Integrated Bayesian Hierarchical Choice Modeling to Capture Heterogeneous Consumer Preferences in Engineering Design

2010 ◽  
Vol 132 (12) ◽  
Author(s):  
Christopher Hoyle ◽  
Wei Chen ◽  
Nanxin Wang ◽  
Frank S. Koppelman
Keyword(s):  
Complex System ◽  
Engineering Design ◽  
Consumer Preferences ◽  
Critical Role ◽  
Choice Modeling ◽  
System Level ◽  
Multiple Model ◽  
Modeling Framework ◽  
Bayesian Hierarchical ◽  
Modeling Approach

Choice models play a critical role in enterprise-driven design by providing a link between engineering design attributes and customer preferences. However, existing approaches do not sufficiently capture heterogeneous consumer preferences nor address the needs of complex design artifacts, which typically consist of many subsystems and components. An integrated Bayesian hierarchical choice modeling (IBHCM) approach is developed in this work, which provides an integrated solution procedure and a highly flexible choice modeling approach for complex system design. The hierarchical choice modeling framework utilizes multiple model levels corresponding to the complex system hierarchy to create a link between qualitative attributes considered by consumers when selecting a product and quantitative attributes used for engineering design. To capture heterogeneous and stochastic consumer preferences, the mixed logit choice model is used to predict consumer system-level choices, and the random-effects ordered logit model is used to model consumer evaluations of system and subsystem level design features. In the proposed approach, both systematic and random consumer heterogeneity are explicitly considered, the ability to combine multiple sources of data for model estimation and updating is provided using the Bayesian estimation methodology, and an integrated estimation procedure is introduced to mitigate error propagated throughout the model hierarchy. The new modeling approach is validated using several metrics and validation techniques for behavior models. The benefits of the IBHCM method are demonstrated in the design of an automobile occupant package.

Bayesian Hierarchical Choice Modeling Framework for Capturing Heterogeneous Preferences in Engineering System Design

2009 ◽  
Author(s):  
Christopher Hoyle ◽  
Wei Chen ◽  
Nanxin Wang ◽  
Frank S. Koppelman
Keyword(s):  
System Design ◽  
Engineering Design ◽  
Choice Modeling ◽  
Engineering System ◽  
Multiple Model ◽  
Consumer Heterogeneity ◽  
Modeling Framework ◽  
Bayesian Hierarchical ◽  
Modeling Approach ◽  
Engineering System Design

Choice models play a critical role in enterprise-driven design by providing a link between engineering design attributes and customer preferences. In our previous work, we introduced the hierarchical choice modeling approach to address the special needs in complex engineering system design. The hierarchical choice modeling approach utilizes multiple model levels to create a link between qualitative attributes considered by consumers when selecting a product and quantitative attributes used for engineering design. In this work, the approach is expanded to the Bayesian Hierarchical Choice Modeling (BHCM) framework, estimated using an All-at-Once (AAO) solution procedure. This new framework addresses the shortcomings of the previous method while providing a highly flexible modeling framework to address the needs of complex system design. In this framework, both systematic and random consumer heterogeneity is explicitly considered, the ability to combine multiple sources of data for model estimation and updating is significantly expanded, and a method to mitigate error propagated throughout the model hierarchy is developed. In addition to developing the new choice model approach, the importance of including a complete representation of consumer heterogeneity in the model framework is provided. The new modeling framework is validated using several metrics and techniques. The benefits of the BHCM method are demonstrated in the design of an automobile occupant package.

A Framework for Choice Modeling in Usage Context-Based Design

2010 ◽  
Author(s):  
Lin He ◽  
Christopher Hoyle ◽  
Wei Chen ◽  
Jiliang Wang ◽  
Bernard Yannou
Keyword(s):  
Data Collection ◽  
Product Design ◽  
Consumer Choice ◽  
Choice Model ◽  
Consumer Preferences ◽  
Choice Modeling ◽  
Product Performance ◽  
Choice Data ◽  
Modeling Framework ◽  
Usage Context

Usage Context-Based Design (UCBD) is an area of growing interest within the design community. A framework and a step-by-step procedure for implementing consumer choice modeling in UCBD are presented in this work. To implement the proposed approach, methods for common usage identification, data collection, linking performance with usage context, and choice model estimation are developed. For data collection, a method of try-it-out choice experiments is presented. This method is necessary to account for the different choices respondents make conditional on the given usage context, which allows us to examine the influence of product design, customer profile, usage context attributes, and their interactions, on the choice process. Methods of data analysis are used to understand the collected choice data, as well as to understand clusters of similar customers and similar usage contexts. The choice modeling framework, which considers the influence of usage context on both the product performance, choice set and the consumer preferences, is presented as the key element of a quantitative usage context-based design process. In this framework, product performance is modeled as a function of both the product design and the usage context. Additionally, usage context enters into an individual customer’s utility function directly to capture its influence on product preferences. The entire process is illustrated with a case study of the design of a jigsaw.

scholarly journals Choice Modeling for Usage Context-Based Design

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
Lin He ◽  
Wei Chen ◽  
Christopher Hoyle ◽  
Bernard Yannou
Keyword(s):  
Hybrid Electric Vehicle ◽  
Consumer Preferences ◽  
Stated Preference ◽  
Revealed Preference ◽  
Choice Modeling ◽  
Modeling Framework ◽  
Preference Data ◽  
Design Paradigm ◽  
Usage Context ◽  
The Impact

Usage context-based design (UCBD) is an emerging design paradigm where usage context is considered as a critical part of driving factors behind customers’ choices. Here, usage context is defined as all aspects describing the context of product use that vary under different use conditions and affect product performance and/or consumer preferences for the product attributes. In this paper, we propose a choice modeling framework for UCBD to quantify the impact of usage context on customer choices. We start with defining a taxonomy for UCBD. By explicitly modeling usage context’s influence on both product performances and customer preferences, a step-by-step choice modeling procedure is proposed to support UCBD. Two case studies, a jigsaw example with stated preference data and a hybrid electric vehicle example with revealed preference data, demonstrate the needs and benefits of incorporating usage context in choice modeling.

