Multi-attribute Utility Theory Research Articles

Determining Tourist Destination Priorities Using Website-Based Particle Swarm Optimization Methods (Case Study : North Padang Lawas Regency)

. Optimization of tourist routes is very important to minimize travel time and reduce travel costs. This study focuses on optimizing tourist routes in North Padang Lawas Regency using Multi Attribute Utility Theory (MAUT), and Particle Swarm Optimization (PSO) in the context of the Traveling Salesman Problem (TSP). This study discusses problems such as the lack of priority of tourist destinations and the need for shorter travel times. The research process includes problem identification, literature review, data collection through field observations and interviews, and data processing with MAUT to prioritize destinations. The identified priority destinations are Hotel Sapadia Gunung Tua, Barumun Nagari, Batik Sekar Najogi, Durian and Manggis Agrotourism, Rumah Makan Holat Alhamdulillah, Waterboom Gunung Tua, and Candi Bahal I, II, III. Furthermore, PSO is applied to determine the optimal travel route. PSO finds a route with a total travel time of 351 minutes, although the three-day travel time is extended to 375 minutes.

Incomplete Objectives in Decision Making: How Omitting Objectives Affects Identifying the Most Promising Alternative

Research has shown that decision-makers omit a significant number of their objectives when making a decision. This study examines the consequences of incomplete objectives on decision making, i.e., how does omitting objectives affect identifying the most promising alternative? We investigate this question using a dataset of 945 observed decisions. These decisions were developed by students using the decision-skills and training tool entscheidungsnavi.com. The tool guides students in a step-by-step process based on value-focused thinking, multi-attribute utility theory, and debiasing methods. Results show that omitting objectives significantly reduces the chances of identifying the most promising alternative. Hence, neglecting only 20% of the objectives is sufficient to mislead more than one in four decisions. We have found three factors that influence this risk of misidentifying the most promising alternative: (1) the weight of the omitted objectives; (2) the consensus on the best alternative across all objectives; and (3) the consensus on the ranking of all alternatives across all objectives.

Clinical Factors Related to Abnormal General Movements Trajectories: Development of a Multicriteria Index.

To create a multicriteria index with clinical factors associated with abnormal general movements (GMs) trajectories during a stay in the neonatal intensive care unit. A longitudinal study assessed abnormal GMs trajectories in preterm infants in the neonatal intensive care unit. The multicriteria index included clinical factors such as intraventricular hemorrhage, respiratory support, gestational age, patent ductus arteriosus, and infection based on multi-attribute utility theory. A total of 52 preterm newborns, 57.7% male, were evaluated. The multicriteria index correlated with abnormal GMs trajectories and explained 26.5% of the variance in these trajectories. The developed multicriteria index, with the inclusion of combined clinical factors, allowed the identification of part of the abnormal trajectories of GMs in preterm infants. The multicriteria analysis can help clinicians to identify infants who have a greater chance of poor developmental trajectories before hospital discharge.

Best Sales Selection Using a Combination of Reciprocal Rank Weighting Method and Multi-Attribute Utility Theory

The best salespeople are individuals who are not only able to meet or exceed sales targets, but also demonstrate exceptional skills in building relationships with customers, understanding their needs, and offering effective solutions. The problem of selecting the best salespeople often involves the challenge of an objective and fair assessment, as diverse evaluation criteria can affect the final result. One of the main obstacles is the presence of subjectivity in judgment, which can arise from personal preferences or pressure to maintain good relationships. This study aims to implement a sales performance evaluation model that combines the Reciprocal Rank Weighting and Multi-Attribute Utility Theory (MAUT) methods to obtain a more accurate and objective assessment of sales performance. This research contributes to the management literature and decision support systems by offering a new approach in sales performance evaluation. This opens up opportunities for further research and practical applications in the field of performance evaluation and salesforce management. Based on the final score calculated using the MAUT method, the salesperson rank from best to lowest is as follows: Sales 7 is ranked top with a value of 0.646, indicating the best overall performance. Sales 3 followed in second place with a value of 0.6125, followed by Sales 9 with a value of 0.5604 in third position.

