mobile Edge Computing

With the coming of the fifth-generation (5G) mobile communications, in mobile edge computing (MEC), the growth of user services and the personalization of QoS requirements have posed great challenges for heterogeneous wireless networks (HWNs) access selection. Based on the multiattribute decision theory and the fuzzy logic theory, we propose a novel network selection scheme for multiservice QoS requirements in MEC. The main procedures of the scheme include dynamic adaptive process, fuzzy process, hierarchical analysis, and integrated attributes assessment. The scheme proposed contributes to efficiently reduce the ping-pong effect and effectively select accurate network in a dynamic environment. Simulation results show that our scheme can select network access according to the type of user services and whether to switch networks. In addition, compared with commonly used simple additive weighting (SAW), random access selection (RAS), and price-based and QoS-based network selection scheme, our scheme has better performance in improving average user satisfaction and reducing access failures.

The role of dry port in hub-and-spoke network under Belt and Road Initiative

ABSTRACTDry port plays increasingly an important role in the integration of inland regions with seaports, and cross-border inland ports especially in the context of Belt and Road Initiative (BRI) originally proposed by China. This paper studies a logistics network connecting the inland regions by dry ports based on a two-stage logistical gravity model. First, a basic logistical gravity model is developed to analyze the radiated inland regions from dry ports, where the logistical quality of dry ports is calculated by principal component analysis. Second, considering mutual impacts among dry ports, seaports and cross-border inland ports, the influence of logistical gravity on the network is examined by using a coefficient based on the Ordered Weighted Averaging Operator in multi-attribute decision theory. An improved logistical gravity model is further developed to investigate the logistical connections among various ports (e.g. dry ports, seaports and cross-border inland ports; hub and feeder ports). Then, a hub-and-spoke network can be established. Dry ports are potential to connect to the Silk Road Economic Belt and the 21st-Century Maritime Silk Road. So a Chinese case is used to verify the proposed method. The strategies of embedding inland regions in the BRI are discussed based on the experimental studies.

Probabilistic Reasoning for Secondary Spectrum Sharing: A Mechanism for Increasing Spectrum Access Efficiency and Assessing Alternative Secondary Sharing Models

Uncertainties regarding wireless propagation environments pose challenges for spectrum management in general and specifically hinder the implementation of dynamic spectrum sharing systems. Spectrum management mechanisms must balance increases in spectrum access efficiencies with risks of harmful interference among spectrum users. Without the ability to reliably assess the local propagation environment and spectrum usage in real time, secondary sharing systems have limited ability to evaluate interference risks. As a result, spectrum regulators specify spectrum access behaviors such as exclusion zones and maximum transmit powers based on risk thresholds applied to statistical results from propagation models and measurements. Because the models can contain significant levels of uncertainty, establishing behavior limits for low interference risk necessarily results in significant spectrum access inefficiencies. It is only by reducing the degree of uncertainty that risk thresholds can be maintained while increasing spectrum access efficiency. Probabilistic reasoning provides significant potential to increase spectrum access efficiency in dynamic spectrum sharing systems. Probabilistic reasoning approaches enable risk-constrained spectrum access, a concept in which regulators and spectrum users establish spectrum access rules defining acceptable levels of interference and spectrum access risks while potentially optimizing their decisions relative to sets of decision criteria. The theoretical foundations are established, and the concept is demonstrated through the development of a probabilistic reasoning model using Functional Causal Model Theory. The probabilistic reasoning model is coupled with multi-attribute decision theory to enable assessments of user decision-making subject to risk attitudes, levels of situational uncertainty, and alternative spectrum sharing models. Simulations demonstrate several significant capabilities associated with a probabilistic reasoning approach. First, risk-constrained access mechanisms inherently regulate spectrum access behaviors in accordance with the degree of situational uncertainty; greater situational uncertainty results in lower spectrum access performance (e.g., reduced transmit power levels) while maintaining the requisite level of risk. Second, current regulatory approaches based on spectrum models and surveys define the minimum performance level; additional information gained through local situational awareness mechanisms lead to increased performance. Third, probabilistic reasoning combined with situation-specific awareness potentially enables greater spectrum access by reducing requisite exclusion zone size and increasing user density. Finally, probabilistic reasoning combined with decision modeling enables assessment of alternative spectrum access regulations and models based on user behavior and risk attitudes.

Selection Strategy via Analytic Hierarchy Process: An Application for a Small Enterprise in Milk Sector

Strategic structure for decisions hierarchically is to obtain the goal of the decision at the top, basic strategic purposes in the higher levels, evaluation criteria in the middle, and alternative choices at the bottom. Organizational structures which are dynamic, process-centric, strategy-oriented, and trust-based come up as key for competitiveness for every level enterprises. The basic efficient for the system and the link between business strategic management is that focusing increases can be fractional and at least be correlated with, the relative value of contribution during the period for enhancement AHP(Analytic Hierarchy Process) is a method as needed for the benefit of the structure as tool of multi-attribute decision theory. The aim of this study to determine the best strategy and the development of small business management via SWOT (Strengths, Weaknesses, Opportunities, Threats), and AHS ‘s is used in combination. For this purpose, SWOT analysis will be implemented for a small scale enterprise in the food sector(milk factory) and alternative strategies will be determined that are based on SWOT factors. And then the SWOT matrix is converted into a hierarchical structure. AHP model will be built and try to generate the general structure for enterprise. Finally the model structure will be solved with AHP method.