Incorporating Customer Preferences and Market Trends in Vehicle Package Design

2007 ◽  
Author(s):  
Deepak Kumar ◽  
Chris Hoyle ◽  
Wei Chen ◽  
Nanxin Wang ◽  
Gianna Gomez-Levi ◽  
...  
Keyword(s):  
Choice Model ◽  
Critical Role ◽  
Choice Modeling ◽  
System Level ◽  
Demand Model ◽  
Package Design ◽  
Customer Preferences ◽  
Demand Models ◽  
Multiple Component ◽  
The Impact

Demand models play a critical role in enterprise-driven design by expressing revenues and costs as functions of product attributes. However, existing demand modeling approaches in the design literature do not sufficiently address the unique issues that arise when complex systems are being considered. Current approaches typically consider customer preferences for only quantitative product characteristics and do not offer a methodology to incorporate customer preference-data from multiple component/subsystem-specific surveys to make product-level design trade-offs. In this paper, we propose a hierarchical choice modeling approach that addresses the special needs of complex engineering systems. The approach incorporates the use of qualitative attributes and provides a framework for pooling data from multiple sources. Heterogeneity in the market and in customer-preferences is explicitly considered in the choice model to accurately reflect choice behavior. Ordered logistic regression is introduced to model survey-ratings and is shown to be free of the deficiencies associated with competing techniques, and a Nested Logit-based approach is proposed to estimate a system-level demand model by pooling data from multiple component/subsystem-specific surveys. The design of the automotive vehicle occupant package is used to demonstrate the proposed approach and the impact of both packaging design decisions and customer demographics upon vehicle choice are investigated. The focus of this paper is on demonstrating the demand (choice) modeling aspects of the approach rather than on the vehicle package design.

System Level Dynamic Modeling Framework Being Developed at Clemson University’s Wind Turbine Drivetrain Testing Facility

2013 ◽  
Author(s):  
Ryan Schkoda ◽  
Konstantin Bulgakov ◽  
Kalyan Chakravarthy Addepalli ◽  
Imtiaz Haque
Keyword(s):  
Wind Turbine ◽  
Dynamic Modeling ◽  
Preliminary Analysis ◽  
Collaborative Work ◽  
Model Development ◽  
System Level ◽  
Modeling Framework ◽  
Testing Facility ◽  
Simulation Strategy ◽  
North Charleston

This paper describes the system level, dynamic modeling and simulation strategy being developed at the Wind Turbine Drivetrain Testing Facility (WTDTF) at Clemson University’s Restoration Institute in North Charleston, SC, USA. An extensible framework that allows various workflows has been constructed and used to conduct preliminary analysis of one of the facility’s test benches. The framework dictates that component and subsystem models be developed according to a list of identified needs and modeled in software best suited for the particular task. Models are then integrated according to the desired execution target. This approach allows for compartmentalized model development which is well suited for collaborative work. The framework has been applied to one of the test benches and has allowed researches to begin characterizing its behavior in the time and frequency domain.

Modeling the response of non-perennial streams to climate change impact: The Bouregreg watershed in Morocco

2021 ◽  
Author(s):  
Anna Maria De Girolamo ◽  
Youssef Brouziyne ◽  
Lahcen Benaabidate ◽  
Aziz Aboubdillah ◽  
Ali El Bilali ◽  
...  
Keyword(s):  
Climate Change ◽  
Assessment Tool ◽  
Swat Model ◽  
Circulation Model ◽  
Hybrid Modeling ◽  
Climate Data ◽  
Global Circulation Model ◽  
Stream Network ◽  
Modeling Framework ◽  
Modeling Approach

<p>The non-perennial streams and rivers are predominant in the Mediterranean region and play an important ecological role in the ecosystem diversity in this region. This class of streams is particularly vulnerable to climate change effects that are expected to amplify further under most climatic projections. Understanding the potential response of the hydrologic regime attributes to climatic stress helps in planning better conservation and management strategies. Bouregreg watershed (BW) in Morocco, is a strategic watershed for the region with a developed non-perennial stream network, and with typical assets and challenges of most Mediterranean watersheds. In this study, a hybrid modeling approach, based on the Soil and Water Assessment Tool (SWAT) model and Indicator of Hydrologic Alteration (IHA) program, was used to simulate the response of BW's stream network to climate change during the period: 2035-2050. Downscaled daily climate data from the global circulation model CNRM-CM5 were used to force the hybrid modeling framework over the study area. Results showed that, under the changing climate, the magnitude of the alteration will be different across the stream network; however, almost the entire flow regime attributes will be affected. Under the RCP8.5 scenario, the average number of zero-flow days will rise up from 3 to 17.5 days per year in some streams, the timing of the maximum flow was calculated to occur earlier by 17 days than in baseline, and the timing of the minimal flow should occur later by 170 days in some streams. The used modeling approach in this study contributed in identifying the most vulnerable streams in the BW to climate change for potential prioritization in conservation plans.</p>

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