The Open Workshop on Decision-Based Design

2006 ◽  
pp. 5-11 ◽  
Keyword(s):  
Open Workshop ◽  
Decision Based Design

A Goal-Oriented, Inverse Decision-Based Design Method to Achieve the Vertical and Horizontal Integration of Models in a Hot Rod Rolling Process Chain

2017 ◽  
Author(s):  
Anand Balu Nellippallil ◽  
Vignesh Rangaraj ◽  
B. P. Gautham ◽  
Amarendra Kumar Singh ◽  
Janet K. Allen ◽  
...  
Keyword(s):  
Manufacturing Process ◽  
Design Method ◽  
Manufacturing Processes ◽  
Process Chain ◽  
Structure Property ◽  
Horizontal Integration ◽  
Design Exploration ◽  
Unit Operations ◽  
Manufacturing Process Chain ◽  
Decision Based Design

Reducing the manufacturing and marketing time of products by means of integrated simulation-based design and development of the material, product, and the associated manufacturing processes is the need of the hour for industry. This requires the design of materials to targeted performance goals through bottom-up and top-down modeling and simulation practices that enables handshakes between modelers and designers along the entire product realization process. Manufacturing a product involves a host of unit operations and the final properties of the manufactured product depends on the processing steps carried out at each of these unit operations. In order to effectively couple the material processing-structure-property-performance spaces, there needs to be an interplay of the systems-based design of materials with enhancement of models of various unit operations through multiscale modeling methodologies and integration of these models at different length scales (vertical integration). This ensures the flow of information from one unit operation to another thereby establishing the integration of manufacturing processes (horizontal integration). Together these types of integration will support the decision-based design of the manufacturing process chain so as to realize the end product. In this paper, we present a goal-oriented, inverse decision-based design method to achieve the vertical and horizontal integration of models for the hot rolling and cooling stages of the steel manufacturing process chain for the production of a rod with defined properties. The primary mathematical construct used for the method presented is the compromise Decision Support Problem (cDSP) supported by the proposed Concept Exploration Framework (CEF) to generate satisficing solutions under uncertainty. The efficacy of the method is illustrated by exploring the design space for the microstructure after cooling that satisfies the requirements identified by the end mechanical properties of the product. The design decisions made are then communicated in an inverse manner to carry out the design exploration of the cooling stage to identify the design set points for cooling that satisfies the new target microstructure requirements identified. Specific requirements such as managing the banded microstructure to minimize distortion in forged gear blanks are considered in the problem. The proposed method is generic and we plan to extend the work by carrying out the integrated decision-based design exploration of rolling and reheating stages that precede to realize the end product.

Enabling Decision-Based Design Using Solution Trajectory Correction and Backtracking Rules

2012 ◽  
Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos ◽  
Monica Majcher
Keyword(s):  
Optimal Design ◽  
Utility Function ◽  
Design Space ◽  
A Priori ◽  
Preference Order ◽  
Beam Design ◽  
Effective Decision ◽  
Suboptimal Design ◽  
Trajectory Correction ◽  
Decision Based Design

Optimization is needed for effective decision based design (DBD). However, a utility function assessed a priori in DBD does not usually capture the preferences of the decision maker over the entire design space. As a result, when the optimizer searches for the optimal design, it traverses (or ends up) in regions where the preference order among different solutions is different from the actual order. For a highly non-convex design space, this can lead to convergence to a grossly suboptimal design depending on the initial design. In this article, we propose two approaches to alleviate this issue. First, we map the trajectory of the solution as generated by the optimizer and generate ranking questions that are presented to the designer to verify the correctness of the utility function. We then propose backtracking rules if a local utility function is very different from the initially assessed function. We demonstrate our methodology using a mathematical example and a welded beam design problem.

An Implementation of Expected Utility Theory in Decision Based Design

1998 ◽  
Author(s):  
Matthew Marston ◽  
Farrokh Mistree
Keyword(s):  
Expected Utility ◽  
Utility Theory ◽  
Design Science ◽  
Expected Utility Theory ◽  
Simple Design ◽  
Von Neumann ◽  
Decision Based Design ◽  
Mathematics And Physics ◽  
Science Of The Artificial ◽  
The Ideal

Abstract The development of a design science rests on the ideal that design is anchored in a set of fundamental axioms similar to the more ‘traditional’ sciences of mathematics and physics. However, the axioms upon which a design science is constructed must reflect that design is a science of the artificial. It is our contention that such axioms may exist in Decision-Based Design as those formulated by von-Neumann and Morgenstern for developing utilities under conditions of risk. In this paper we have a very narrow focus: evaluating a proposed framework for applying these axioms in the context of a simple design problem through the use of Monte Carlo simulation and expected utility theory.