Decision Support System for Determining the Best School Extracurricular Activities by Combining the ROC and MAUT Methods

The various extracurricular activities at school make students confused and difficult to choose which extracurricular activities are more suitable for participation. However, sometimes there are also students choosing extracurricular activities based on many of their friends. Therefore, determining the best school extracurricular activities is the best solution for students as a reference to find which is the best extracurricular activity. The criteria used in this study in choosing the best extracurricular activities are Regional Event Activities, Allocation, Creativity and Talent Channeling. By utilizing SPK, decision makers can make more systematic decisions, based on a deeper understanding of the various alternatives available and relevant criteria. SPK or decision support system is a technique that has the ability to determine a decision using a technical design based on alternatives and predetermined criteria. SPK or decision support system is a technique that has the ability to determine a decision using a technical design based on alternatives and predetermined criteria. In the context of extracurricular school selection, combining the ROC (Rank Order Centroid) and MAUT (Multi-Attribute Utility Theory) methods in a Decision Support System is an interesting approach. The ROC method is used to cluster and rank schools based on certain criteria, while MAUT helps in the calculation of appropriate weights for these criteria. By integrating these two methods, the SPK can provide a more structured guideline in the selection of extracurricular activities that suit students' interests and needs. The research results obtained show that the Futsal alternative is the first recommendation as the best extracurricular with a final value of 0.655086.

Implementasi Kombinasi Metode ROC dan MAUT Dalam Menentukan Aplikasi Chatting Terbaik

The rapid development of technology makes it easier for humans to communicate with each other more easily by using the best chatting applications. The purpose of this research is to help users who have just used a smartphone to choose the chatting application that suits their needs. The method used in this study is a Decision Support System with Rank Order Centroid (ROC) as the weight value and Multi Attribute Utility Theory (MAUT) as a solution. The alternatives used are Whatsapp, Messager, Discord, Telegram, Line, WeChatting and the criteria used are Security, Storage Media, Network Usage, application features, Display / Interface. The results obtained indicate that the Whatsapp alternative is the first recommendation as the best chatting application with a final value of 0.929456. The Telegram alternative became the second recommendation with a final value of 0.847485 and Line became the third recommendation with a final value of 0,624656.

A multicriteria decision model to improve emergency preparedness: Locating-allocating urban shelters against floods

The increasing prominence of natural disasters in recent years has made emergency disaster management one of the main challenges for building resilient societies. This way, the warming combination with human-induced urban interventions with climate change reshapes the strategic decision-making that public managers must deal with to promote safety and preparedness. Consequently, it implies complex situations under uncertainty that comprise conflicting dilemmas about which decision-makers (DMs) should establish trade-offs. Given this backdrop, this work proposes a risk-based multidimensional model for assessing urban shelter location against flood risk with Decision Analysis and Multi-Attribute Utility Theory. By using principles of the Queue Theory, the decision model undertakes four criteria that measure in a probabilistic manner the alternatives’ performance when considering urban flood disaster evacuation routes in affected areas. Altogether, it enables the DM to establish his/her preferences with views to rank potential locations for mounting temporary shelters and for planning emergency preparedness in risky areas. Being replicable to any urban context, our proposal has the potential to support and promote the development and implementation of crisis protocols consisting of measures to reduce flood impacts and adapt critical areas to climate change. From a numerical application of the proposed model, the DM is encouraged to explore graphical and tabular visualization tools, as well as sensitivity analysis, to establish in strategic terms how to design and implement emergency shelters to reduce flood risks in urban areas effectively.

Investigating Participant Characteristics and Scores in Hypertension Using Multi-Attribute Utility Theory Analysis on Hypertension Patients from Pakistan

Investigating Participant Characteristics and Scores in Hypertension Using Multi-Attribute Utility Theory Analysis on Hypertension Patients from Pakistan

Advancing sustainability: An integrated decision support framework for fleet selection in open pit mining construction

The process of selecting and configuring equipment fleets for open-pit mining is complex, requiring consideration of both quantitative and qualitative criteria. This paper presents a novel comprehensive framework for selecting loading/hauling fleets in open-pit mining projects. Through an extensive review of the literature, a total of 21 selection criteria are initially identified. With guidance from local experts, these criteria are shortlisted into five primary groups: site conditions, managerial, social, and environmental criteria. The ranking of the identified selection criteria is carried out by employing the analytical hierarchy process (AHP) and multi-attribute utility theory (MAUT) methods, utilizing a questionnaire survey. The most crucial criteria in selecting loading/hauling equipment are identified as the equipment's operating cost, productivity, repair and maintenance costs, and soil condition. An optimization module is developed to optimize the selected fleet, while adhering to sustainability principles and project constraints. The optimization module considers social, economic, and environmental sustainability variables to determine the optimal number, size, type, and capacity of fleets needed to complete the work within the allotted time. As a case study, the proposed framework is validated by experts in these types of projects. The framework's ability to effectively select fleet units for sustainable loading/hauling fleet is confirmed through this validation method.