Application of the Rasch Model to Develop a Simplified Version of a Multiattribute Utility Measurement on Attitude Toward Labor Epidural Analgesia

Valid and reliable measures based on health behavior theory, such as multiattribute utility decision theory, are essential to elucidate complex relationships between psychological factors and labor pain. In this study we aimed to use Rasch analysis to simplify a previously developed 20-item multidimensional questionnaire on attitude toward labor epidural analgesia using multiattribute utility theory. The Rasch analysis was performed to condense item selection categories, to exclude misfit items and persons, and to generate a unidimensional attitude toward labor epidural analgesia (ATLEA) score. Item characteristics and thresholds of rating categories in the questionnaire were also estimated. Reliability and empirical validity of the simplified version were further compared with those of the full version. One hundred sixty-seven postpartum women completed the questionnaire. The original 10 rating scale categories were combined to make 4 without compromising reliability. Three respondents and 11 items were excluded because of misfit. Reliability indices of the simplified and full versions were 0.68 and 0.74, respectively. The correlation coefficient between ATLEA scores from the simplified and full versions was 0.89. Empirical validity values of ATLEA scores from the simplified and full versions for labor epidural analgesia decision, as assessed by area under the receiver operating characteristic curves, were 0.80 and 0.81, respectively. We demonstrated application of the Rasch analysis to simplifying a multiattribute utility questionnaire without compromising reliability. Further study is necessary to determine whether the simplified questionnaire is valid for use in clinical practice.

My method is better!

Engineering Design is an activity where what you want to create and how you want to create are fundamental to the underlying process. Both aspects of this activity require selection among alternatives that are intertwined. In engineering, how a problem is defined and the creation and selection of alternatives are the focus of methods performed in a team. How do we carry it out, are we content with the outcome, can we do it better, are important questions to address. These questions have been studied to varying depths in many disciplines including psychology, neurosciences, economics, management, non-fiction literature, and engineering. While we are interested in engineering design contexts, which might be different from other non-engineering contexts, these questions transcend most disciplinary boundaries. In design, there are always many alternative solutions, some of which await discovery and some of which may require additional knowledge for it to work. The choice of multiple criteria for evaluating each alternative, and the specific selection are decided in a team process comprising of participants from diverse disciplines including those that are non-technical. Over the last 20 years, there have been many studies done on selection among alternatives in design. These studies have been reported in a variety of journals including Research in Engineering Design; they form a web from which part is depicted in Fig. 1. The web reflects the relations between them: some studies rely on other study results (stated ‘‘positive reference’’ in the legend); they criticize previous studies (stated ‘‘negative reference’’) and they propose improvements to other proposals (thereby describing their limitations, also considered negative); or they merely report what another study stated (‘‘indifferent reference’’). It is difficult to go through these studies and synthesize a coherent position as they have been conducted by authors from different disciplines, having different beliefs, and often critical of each other. The studies at the top of the figure represent the pool of methods and theoretical work that form the basis of subsequent work. Some of these studies are based on theoretical frameworks, e.g., Sen and Arrow (both from public choice theory) and Keeny and Raiffa (from multi-attribute decision theory); some are based on a theoretical framework with strong influence from practice, e.g., Saaty and Suh, and others originated in practice, e.g., QFD and Pugh. These origins are important because they form a context that permeates the discussion on the questions regarding methods I raised before. For example, consider a subset of these studies (Fig. 2), Arrows impossibility theorem (AIT) [Ar63], constructed in the context of public choice by voting, forms the basis of Hazelrigg’s (1996a, b, 1997, 1998, 1999) contention that

TU‐E‐204C‐03: How to Make Clinical Decisions in the Multicriteria Framework

Multiattribute decision making begins with the premise that the solution to the problem at hand involves multiple goals, some of which are mutually antagonistic, and not all of which can be achieved in a single solution. Choosing a multiobjective optimization algorithm should be done in conjunction with the selection of a decision making approach. A vital component of the decision making method is to be explicit about the criteria, how they are to be evaluated and how they relate to the value structure of the decision maker.Much of the theory of multiattribute decision theory stems from economic theory. In particular, Pareto optimality was first formulated as the condition in which it is impossible to make one person better off without making someone else worse off. Another economic concept, utility, is used to quantify values on a relative scale. In problems not related to economics, it is usually the case that the criteria used to make a decision are difficult to formulate in terms than can be mathematically optimized. For example, in radiation therapy, surrogates for patient outcomes must be formulated in terms of physical dose deposition.Mathematical optimization algorithms can take several forms. Prior methods require the user to input some preferences before the optimization can proceed. Such methods can produce a single “optimal” solution. Interactive methods rely on feedback between the decision maker and the optimization algorithm. These methods can be the most efficient at searching but require considerable commitment from the decision maker. Posterior optimization methods produce a set of solutions that hopefully spans the trade‐offs of interest. The mathematical optimization is followed by a decision making process that selects one of them.Learning objectives:1. To appreciate the relationship between Pareto optimality and decision making.2. To understand the current state of decision making and how it relates to other approaches.3. To understand how multiattribute decision making approaches in radiation therapy relate to methods used in other fields such as operations research.4. To become acquainted with the basics of decision theory such as utility theory.

