Utility Theory Approach Research Articles

Risk behavior among farmers: examining expected utility and prospect theory approach

Risk behavior among farmers: examining expected utility and prospect theory approach

Risk Behavior among Farmers: Examining through Expected Utility and Prospect Theory Approach

Risk Behavior among Farmers: Examining through Expected Utility and Prospect Theory Approach

A mass vaccination site selection problem: An application of GIS and entropy-based MAUT approach

Coronavirus disease (COVID-19) was recognized in December 2019 and spread very severely throughout the world. In 2022 May, the total death numbers reached 6.28 million people worldwide. During the pandemic, some alternative vaccines were discovered in the middle of 2020. Today, many countries are struggling to supply vaccines and vaccinate their citizens. Besides the difficulties of vaccine supply, mass vaccination is a challenging but mandatory task for the countries. Within this context, determining the mass vaccination site is very important for recovering, thus a five-step approach is generated in this paper to solve this real-life problem. Firstly the mass vaccination site selection criteria are determined, and secondly, the spatial data are collected and mapped by using Geographical Information System (GIS) software. Then, the entropy weighting method (EWM) is used for determining the relative importance levels of criteria and fourthly, the multiple attribute utility theory (MAUT) approach is used for ranking the potential mass vaccination sites. Lastly, ranked alternative sites are analyzed using network analyst tool of GIS in terms of covered population. A case study is conducted in Gaziantep city which is the ninth most population and having above-average COVID-19 patients in Turkey. As a result, the fourth alternative (around the Şehitkamil Monument) is chosen as the best mass vaccination site for the city. It is believed that the outcomes of the paper could be used by city planners and decision-makers.

CLACD: A complete LAne-Changing decision modeling framework for the connected and traditional environments

This study develops a Complete LAne-Changing Decision (CLACD) modeling framework that explains both the mandatory and discretionary lane-changing behaviors, incorporates both the successful and failed lane-changing attempts, and describes lane-changing behaviors in both the traditional and connected environments. The connected environment provides real-time and valuable information about surrounding vehicles and downstream situations (e.g., congestion ahead) that drivers cannot foresee. Such information can be particularly useful for complex driving maneuvers like discretionary lane-changing (DLC), which requires information about surrounding traffic speeds and subsequent gaps available on the adjacent lanes. DLC modeling efforts in the connected environment appear to be nascent, primarily because of: (a) the novelty of the connected environment (and the consequent scarcity of the relevant data), and (b) the lack of a behaviorally sound DLC model even for the traditional environment (that is, without driver-aid messages). Moreover, the existing models omit failed DLC attempts by motorists that are likely to create traffic disruptions and have a significant impact on traffic safety. To overcome these challenges, this study develops a DLC model (i.e., CLACD model) for the traditional environment, which is extended for the connected environment. An integrated approach is employed to model DLC behavior by combining the target lane selection using the utility theory approach and the gap acceptance behavior using a game theory approach. To collect high-quality vehicle trajectory data in the connected environment, the CARRS-Q advanced driving simulator was used. The CLACD models (for both the traditional and connected environments) are calibrated using NGSIM and the driving simulator data, and the CLACD models’ performances were rigorously assessed using various performance indicators. Results reveal that the CLACD models can effectively capture the observed DLC decisions with high accuracy. The performance of CLACD models is compared with the performance of two alternative models described in the literature, revealing that the CLACD models outperform the alternatives. A sensitivity analysis suggests a consistent trend of the CLACD models, which agrees with past research. Furthermore, the CLACD model shows a realistic prediction of traffic flow patterns compared with the utility theory model. By incorporating failed DLC attempts into the CLACD models, the performance and realism of CLACD models have been significantly improved.

Maximizing pleasure in day tours: Utility theory approach

AbstractA personalized day tour planning problem is studied to maximize the total pleasure gained from the tour. The concept of utility theory is employed for this purpose. Considering the uniform and diminishing marginal pleasure intensities, two zero–one mixed integer nonlinear programming models are proposed. Both the models can accommodate preferred time slots, visits to specific spots, and spots diversity. A branch‐and‐bound algorithm is used to solve the proposed models. Finally, they are applied to solve the day tour problem for Delhi, India. The tour plan obtained using the diminishing marginal pleasure intensity offers an improved value of total pleasure.