LEVERAGING MISSION SOLUTION CONFIGURATION THROUGH MBSE AND TRADESPACE EXPLORATION

AbstractThales is a worldwide leader in innovative radar and mission solution systems used by naval ships. As the demand for personalized products increased through time, Thales shifted from a project‐oriented to a product‐oriented approach. This shift aims to capitalize on variants, minimize customization, and streamline operations. In this context, Thales established a mission solution configuration process (SCP) to facilitate the selection of product variants to compose a system during the bidding phase. However, the current SCP has limitations, constraining exploration and integration with engineering processes and system data. Consequently, the proposed systems sometimes fall short of the most optimal solution the client could get. Therefore, the objective of this work is to develop and validate an improved mission solution configuration process to streamline the creation and selection of product variants at Thales, particularly during the bidding phase, to better meet client needs and operational requirements. This method integrates Model‐Based Systems Engineering (MBSE) and Tradespace Exploration (TSE), utilizing ARCADIA as the methodology and Capella as the tool. A descriptive model is generated for analytical purposes within TSE, employing Multi‐Attribute‐Utility‐Theory (MAUT) and Pareto‐Optimization for evaluating and selecting optimal mission solution variants. Validation was conducted through a Coast Guard mission case study involving 125 solution variants, revealing Pareto‐optimal solutions balancing performance and cost. This method enhances the current configuration process by aligning client and operational needs with Thales's sales and product teams, ensuring accurate interpretation of requirements and minimizing information inconsistencies. The case study results identify technological gaps in variant designs, guiding research and development efforts towards subsystems or components with significant impact on system performance.

Implementing multi-attribute utility theory in service recovery: An operational management perspective on online retailing

How corporate decision-makers allocate resources to address the major issues of service recovery is critical. This article, from the perspective of operations management, integrates Multi-Attribute Utility theory into the field of service recovery. Through the use of online review data from smart home products, the PCRA-Multi-Attribute Utility model is proposed. Based on the model, this paper presents a prioritization strategy for service recovery and reveals that the utility of intelligent products is primarily influenced by service attitude. Furthermore, we discovered that manager's response strategies should pay more attention to recovery related to vital attributes. Strategy optimization can be achieved by adjusting the response length of these key attributes.

Optimization of Recruitment of Prospective Financial Managers Through a Decision Support System with the Multi Attribute Utility Theory Method

Optimization of Recruitment of Prospective Financial Managers Through a Decision Support System with the Multi Attribute Utility Theory Method

Implementasi Metode Multi Attribute Utility Theory pada Sistem Pendukung Keputusan dalam Pemilihan Siswa Unggulan

Vocational School of Al Furqon is a private school that annually holds an election of excellent students. Vocational school Al Furqon selects excellent students manually so that the results of the assessment of superior students are less effective. The purpose of our research is to produce a decision-support information system for the selection of Excellent Students at Al-Furqon Batu Bara Vocational School by building a decision-support system using the Multi-Attribute Utility Theory (MAUT) method. The type of research used is quantitative. The research method used to develop this system is the waterfall method which starts from analysis, planning, implementation, and testing. In our research stages are interviews, observations, and literature studies. There are 70 alternatives but only 9 alternatives fall into the criteria, so only 9 alternatives are processed in the calculation. The subject and object of our research is the excellent student of Al Furqon Vocational School. The results of our research are presented in the form of a Decision Support System, the selection for superior students has been successfully implemented in the form of a website that can be accessed universally and anytime using the Internet with the results of the MAUT method calculation, Tantri Agustin Damanik got the highest score, namely with a total score of 1000 and was successfully named as an excellent student according to the calculation of the MAUT method and according to predetermined criteria.