Integrating Distributed Design Information in Decision-Based Design

2009 ◽  
pp. 67-90
Author(s):  
Justin A. Rockwell ◽  
Sundar Krishnamurty ◽  
Ian R. Grosse ◽  
Jack Wileden
Keyword(s):  
Distributed Design ◽  
Design Information ◽  
Decision Based Design

A Framework for Decision-Based Engineering Design

1998 ◽  
Vol 120 (4) ◽  
pp. 653-658 ◽  
Author(s):  
G. A. Hazelrigg
Keyword(s):  
Engineering Design ◽  
Operations Research ◽  
Quality Function Deployment ◽  
Value Theory ◽  
Decision Making Process ◽  
Design Decisions ◽  
Decision Sciences ◽  
Research Decision ◽  
Decision Based Design ◽  
Total Life

Engineering design is increasingly recognized as a decision-making process. This recognition brings with it the richness of many well-developed theories and methods from economics, operations research, decision sciences, and other disciplines. Done correctly, it forces the process of engineering design into a total systems context, and demands that design decisions account for a product’s total life cycle. It also provides a theory of design that is based on a rigorous set of axioms that underlie value theory. But the rigor of decision-based design also places stringent conditions on the process of engineering design that eliminate popular approaches such as Quality Function Deployment. This paper presents the underlying notions of decision-based design, points to some of the axioms that underlie the theory of decision-based design, and discusses the consequences of the theory on engineering education.

Assessing Lifecycle Value Using Object-Based Modeling by Incorporating Excess and Changeability

2020 ◽  
Vol 143 (5) ◽  
Author(s):  
Daniel Long ◽  
Scott Ferguson
Keyword(s):  
System Level ◽  
Change Propagation ◽  
Processing Unit ◽  
Component Level ◽  
Desktop Computer ◽  
Central Processing ◽  
Generic Modeling ◽  
Supply Capacity ◽  
Uncertain Future ◽  
Decision Based Design

Abstract Prior research suggests that excess (purposeful inclusion of margin beyond what is required for known system uncertainties) can limit change propagation and reduce system modifications. Reducing change costs increases system flexibility, permitting adaptions that satisfy uncertain future requirements. The benefits of excess, however, must be traded against higher costs of the initial system and likely performance decreases. Assessing the benefits and costs of excess requires evaluating what forms, locations, and magnitudes of excess inclusion are optimal. This paper improves the state-of-the-art in two ways. First, prior research has generally assessed excess in system-level properties (aggregating component properties into a single metric). The approach presented in this paper extends excess assessment to the component level so that the effects of excess on change propagation may be explicitly captured. Second, this approach holistically assesses the value of excess by evaluating both its costs and benefits. The approach borrows from Decision-Based Design and Model Based System Engineering (MBSE) in creating a generic modeling method capable of excess valuation. A desktop computer example is used for demonstrating how excess is valued in a system and the potential gains associated with excess inclusion when mining cryptocurrency. A single component optimization of the power supply capacity for the desktop is assessed to be 750 W, which balances the initial cost against the future flexibility. A system-level optimization then demonstrates the identification of critical change propagation pathways and illuminates both where and how excess may be included to inhibit change propagation. This key component was identified as the motherboard-central processing unit (CPU) slot in the tested systems.

Towards a Hybrid Solver: Integration of a Genetic Algorithm Within “DSIDES”

2002 ◽  
Author(s):  
Shuiwei Xie ◽  
Warren F. Smith
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Linear Programming ◽  
Decision Support ◽  
Hybrid Genetic Algorithm ◽  
Systems Design ◽  
Nonlinear Problems ◽  
The Body ◽  
Hybrid Solver ◽  
Decision Based Design

In contributing to the body of knowledge for decision-based design, the work reported in this paper has involved steps towards building a hybrid genetic algorithm to address systems design. Highlighted is a work in progress at the Australian Defence Force Academy (ADFA). A genetic algorithm (GA) is proposed to deal with discrete aspects of a design model (e.g., allocation of space to function) and a sequential linear programming (SLP) method for the continuous aspects (e.g., sizing). Our historical Decision Based Design (DBD) tool has been the code DSIDES (Decision Support In the Design of Engineering Systems). The original functionality of DSIDES was to solve linear and non-linear goal programming styled problems using linear programming (LP) and sequential (adaptive) linear programming (SLP/ALP). We seek to enhance DSIDES’s solver capability by the addition of genetic algorithms. We will also develop the appropriate tools to deal with the decomposition and synthesis implied. The foundational paradigm for DSIDES, which remains unchanged, is the Decision Support Problem Technique (DSPT). Through introducing genetic algorithms as solvers in DSIDES, the intention is to improve the likelihood of finding the global minimum (for the formulated model) as well as the ability of dealing more effectively with nonlinear problems which have discrete variables, undifferentiable objective functions or undifferentiable constraints. Using some numerical examples and a practical ship design case study, the proposed GA based method is demonstrated to be better in maintaining diversity of populations, preventing premature convergence, compared with other similar GAs. It also has similar effectiveness in finding the solutions as the original ALP DSIDES solver.

Decision-Based Collaborative Optimization Under Uncertainty

2000 ◽  
Author(s):  
Xiaoyu Gu ◽  
John E. Renaud ◽  
Leah M. Ashe ◽  
Stephen M. Batill ◽  
Amarjit S. Budhiraja ◽  
...  
Keyword(s):  
Systems Design ◽  
Collaborative Optimization ◽  
Optimization Under Uncertainty ◽  
System Level ◽  
Optimization Strategy ◽  
Business Decisions ◽  
Single Level ◽  
Optimization Framework ◽  
Multidisciplinary Systems ◽  
Decision Based Design

Abstract In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg (1996a, 1998) is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming the Hazelrigg framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.

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