Fishery management in a regime switching environment: Utility theory approach

In this paper, we study the problem of optimal fishing for regime switching in the growth dynamics of a given fish species which is described by the differential stochastic logistic model with two states: prior or during floods and after. The resulting dynamic programming principle leads to a system of two variational inequalities. By using viscosity solutions approach, we prove the existence and uniqueness of the value functions. Then numerical approximation of the obtained system is used to determine the optimal fishing effort for a sustainable fishery.

The potential learning effect of a MCDA approach on consumer preferences for alternative fuel vehicles

Despite efforts from governments to increase the diffusion of more sustainable vehicles, such as alternative fuel vehicles (AFV), the market penetration of these vehicles has been difficult. Eliciting consumer preferences may provide valuable information on how to increase AFV diffusion. Since these are unfamiliar and complex products for most consumers, preferences are usually learnt during the process of elicitation. Preference learning is dependent on several factors, which include the type of elicitation task and its complexity. In this work, a stated preference survey was designed to analyze the potential impact of more complex elicitation tasks, multiattribute utility theory approach (MAUT), on the learning of preferences elicited through a traditional approach, choice-based conjoint analysis (CBC). The survey comprised two CBC sets of questions, one asked before and another asked after the MAUT. As a result three rankings of the vehicles set were obtained for each consumer, one derived from the initial set of CBC answers, a second one derived from the elicited MAUT model, and a third one derived from the second set of CBC answers. According to the results, there are significant differences from the first to the third ranking, possibly due to learning effects. Differences between the CBC-derived rankings were analyzed to assess if they were aligned with the MAUT model.

A multi-attribute utility theory approach to ordering policy for perishable items

Most retailers aim at operating with low cost and low stock-out probability. In this paper, we consider a retailer who manages the inventory of a perishable product using a continuous review (Q,r) policy with these two performance measures. We rely on the multi-attribute utility theory to combine these two measures into a single weighted utility function. We incorporate the decision maker’s risk-aversion/risk-seeking attitude. We obtain the optimal policy parameters by maximizing the weighted utility function. We conduct a numerical study to demonstrate the sensitivity of the optimal policy parameters to the risk-aversion/risk-seeking attitude of the decision maker and weights of evaluation measures. Our results reveal that the risk behavior does not have a significant effect on policy parameters while the weights of evaluation measures are influential. We compare our multi-attribute utility model with three well-known alternative (Q,r) policies. In the first one, we study the expected cost as a single performance measure. In the second one, we consider a service level constraint and in the third one, we use CVaR measure. We conclude that our multi-attribute approach has multiple favorable attributes compared to its widely used alternatives.

Modeling, analysis and multi-objective optimization of an industrial batch process for the production of tributyl citrate

This work describes the modeling and optimization of a trybutil citrate (TBC) production process at the industrial scale. The process comprises a batch reactive distillation for the esterification of citric acid with butanol. The dynamic model of the process was constructed based upon validated kinetic and thermodynamic models, implemented in gPROMSⓇ software, verified with industrial scale experiments, and used for further optimization to minimize batch time and energy consumption. Manipulated process variables were the molar ratio of butanol to citric acid, the alcohol loading policy, and the heat supply profile, and a Pareto front of optimal solutions was obtained. Depending on the main goal, the optimization process allowed to obtain energy savings up to 28%, and the corresponding processing times were reduced by 36%. Finally, multi-attribute utility theory (MAUT) approach was used as decision-making aid tool to select the preferred operating conditions for TBC production at the industrial scale.

Impacts of networking effects on software reliability growth processes: A multi‐attribute utility theory approach

AbstractSoftware reliability growth models, which are based on nonhomogeneous Poisson processes, are widely adopted tools when describing the stochastic failure behavior and measuring the reliability growth in software systems. Faults in the systems, which eventually cause the failures, are usually connected with each other in complicated ways. Considering a group of networked faults, we raise a new model to examine the reliability of software systems and assess the model's performance from real‐world data sets. Our numerical studies show that the new model, capturing networking effects among faults, well fits the failure data. We also formally study the optimal software release policy using the multi‐attribute utility theory (MAUT), considering both the reliability attribute and the cost attribute. We find that, if the networking effects among different layers of faults were ignored by the software testing team, the best time to release the software package to the market would be much later while the utility reaches its maximum. Sensitivity analysis is further delivered.