Determining the most useful renewable energy alternative for Turkey by combining MAUT and TAOV methods

This study focuses on various Multi-Criteria Decision Making (MCDM) methods developed in the academic literature to facilitate the decision-making process. These methods are primarily aimed at establishing a ranking of alternatives based on specific criteria, with the ultimate goal of determining the most optimal alternative. In this work, we propose a novel integrated MCDM approach that combines the Total Area Based on Orthogonal Vectors (TAOV) method with the Multi-Attribute Utility Theory (MAUT) method. Within this framework, criteria weights are determined using the MAUT method, while the TAOV method is employed to ascertain the ranking of alternatives. The primary objective of this approach is to identify the most suitable renewable energy resource for the context of Turkey. Based on a comprehensive review of relevant literature regarding the evaluation of renewable energy sources in Turkey, a set of critical criteria influencing the decision-making process is delineated. These criteria encompass a range of factors including efficiency, construction duration, cost, government incentives, economic lifespan, external dependence, employment opportunities, social acceptance, spatial requirements, and environmental considerations such as greenhouse gas emissions. The outcomes of this study reveal that, according to the proposed approach, the most beneficial renewable energy resources for Turkey are ranked as follows: hydroelectric, wind, solar, biomass, and geothermal energy sources, respectively.

Integrating Uncertainties in a Multi-Criteria Decision Analysis with the Entscheidungsnavi

The Entscheidungsnavi is an open-source decision support system based on multi-attribute utility theory, that offers various methods for dealing with uncertainties. To model decisions with uncertainties, decision-makers can use two categories: Forecast and Parameter Uncertainties. Forecast Uncertainty is modeled with (combined) influence factors using discrete, user-defined probability distributions or predefined ‘worst-median-best’ distributions. Parameter Uncertainty allows imprecision for utilities, objective weights, and probability distributions. To analyze these uncertainties, the Entscheidungsnavi offers several methods and tools, like a robustness check, based on (Monte Carlo) simulations and a sensitivity analysis. The objective weight analysis provides insights into the effects of different objective weight combinations. Indicator impacts, tornado diagrams, and risk profiles visualize the impact of uncertainties in a decision under risk. Risk profiles also enable a check for stochastic and simulation dominance. This article presents the complete range of methods for dealing with uncertainties in the Entscheidungsnavi using a hypothetical case study.

A multi-criteria decision-making model for heavy construction equipment replacement in Saudi Arabia

Purpose This paper aims to propose a novel model that addresses the limitations of current practices, through considering quantitative and qualitative criteria in the decision-making process for equipment replacement. Design/methodology/approach Literature review and consultation with professionals in the heavy construction industry was conducted to identify the criteria influencing the replacement of construction machines. A questionnaire survey using analytic hierarchy process and multi-attribute utility theory was used to rank these criteria and establish their utility scores. Sensitivity analysis was performed to assess how adjustments in the weights of main criteria would impact equipment replacement decisions. Findings The identified criteria were classified into three categories: economic, technical and socioenvironmental, encompassing a total of 15 criteria. The findings indicated that salvage value/meeting payback period/maximizing profitability held the highest importance in the replacement process, followed by considerations like high repair and maintenance cost; working condition and economic conditions. Safety and social benefits scored the least among all criteria and categories. Research limitations/implications This study focuses on earth-moving equipment and involves experts from the Eastern Province of Saudi Arabia. The model introduces a novel methodology to aid decision-makers, particularly contractors and project managers, in determining when to replace heavy construction equipment, which results in resource efficiency and time saving. Originality/value The model integrates expertise and knowledge from experts to establish criteria for replacing construction equipment. This research aims to improve the functionality of the decision-making process regarding the acquisition or replacement of equipment throughout its lifespan.

Industry 4.0 and Lean Manufacturing, using the MAUT Method

Industry 4.0 covers a wide range of areas, such as production processes, efficiency, data management, consumer relationships, and competitiveness. It is also becoming a topic of academic study, with researchers and scholars exploring various questions related to this revolution. Furthermore, the text mentions the Toyota Production System, which promotes efficiency and effective communication in production operations and introduces the concept of Lean Manufacturing, which aims to reduce costs and increase efficiency. The integration of Industry 4.0 with Lean Manufacturing is referred to as “Lean 4.0”. The method used in this article was a questionnaire sent to manufacturing companies in southern Brazil and analyzed the data obtained through the Multi-Attribute Utility Theory (MAUT), which is presented as a decision-making tool that allows comparing alternatives taking into account multiple criteria. The conclusions of this study show that both the views of senior management and those surveyed consider that the companies involved in this research will be in 5 years, with their Industry 4.0 well developed. On the Lean Manufacturing side, considering only the view of those surveyed, they consider that the companies involved in this study will be in 5 years, with Lean Manufacturing quite developed. The practical contribution of this study shows that the companies participating in the study are committed to adopting advanced manufacturing technologies and practices. This conclusion is important because it indicates that companies are aware of the opportunities and challenges of Industry 4.0 and Lean Manufacturing.

