Pooling stated and revealed preference data in the presence of RP endogeneity

Pooled discrete choice models combine revealed preference (RP) data and stated preference (SP) data to exploit advantages of each. SP data is often treated with suspicion because consumers may respond differently in a hypothetical survey context than they do in the marketplace. However, models built on RP data can suffer from endogeneity bias when attributes that drive consumer choices are unobserved by the modeler and correlated with observed variables. Using a synthetic data experiment, we test the performance of pooled RP–SP models in recovering the preference parameters that generated the market data under conditions that choice modelers are likely to face, including (1) when there is potential for endogeneity problems in the RP data, such as omitted variable bias, and (2) when consumer willingness to pay for attributes may differ from the survey context to the market context. We identify situations where pooling RP and SP data does and does not mitigate each data source’s respective weaknesses. We also show that the likelihood ratio test, which has been widely used to determine whether pooling is statistically justifiable, (1) can fail to identify the case where SP context preference differences and RP endogeneity bias shift the parameter estimates of both models in the same direction and magnitude and (2) is unreliable when the product attributes are fixed within a small number of choice sets, which is typical of automotive RP data. Our findings offer new insights into when pooling data sources may or may not be advisable for accurately estimating market preference parameters, including consideration of the conditions and context under which the data were generated as well as the relative balance of information between data sources.This work was supported in part by a grant from the Link Foundation, a grant from the National Science Foundation # 1064241 , and a grant from Ford Motor Company. The opinions expressed are those of the authors and not necessarily those of the sponsors.Accepted manuscrip

An efficient weighting update method to achieve acceptable inconsistency deviation in analytical target cascading.

Weighting coefficients are used in analytical target cascading (ATC

Global Optimization of Plug-In Hybrid Vehicle Design and Allocation to Minimize Life Cycle Greenhouse Gas Emissions

We pose a reformulated model for optimal design and allocation of conventional (CV), hybrid electric (HEV), and plug-in hybrid electric (PHEV) vehicles to obtain global solutions that minimize life cycle greenhouse gas (GHG) emissions of the fleet. The reformulation is a twice-differentiable, factorable, nonconvex mixed-integer nonlinear programming (MINLP) model that can be solved globally using a convexification-based branch-and-reduce algorithm. We compare results to a randomized multistart local-search approach for the original formulation and find that local-search algorithms locate global solutions in 59% of trials for the two-segment case and 18% of trials for the three-segment case. The results indicate that minimum GHG emissions are achieved with a mix of PHEVs sized for 25-45 miles of electric travel. Larger battery packs allow longer travel on electrical energy, but production and weight of underutilized batteries result in higher GHG emissions. Under the current average U.S. grid mix, PHEVs offer a nearly 50% reduction in life cycle GHG emissions relative to equivalent conventional vehicles and about 5% improvement over HEVs when driven on the standard urban driving cycle. Optimal allocation of different PHEVs to different drivers turns out to be of second order importance for minimizing net life cycle GHGs

Preference coordination in engineering design decision-making.

Whether interested in profit or in social welfare, designers are concerned with the preferences people have and the choices they make. Tools such as Quality Function Deployment have been developed to help designers organize thinking about the relationship between design decisions and stakeholder preferences; however, work incorporating explicit quantitative models of stakeholder preferences into engineering design decision making is still sparse. Preference coordination draws on theory and methods from the marketing, economics, and psychology literatures to model preference structures and to coordinate them effectively with design models of engineering feasibility and performance for achieving jointly optimal solutions with both technical and market feasibility. This process resolves tradeoffs among competing technical objectives while ensuring that product targets based on market preferences are physically realizable. Specifically, theory is reviewed and developed for analytical target cascading (ATC), a methodology for decomposing a system into a hierarchy of subsystems and coordinating optimization of each subsystem so as to achieve the joint solution. The ATC methodology is then applied to coordinate marketing and engineering design decision models in a profit-seeking firm. It is demonstrated with a case study that the joint solution obtained through coordination is superior to the solution obtained by treating each discipline independently. The modularity of the framework facilitates extensions, and two such extensions are pursued: First, the methodology is extended for product line design by coordinating preference models that capture heterogeneity with a set of engineering design models. Second, manufacturing decisions are incorporated by adding a module to coordinate machine investment and allocation decisions. Finally, the scope of preferences is expanded to explore social preferences as expressed through regulation: Game theory is used to predict the design decisions made by profit-seeking producers in a competitive marketplace, and the effects of different regulation scenarios on the resulting decisions are examined. It is the hope that the methods developed in this dissertation for modeling stakeholder preferences and coordinating with engineering design decision-making will help design engineers and managers to understand the relationship between their decisions and the interests upon which they have impact so that better, more informed decision-making can be realized.Ph.D.Applied SciencesMarketingMechanical engineeringSocial SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/124888/2/3163888.pd