A modified principal component analysis-based utility theory approach for optimization of correlated responses of EDM process

Electrical discharge machining (EDM) is one of the most extensively used non-traditional machining processes having multiple performance characteristics, some of which are usually correlated. So, ideally, use of principal component analysis (PCA)-based approaches that take into account the possible correlations between the responses are suitable for optimization of EDM process. A recently reported study reveals that PCA-based proportion of quality loss reduction (PQLR) method results in the best optimization performance among the four considered PCA-based approaches for EDM process. This paper presents a modified PCA-based utility theory (UT) approach for optimization of correlated responses. The reported experimental data on EDM processes in literature are analyzed using the modified PCA-based UT approach and PCA-based PQLR method. Comparison of the predicted performance measures at the optimal process conditions derived based on these two PCA-based approaches reveal that the modified PCA-based UT approach leads to better overall optimization performance. So, it can be the most promising approach for optimizing EDM processes.Keywords: EDM; Correlated responses; Optimization; Principal component analysis; Proportion of quality loss reduction; Utility theory.

Decision of decentralization in a healthcare service – a case of diabetes management

ABSTRACTHealthcare services face a dual challenge of being accessible as well as affordable. Making health centres available at remote locations increases the accessibility of the service but results in low utilization of resources, which eventually results in higher cost of service. This study proposes a middle-path approach in which some components of services are available at the central facility only, while the others are available at central as well as remote locations. The study proposes and uses a Multi-Attribute Utility Theory (MAUT) approach to determine which component should be kept at the central facility. To elaborate the method, a case study approach using a diabetes management as a case organization was used. Taking a utility score of 0.5 as the cut-off point, the study concludes that Hospitalization, Neuropathy detection, Pathology, and Medicine (Pharmacy) should be kept at the central facility only. The study is first of its kind and provides an objective approach for decentralization in the case of healthcare services using the utility of the service components for a policy-maker as the criteria of selection.

Mirage events & driver haptic steering alerts in a motion-base driving simulator: A method for selecting an optimal HMI

This paper describes a new method, a ‘mirage scenario,’ to support formative evaluation of driver alerting or warning displays for manual and automated driving. This method provides driving contexts (e.g., various Times-To-Collision (TTCs) to a lead vehicle) briefly presented and then removed. In the present study, during each mirage event, a haptic steering display was evaluated. This haptic display indicated a steering response may be initiated to drive around an obstacle ahead. A motion-base simulator was used in a 32-participant study to present vehicle motion cues similar to the actual application. Surprise was neither present nor of concern, as it would be for a summative evaluation of a forward collision warning system. Furthermore, no collision avoidance maneuvers were performed, thereby reducing the risk of simulator sickness. This paper illustrates the mirage scenario procedures, the rating methods and definitions used with the mirage scenario, and analysis of the ratings obtained, together with a multi-attribute utility theory (MAUT) approach to evaluate and select among alternative designs for future summative evaluation.

A utility theory approach for insurance pricing

A utility theory approach for insurance pricing

A MAUT APPROACH FOR REUSING DOMAIN ONTOLOGIES ON THE BASIS OF THE NeOn METHODOLOGY

Knowledge resource reuse is becoming a widespread approach in the ontology engineering field because it can speed up the ontology development process. In this context, the NeOn Methodology specifies some guidelines for reusing different types of knowledge resources (ontologies, nonontological resources, and ontology design patterns). These guidelines prescribe how to perform the different activities involved in any of the diverse types of reuse processes. One such activity is to select the best knowledge resources for reuse in an ontology development. This selection activity is a complex decision-making problem involving conflicting objectives, like understandability, integration or reliability. We propose a multi-attribute utility theory (MAUT) approach to deal with the selection of the best domain ontologies for reuse, stressing the identification of attributes to measure ontology performances. We take advantage of the sensitivity analysis tools provided by the GMAA system, a PC-based decision support system based on an additive multi-attribute utility model, to exploit imprecise information on the inputs. An example concerning the selection of a subset of ontologies for reuse in the development of a new ontology in the sports domain illustrates the approach.

