Value-Driven Design Using Discipline-Based Decomposition for a Family of Front-Loading Washing Machines

2017 ◽  
Author(s):  
Sangjin Jung ◽  
Timothy W. Simpson ◽  
Christina Bloebaum
Keyword(s):  
Design Research ◽  
Value Function ◽  
Product Line ◽  
Target Market ◽  
Washing Machines ◽  
Multi Level ◽  
Line Level ◽  
Future Work ◽  
The Value Function ◽  
Corporate Level

In order to determine target market and price, and design products/components for a family of front-loading washing machines, the coordination for decision-making from the corporate level down to the product and ultimately component levels is required. However, existing design research for many products focuses on analyzing single or multiple disciplines, even though optimizing local performance does not guarantee minimizing total cost at the product line level or maximizing value at the company level. In this work, we apply a multi-level value-driven design (VDD) approach to optimize a family of front-loading washing machines using a discipline-based decomposition. The VDD solutions obtained using discipline-based decomposition (DD) are compared with those obtained using product-based decomposition (PD). Consequently, the multi-level VDD approach based on DD for the washer family provides better performance for attributes than PD, but we observed that DD for the washer family does not guarantee maximizing the value function compared to PD because of the larger numbers of subsystems and consistency-related variables. Ongoing and future work to address this problem are discussed.

Multi-Level Value-Driven Design Approaches for Product Family Design

2017 ◽  
Author(s):  
Sangjin Jung ◽  
Timothy W. Simpson ◽  
Christina Bloebaum
Keyword(s):  
Product Family ◽  
Optimization Methods ◽  
Value Functions ◽  
Product Family Design ◽  
Design Problems ◽  
Cost Performance ◽  
Best Value ◽  
Multi Level ◽  
Future Work ◽  
The Value Function

Companies usually launch families of products into the market to provide value to different segments based on different customer needs; however, most of the research on Value-Driven Design (VDD) in the literature has focused on modeling value functions and optimizing the design of single products, not families of products. In order to increase profit and minimize total cost for product design and manufacturing, VDD should be applicable to product family design. In this work, we propose a multi-level VDD approach for product family design by extending multidisciplinary design optimization methods. The multi-level VDD is applied to a family of front-loading washing machines to validate the effectiveness of the proposed approach. With this example, we demonstrate that design problems that optimize traditional objective functions (e.g., cost, performance) at each level do not necessarily maximize value when compared to an appropriate VDD formulation. On the other hand, when the value function is set as an objective function throughout the organization (company, product family, and product level), we find that the VDD formulation provides the best value. Future work based on these promising findings is also discussed.

The value function for time-related decisions

2011 ◽  
Author(s):  
Anouk Festjens ◽  
Siegfried Dewitte ◽  
Enrico Diecidue ◽  
Sabrina Bruyneel
Keyword(s):  
Value Function ◽  
The Value Function

scholarly journals Pricing Perpetual American Put Options with Asset-Dependent Discounting

2021 ◽  
Vol 14 (3) ◽  
pp. 130
Author(s):  
Jonas Al-Hadad ◽  
Zbigniew Palmowski
Keyword(s):  
Value Function ◽  
Asset Price ◽  
Stopping Times ◽  
Martingale Measure ◽  
Put Options ◽  
American Put Options ◽  
Exact Calculations ◽  
Negative Exponential ◽  
American Put ◽  
The Value Function

The main objective of this paper is to present an algorithm of pricing perpetual American put options with asset-dependent discounting. The value function of such an instrument can be described as VAPutω(s)=supτ∈TEs[e−∫0τω(Sw)dw(K−Sτ)+], where T is a family of stopping times, ω is a discount function and E is an expectation taken with respect to a martingale measure. Moreover, we assume that the asset price process St is a geometric Lévy process with negative exponential jumps, i.e., St=seζt+σBt−∑i=1NtYi. The asset-dependent discounting is reflected in the ω function, so this approach is a generalisation of the classic case when ω is constant. It turns out that under certain conditions on the ω function, the value function VAPutω(s) is convex and can be represented in a closed form. We provide an option pricing algorithm in this scenario and we present exact calculations for the particular choices of ω such that VAPutω(s) takes a simplified form.

scholarly journals Robo-Advising: Learning Investors’ Risk Preferences via Portfolio Choices*

2020 ◽  
Author(s):  
Humoud Alsabah ◽  
Agostino Capponi ◽  
Octavio Ruiz Lacedelli ◽  
Matt Stern
Keyword(s):  
Opportunity Cost ◽  
Value Function ◽  
Risk Preference ◽  
Portfolio Decisions ◽  
Learning Framework ◽  
Portfolio Choices ◽  
Trading Decisions ◽  
Exploration Exploitation ◽  
The Value Function ◽  
Over Time

Abstract We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor’s risk preference but learns it over time by observing her portfolio choices in different market environments. We develop an exploration–exploitation algorithm that trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor’s risk aversion. We show that the approximate value function constructed by the algorithm converges to the value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor’s mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor’s opportunity cost for making portfolio decisions.

Metric for Disassembly and Reuse Decisions: Formulation and Validation

2008 ◽  
Author(s):  
Vijitashwa Pandey ◽  
Deborah Thurston
Keyword(s):  
Maximum Likelihood ◽  
Value Function ◽  
Choice Theory ◽  
Decision Makers ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Maximum Value ◽  
Long Range Planning ◽  
The Relationship ◽  
The Value Function

Design for disassembly and reuse focuses on developing methods to minimize difficulty in disassembly for maintenance or reuse. These methods can gain substantially if the relationship between component attributes (material mix, ease of disassembly etc.) and their likelihood of reuse or disposal is understood. For products already in the marketplace, a feedback approach that evaluates willingness of manufacturers or customers (decision makers) to reuse a component can reveal how attributes of a component affect reuse decisions. This paper introduces some metrics and combines them with ones proposed in literature into a measure that captures the overall value of a decision made by the decision makers. The premise is that the decision makers would choose a decision that has the maximum value. Four decisions are considered regarding a component’s fate after recovery ranging from direct reuse to disposal. A method on the lines of discrete choice theory is utilized that uses maximum likelihood estimates to determine the parameters that define the value function. The maximum likelihood method can take inputs from actual decisions made by the decision makers to assess the value function. This function can be used to determine the likelihood that the component takes a certain path (one of the four decisions), taking as input its attributes, which can facilitate long range planning and also help determine ways reuse decisions can be influenced.

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