Single and Multiobjective Shutdown Optimization of a Multistage Continuous Crystallizer.

This study presents the first model-based optimal shutdown procedure of a multistage continuous crystallization process which aims at the maximization of on-spec production and minimization of the shutdown time. The cooling antisolvent crystallization of Aspirin (acetylsalicylic acid) in a three-stage continuous crystallizer was used as a case study. To address the optimal shutdown problem, several single optimization scenarios were considered to assess the impact of the degrees of freedom, discretization schemes, and optimization settings such as the constraints. The proposed optimal shutdown procedures showed that significant amounts of on-spec crystals can be produced both at fixed and variable shutdown times. Most importantly, the optimal shutdown procedures can match the steady-state productivity, based on the shutdown to steady-state productivity ratio (STSPR) which can easily reach 100%. Moreover, the residual shutdown material, considered as waste, can be dramatically reduced by >80% compared to the current standard shutdown procedures. Given the conflicting nature of the maximization of on-spec production and minimization of the shutdown time, multiobjective optimization of the shutdown operation was also addressed to identify the set of Pareto optimal solutions. Finally, a multicriteria decision-aiding method, based on multiattribute utility theory, was proposed to rank the Pareto optimal solutions to support the decision-making and help identify a suitable and feasible single optimal shutdown solution.

Spatial multi-criteria analysis for prioritising forest management zones to prevent large forest fires in Catalonia (NE Spain)

Large forest fires are one of the most common environmental issues in the Mediterranean region. In this study, we defined a set of indicators and criteria based on available spatial data to assess and prioritize management zones for preventing large forest fires in Catalonia (NE Spain). In total, 22 indicators were defined and grouped into 5 criteria. The process involved geospatial modelling using a Geographical Information System (GIS) and the Multi-Attribute Utility Theory (MAUT) analysis. The conducted MAUT analysis resulted in the development of rules for a more standardized assessment of priority management areas. This assessment is based on participatory evaluation and introduces a utility-function-based hierarchical spatial model. Within the model, each of the 5 criteria contains relevant information, including fuel hazard, potential fire behaviour, fire extinction accessibility, valuable resources at risk, and historical fire data, serving as the foundation for a comprehensive analysis of fire prevention measures. During the assessment, two distinct sets of weights were applied: one based on expert evaluation and another using equal weights. These weights describe the relative importance of the indicators within each criterion in the assessment of fire prevention measures. Our results reveal noticeable differences between the two applied approaches. In general terms, management priority levels showcase certain spatial aggregation patterns and spatial polarization characteristics. Our study underscores the importance of participatory planning for prioritizing forest management areas to reduce fire impacts and presents a methodological framework that facilitates such spatial assessments, utilizing tools that combine expert knowledge and scientific expertise. Therefore, the main focus of this study is not on the obtained results, but on the novel methodological framework used, which consists in combining geospatial modelling with MAUT and the participatory workshops for generating the utility rules. This framework allows participation of stakeholders which at the same time enlarges credibility and acceptance of the methodology and results.

Introducing the Comprehensive Value Function for Sustainability Full-Spectrum Assessment

This paper introduces the comprehensive value function, a novel framework designed to address multi-dimensional challenges in sustainability assessment within decision-making processes. Multi-criteria decision-making methods based on multi-attribute utility theory excel at integrating environmental, social, and economic factors but typically focus on positive and neutral impacts. This limitation often results in the oversight of critical negative consequences, thus restricting their effectiveness in all-encompassing sustainability evaluations. The proposed framework addresses this gap by quantifying the degree of satisfaction across a full spectrum of sustainability impacts and integrating negative outcomes essential for holistic assessments. The necessity of this model is highlighted by the shortcomings of current practices, where adverse impacts are frequently overlooked and existing frameworks fail to foresee the negative repercussions of various alternatives. By facilitating the creation of positive, negative, or piecewise functions, the CVF provides a comprehensive reflection of impacts, essential for well-informed sustainability decisions. Integrating this function into established decision-making models leads to a more balanced approach adept at navigating the intricate trade-offs inherent in sustainable development. Organised systematically, the paper presents the comprehensive value function, its application across various domains, and a concluding section synthesising the findings.