Optimization of multi-response dynamic systems using principal component analysis (PCA)-based utility theory approach

Article history: Received July 2 2013 Received in revised format September 7 2013 Accepted September 12 2013 Available online September 14 2013 Optimization of a multi-response dynamic system aims at finding out a setting combination of input controllable factors that would result in optimum values for all response variables at all signal levels. In real life situation, often the multiple responses are found to be correlated. The main advantage of PCA-based approaches is that it takes into account the correlation among the multiple responses. Two PCA-based approaches that are commonly used for optimization of multiple responses in dynamic system are PCA-based technique for order preference by similarity to ideal solution (TOPSIS) and PCA-based multiple criteria evaluation of the grey relational model (MCE-GRM). This paper presents a new PCA-based approach, called PCA-based utility theory (UT) approach, for optimization of multiple dynamic responses and compares its optimization performance with other existing PCA-based approaches. The results show that the proposed PCA-based UT method is superior to the other PCA-based approaches. © 2013 Growing Science Ltd. All rights reserved

Revealed Willingness to Pay for Leisure

In this paper, a new theoretical construct, the revealed willingness to pay for leisure, is presented as a link between two of the most powerful time use models that are aimed at understanding time allocation and valuing time: the microeconomic utility theory approach and the structural equations modeling method. This link is achieved by formulating a structural equations model (SEM) that keeps the generic features of the approach as applied to time use modeling but improves the incorporation of expenses in addition to activities and socioeconomic variables. The new equation on expenses in a leisure activity permits the calculation of a revealed willingness to pay for leisure, which the authors show to differ theoretically from the value of leisure that emerges from a micro-economic formulation (value of time as a resource). Using the same data from Santiago, Chile, the authors estimate both the SEM and the system of equations for working time, for the time allocated to a leisure activity, and for the expenses associated to a leisure activity, by following a micro-economic formulation. The authors confirm the theoretical derivations and conclude that the SEM does not permit the calculation of the full value of leisure and that the microeconomic approach needs an explicit constraint relating goods consumption and time use for an improved estimation of a most important component of the value of travel time savings.

Optimisation of the correlated responses of EDM process using modified principal component analysis-based utility theory

One of the most extensively used non-traditional machining processes is electrical discharge machining (EDM). Achieving the best quality performance from the EDM process requires simultaneous optimisation of multiple performance characteristics (responses). Researchers have applied different techniques of multi-response optimisation for optimising the EDM processes. But those approaches do not take care of the possible correlation between the responses, whereas some of the responses of EDM process are usually correlated. In recent times, different principal component analysis (PCA)-based approaches for multi-response optimisation have been proposed, which take into account the correlations among the responses. One of such approaches is the PCA-based utility theory (UT) approach. This paper highlights one weakness of the PCA-based UT approach and proposes the necessary modifications. Two sets of past experimental data on EDM processes are analysed using the modified procedure. The results show that the mod...

Green supplier selection generic framework: a multi-attribute utility theory approach

This paper presents a generic framework integrating environmental and social criteria leading to a comprehensive selection process of green suppliers. Traditionally, price, quality, lead time and flexibility are considered for the supplier selection. With the increase in awareness of environmental and social responsibility issues, many companies are tending towards adopting green concepts and sourcing green suppliers. This study proposes a framework consisting of environmental (E), green (G) and organisational (O) factors that are required for the green supplier selection process. These factors are further classified as criteria for which attributes are presented. A hierarchy is constructed to facilitate in evaluating the importance of the selected criteria and alternatives of green suppliers. To cater to the multi-criteria decision-making approach with both quantitative and qualitative attributes, we applied the multiple attribute utility theory, which is a decision support that helps managers formulating viable sourcing strategies. A hypothetical example is presented to illustrate the applicability of the approach.

Joint Analysis of Time Use and Consumer Expenditure Data

Estimating the value of time is of considerable interest to transportation professionals charged with evaluating infrastructure investments. Two approaches used to calculate the value of time are the microeconomic utility theory approach and the structural equations modeling method. In an effort to clarify the interpretation and the relationship between the values of time derived from these two approaches, both models were applied to a synthesized data set of one-person, one-worker households created by merging records from the 2008 American Time Use Survey data set with records from the 2008 Consumer Expenditure Survey data set of the United States. The microeconomic model results show that people in the sample data set work until the marginal utility of work is nearly zero. This finding implies that the value of leisure is nearly equal to the wage rate. Comparisons of model parameter estimates between the microeconomic model and the structural equations model suggest that the models offer vastly different measures of the value of leisure. Although the microeconomic model offers a utilitarian measure suitable for computing user benefits, the structural equations model provides a much smaller value of leisure, implying that it is a measure of the willingness to pay as represented by the average relationship between monetary expenditure and time allocation.