Interactive design optimization of architectural layouts. Engineering Optimization (this issue

Many areas of design involve both quantifiable and subjective goals, preferences, and constraints. Aesthetic and other subjective aspects of design are typically ignored in optimization models because they are difficult to model with mathematics; however, they are extremely important in areas such as product design and architectural design. This article presents an interactive method for integrating mathematical optimization with human decision-making during conceptual design of architectural floorplan layouts. The optimization models and algorithms were presented in a previous article. Here, an object-oriented representation allows the designer to interact with physically relevant building objects during optimization. The designer’s interaction causes the program to dynamically change the optimization representation on-the-fly by adding, deleting, and modifying objectives, constraints, and structural units. This work presents mathematical optimization as a tool to assist the designer in refining ill-defined design problems during the early conceptual design phase. The designer can quickly explore design alternatives visually and computationally by taking advantage of computational algorithms to maintain feasibility and compute efficient solutions

Should Designers Worry About Market Systems?

Engineering approaches for optimizing designs within a market context generally take the perspective of a single producer, asking what design and price point will maximize producer profit predicted by consumer choice simulations. These approaches treat competitors and retailers as fixed or nonexistent, and they take business-oriented details, such as the structure of distribution channels, as separate issues that can be addressed post hoc by other disciplines. It is well established that the structure of market systems influences optimal product pricing. In this paper, we investigate whether two types of these structures also influence optimal product design decisions; specifically, 1) consumer heterogeneity and 2) distribution channels. We first model firms as players in a profit-seeking game that compete on product attributes and prices. We then model the interactions of manufacturers and retailers in Nash competition under alternative market structures and compare the equilibrium conditions for each case. We find that when consumers are modeled as homogeneous in their preferences, optimal design can be decoupled from the game, and design decisions can be made without regard to price, competition, or channel structure. However, when consumer preferences are heterogeneous, the behavior of competitors and retailers is key to determining which designs are profitable. We examine the extent of this effect in a vehicle design case study from the literature and find that the presence of heterogeneity leads different market structures to imply significantly different profit-maximizing designs

Evaluation of the Effects of Thermal Management on Battery Life in Plug-in Hybrid Electric Vehicles

<p>We develop a simulation model that aims to evaluate the effect of thermal management on battery life. The model consists of two submodels: a thermal model and a battery degradation model. The temperature rise in the battery is calculated using the thermal model, and a temperature profile is obtained under pre-defined driving, charging and stand-by scenarios. The temperature profile and the energy requirement required to achieve a driving profile act as inputs to the degradation sub-model, which is used to predict the battery life. The degradation model is derived from models and test data available in literature, and the model is constructed for aircooled cylindrical LiFePO4 cells based on the Hymotion Prius-conversion configuration. Preliminary results suggest that peak temperatures have the greatest impact on degradation: Thermal management increases life substantially in climates with high peak temperatures (Pheonix) and for more aggressive driving cycles (US06), while thermal management has less influence in climates with lower peak temperatures (Miami) and with gentle driving cycles (UDDS). Use of cabin air vs. outside air for thermal management has minor impact on battery life for the control strategy used, but thermostat control settings are important for lowering peak temperatures and extending battery life</p