A decision aid for intensity-modulated radiation-therapy plan selection in prostate cancer based on a prognostic Bayesian network and a Markov model

The prognosis of cancer patients treated with intensity-modulated radiation-therapy (IMRT) is inherently uncertain, depends on many decision variables, and requires that a physician balance competing objectives: maximum tumor control with minimal treatment complications. In order to better deal with the complex and multiple objective nature of the problem we have combined a prognostic probabilistic model with multi-attribute decision theory which incorporates patient preferences for outcomes. The response to IMRT for prostate cancer was modeled. A Bayesian network was used for prognosis for each treatment plan. Prognoses included predicting local tumor control, regional spread, distant metastases, and normal tissue complications resulting from treatment. A Markov model was constructed and used to calculate a quality-adjusted life-expectancy which aids in the multi-attribute decision process. Our method makes explicit the tradeoffs patients face between quality and quantity of life. This approach has advantages over current approaches because with our approach risks of health outcomes and patient preferences determine treatment decisions.

Making decisions for incident management in nuclear power plants using probabilistic safety assessment

Decisions by management during nuclear power plant operations may have safety-related and economic consequences, and may affect the relationship with external stakeholders such as regulatory authorities. Attributes such as worker exposure to radiation, potential radiation release, negative publicity, and regulatory intervention may conflict with concerns such as loss of income due to plant shutdown or reduced power. This article shows that the combination of probabilistic safety assessment and multi-attribute decision theory offers the potential for a structured decision-making methodology that could take into account risk-related aspects (plant and worker safety, for instance), as well as important factors like economics and regulatory requirements. The difficulties we encountered during the elicitation of utilities, and our solutions to those issues, are discussed. We demonstrate our decision analysis methodology using a case study in incident management—the decision making that follows events emanating from non-safety-related component failures. Particular attention has been paid to the assessment of tradeoffs among different objectives, especially when some of the consequences of decision options can be catastrophic.

Generation and evaluation of user tailored responses in multimodal dialogue

When people engage in conversation, they tailor their utterances to their conversational partners, whether these partners are other humans or computational systems. This tailoring, or adaptation to the partner takes place in all facets of human language use, and is based on a mental model or a user model of the conversational partner. Such adaptation has been shown to improve listeners’ comprehension, their satisfaction with an interactive system, the efficiency with which they execute conversational tasks, and the likelihood of achieving higher level goals such as changing the listener’s beliefs and attitudes. We focus on one aspect of adaptation, namely the tailoring of the content of dialogue system utterances for the higher level processes of persuasion, argumentation and advice-giving. Our hypothesis is that algorithms that adapt content for these processes, according to a user model, will improve the usability, efficiency, and effectiveness of dialogue systems. We describe a multimodal dialogue system and algorithms for adaptive content selection based on multi-attribute decision theory. We demonstrate experimentally the improved efficacy of system responses through the use of user models to both tailor the content of system utterances and to manipulate their conciseness.

Generation and evaluation of user tailored responses in multimodal dialogue

AbstractWhen people engage in conversation, they tailor their utterances to their conversational partners, whether these partners are other humans or computational systems. This tailoring, or adaptation to the partner takes place in all facets of human language use, and is based on a mental model or a user model of the conversational partner. Such adaptation has been shown to improve listeners' comprehension, their satisfaction with an interactive system, the efficiency with which they execute conversational tasks, and the likelihood of achieving higher level goals such as changing the listener's beliefs and attitudes. We focus on one aspect of adaptation, namely the tailoring of the content of dialogue system utterances for the higher level processes of persuasion, argumentation and advice‐giving. Our hypothesis is that algorithms that adapt content for these processes, according to a user model, will improve the usability, efficiency, and effectiveness of dialogue systems. We describe a multimodal dialogue system and algorithms for adaptive content selection based on multi‐attribute decision theory. We demonstrate experimentally the improved efficacy of system responses through the use of user models to both tailor the content of system utterances and to manipulate their conciseness.

Evaluation of the Economic Impact of Proposed Non-Indigenous Species Control Measures for the St Lawrence Seaway Using Multi-Attribute Decision Theory

Mandatory ballast water management, resulting in an increase of required freight rates (RFR) on the St Lawrence Seaway, may cause modal shifts from the marine transportation mode to other modes. However, the modal shifts could well produce several undesirable side effects, such as worse air pollution, lower transportation safety, etc. To evaluate the trade-off between these and the positive ballast water management effects, a multi-attribute decision problem is formulated and solved, using the Analytic Hierarchy Process method, fuzzy set theory, and utility theory. A numerical example is presented. The results indicate that such modal shifts should be avoided, since the more the cargo is shifted from the marine mode to other modes, the more unfavourable the consequences are for the system.