10,703 research outputs found
Should Optimal Designers Worry About Consideration?
Consideration set formation using non-compensatory screening rules is a vital
component of real purchasing decisions with decades of experimental validation.
Marketers have recently developed statistical methods that can estimate
quantitative choice models that include consideration set formation via
non-compensatory screening rules. But is capturing consideration within models
of choice important for design? This paper reports on a simulation study of a
vehicle portfolio design when households screen over vehicle body style built
to explore the importance of capturing consideration rules for optimal
designers. We generate synthetic market share data, fit a variety of discrete
choice models to the data, and then optimize design decisions using the
estimated models. Model predictive power, design "error", and profitability
relative to ideal profits are compared as the amount of market data available
increases. We find that even when estimated compensatory models provide
relatively good predictive accuracy, they can lead to sub-optimal design
decisions when the population uses consideration behavior; convergence of
compensatory models to non-compensatory behavior is likely to require
unrealistic amounts of data; and modeling heterogeneity in non-compensatory
screening is more valuable than heterogeneity in compensatory trade-offs. This
supports the claim that designers should carefully identify consideration
behaviors before optimizing product portfolios. We also find that higher model
predictive power does not necessarily imply better design decisions; that is,
different model forms can provide "descriptive" rather than "predictive"
information that is useful for design.Comment: 5 figures, 26 pages. In Press at ASME Journal of Mechanical Design
(as of 3/17/15
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
Application of data mining in scheduling of single machine system
The rapidly growing field of data mining has the potential of improving performance of existing scheduling systems. Such systems generate large amounts of data, which is often not utilized to its potential. The problem is whether it is possible to discover the implicit knowledge behind scheduling practice and then, with this knowledge, we could improve current scheduling practice. In this dissertation, we propose a novel methodology for generating scheduling rules using a data-driven approach. We show how to use data mining to discover previously unknown dispatching rules by applying the learning algorithms directly to production data. We also consider how by using this new approach unexpected knowledge and insights can be obtained, in a manner that would not be possible if an explicit model of the system or the basic scheduling rules had to be obtained beforehand. However, direct data mining of production data can at least mimic scheduling practices. The problem is whether scheduling practice could be improved with the knowledge discovered by data mining. We propose to combine data mining with optimization for effective production. In this approach, we use a genetic algorithm to find a heuristic solution to the optimal instances selection problem, and then induce a decision tree from this subset of instances. The optimal instance selection can be viewed as determining the best practices from what has been done in the past, and the data mining can then learn new dispatching rules from those best practices
Prescribed Performance Control Guided Policy Improvement for Satisfying Signal Temporal Logic Tasks
Signal temporal logic (STL) provides a user-friendly interface for defining
complex tasks for robotic systems. Recent efforts aim at designing control laws
or using reinforcement learning methods to find policies which guarantee
satisfaction of these tasks. While the former suffer from the trade-off between
task specification and computational complexity, the latter encounter
difficulties in exploration as the tasks become more complex and challenging to
satisfy. This paper proposes to combine the benefits of the two approaches and
use an efficient prescribed performance control (PPC) base law to guide
exploration within the reinforcement learning algorithm. The potential of the
method is demonstrated in a simulated environment through two sample
navigational tasks.Comment: This is the extended version of the paper accepted to the 2019
American Control Conference (ACC), Philadelphia (to be published
Consideration behavior and design decision making
Over the past decade, design engineering has developed a systematic framework to coordinate with consumer behavior models. Traditional consumer models applied in the past has mainly focused on the preference of compensatory trade-offs in the choice decisions. Recent marketing research has become interested in developing consumer models that are representative in that they reflect realistic human decision processes. One important example is consideration : the process of quickly screening out many available alternatives using non-compensatory rules before trading off the value of different feature combinations. Is capturing consideration important for design? This research investigates the impact of modeling consideration behavior to design engineering, aiming at constructing consideration models that can inform strategic decisions. The study includes several features absent in existing research: quantifying the mis-specifications of the underlying choice process, tailoring survey instruments for particular models, and exploring the models\u27 strategic value on product profitability and design feature differences.
First, numerical methods are explored to address the discontinuity in the profit-oriented optimization problem introduced by the consideration models. Methods based on complementarity constraints, smoothing functions and genetic algorithms are implemented and evaluated with a vehicle design case study. Second, a simulation experiment based on synthetic market data compares consideration models and a variety of conventional choice models in the process of model estimation and design optimization. The simulation finds that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to sub-optimal design decisions when the population uses consideration behavior; convergence of compensatory models to non-compensatory behavior is likely to require unrealistic amounts of data; modeling heterogeneity in non-compensatory screening is more valuable than heterogeneity in compensatory trade-offs. The synthetic experiment framework then further extends the comparison to include the survey design process guided by the different assumptions behind considerations and traditional models. A product line design case study reveals that even though both compensatory models and consideration models show robustness in profitability, using consideration models leads to optimal portfolios with higher feature diversity while reducing the risk of overestimating profits. Finally, the research explores how to use consideration models to analyze the market penetration of newly designed product in a case study of a consideration maximization problem.
It is the hope that this research will arouse the attention of designers to the informative power of consideration models, expand the understanding of consumer behavior modeling from the predictive power in the marketing field to the strategic impacts to design decisions, and provide technical support to the future application of consideration models in design engineering
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