The Applicability of Bayesian Belief Networks for Measuring User Preferences: Some Numerical Simulations

Orzechowski, Maciej; Arentze, TA Theo; Borgers, Awj Aloys; Timmermans, Hjp Harry

doi:10.1068/b32054

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…If it would turn out that attributes are not grouped hierarchically, but that a non-hierarchical set of relationships between attributes and decision constructs would be obtained, a different representation mechanism would be required. For example, Orzechowski et al (2008) used Bayesian belief networks for measuring preferences.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Hierarchical Information Integration Experiments and Integrated Choice Experiments

Molin

Timmermans

2009

Transport Reviews

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When conjoint experiments are applied to study complex decision making that involve many attributes, this often results in problems of information overload and respondent burden, potentially jeopardizing the validity of such experiments. To avoid or reduce the impact of these potential problems, Hierarchical Information Integration has been suggested. The key notion is to classify the large number of potentially influential attributes into a smaller set of decision constructs, construct separate experimental designs for each of these constructs and in addition a bridging design that allows the scaling of all part-worth utilities into a concatenated utility expression. The basic approach suggested for preference measurements has been elaborated for other measurement tasks and the original design strategy has been refined into an alternative approach. This paper summarizes these developments and briefly discusses aspects of respondent burden and validity.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Hierarchical Information Integration Experiments and Integrated Choice Experiments

Molin

Timmermans

2009

Transport Reviews

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“…When the amount of data is insufficient for sampling, the BN technique for estimation when used together with analytical decision-making tools can be helpful for decision-makers [42]. The interdependent associations between variables are found presuming they have conditional probability distribution employing the cause and result effects of existing variables in the parameter learning of the Bayesian belief network [43].…”

Section: Bayesian Networkmentioning

confidence: 99%

Lean Energy Efficiency: Scenario Analysis of a Refrigerator Plant Application

ALBAYRAK,

DUZDAR ARGUN,

KAYAKUTLU

et al. 2023

KONJES

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Energy concern is increasing in the manufacturing companies implementing the most recent technologies. Energy is a major input for many industries and therefore, within the definition of Industry 4.0 new energy efficiency strategies are defined. Energy flexible processes and waste-to-energy are well-known strategies since they are easily implemented in any manufacturing site. Reduction in energy consumption is also facilitated by the preventive maintenance and renewal of the technologies using energy resources. This study aims to apply a lean production method Value Stream Maps (VSM) on energy consumption levels of the processes to evaluate the decision of technology and/or process change. Processes which use energy excessively when compared with the industry average, will be considered as a bottleneck. Using the value stream maps for energy use is accepted as “Lean Energy Efficiency”. Parameters determined by the preparation of these maps will be enriched by interdependencies determined using the Bayesian Belief Network, which will support finding the priorities among the new efficiency activities. This technique will facilitate the decision of repair or buy through priority scenarios showing the possibility of a decision in each scenario. The combination of the Lean Energy Efficiency Method and the Bayesian Belief Network Method will assist the decision-makers in developing more informed and knowledge-based strategies. The case study is realized in a refrigerator factory handling the energy consumption of different departments. Consequently, these findings could be used as a roadmap for the technology renewal investment decisions made by the firm.

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Computational Optimisation of Urban Design Models: A Systematic Literature Review

Tay,

Ortner,

Wortmann

et al. 2024

Urban Science

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The densification of urban spaces globally has contributed to a need for design tools supporting the planning of more sustainable, efficient, and liveable cities. Urban Design Optimisation (UDO) responds to this challenge by providing a means to explore many design solutions for a district, evaluate multiple objectives, and make informed selections from many Pareto-efficient solutions. UDO distinguishes itself from other forms of design optimisation by addressing the challenges of incorporating a wide range of planning goals, managing the complex interactions among various urban datasets, and considering the social–technical aspects of urban planning involving multiple stakeholders. Previous reviews focusing on specific topics within UDO do not sufficiently address these challenges. This PRISMA systematic literature review provides an overview of research on topics related to UDO from 2012 to 2022, with articles analysed across seven descriptive categories. This paper presents a discussion on the state-of-the-art and identified gaps present in each of the seven categories. Finally, this paper argues that additional research to improve the socio-technical understanding and usability of UDO would require: (i) methods of optimisation across multiple models, (ii) interfaces that address a multiplicity of stakeholders, (iii) exploration of frameworks for scenario building and backcasting, and (iv) advancing AI applications for UDO, including generalizable surrogates and user preference learning.

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The Applicability of Bayesian Belief Networks for Measuring User Preferences: Some Numerical Simulations

Cited by 3 publications

References 8 publications

Hierarchical Information Integration Experiments and Integrated Choice Experiments

Hierarchical Information Integration Experiments and Integrated Choice Experiments

Lean Energy Efficiency: Scenario Analysis of a Refrigerator Plant Application

Computational Optimisation of Urban Design Models: A Systematic Literature Review

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