A systematic approach for integrated product, materials, and design-process design

Designers are challenged to manage customer, technology, and socio-economic uncertainty causing dynamic, unquenchable demands on limited resources. In this context, increased concept flexibility, referring to a designer s ability to generate concepts, is crucial. Concept flexibility can be significantly increased through the integrated design of product and material concepts. Hence, the challenge is to leverage knowledge of material structure-property relations that significantly affect system concepts for function-based, systematic design of product and materials concepts in an integrated fashion. However, having selected an integrated product and material system concept, managing complexity in embodiment design-processes is important. Facing a complex network of decisions and evolving analysis models a designer needs the flexibility to systematically generate and evaluate embodiment design-process alternatives. In order to address these challenges and respond to the primary research question of how to increase a designer s concept and design-process flexibility to enhance product creation in the conceptual and early embodiment design phases, the primary hypothesis in this dissertation is embodied as a systematic approach for integrated product, materials and design-process design. The systematic approach consists of two components i) a function-based, systematic approach to the integrated design of product and material concepts from a systems perspective, and ii) a systematic strategy to design-process generation and selection based on a decision-centric perspective and a value-of-information-based Process Performance Indicator. The systematic approach is validated using the validation-square approach that consists of theoretical and empirical validation. Empirical validation of the framework is carried out using various examples including: i) design of a reactive material containment system, and ii) design of an optoelectronic communication system.Ph.D.Committee Chair: Allen, Janet K.; Committee Member: Aidun, Cyrus K.; Committee Member: Klein, Benjamin; Committee Member: McDowell, David L.; Committee Member: Mistree, Farrokh; Committee Member: Yoder, Douglas P

Model evolution for the realization of complex systems

George Box said, “All models are wrong, but some are useful.” In the design of complex systems, types of complexity need to be managed. Giving the complexities that a decision maker may encounter, corresponding adjustments or improvements should be made to the design. In this dissertation, it is defined that all kinds of engineering design are comprised of four stages – formulation, approximation, exploration and evaluation – and the four stages form the model evolution loop or design evolution loop. By running the design evolution loop iteratively, a designer can handle the complexities and improve the design. Such improvements include but not limited to more robust to uncertainties, more efficient in design evolutions, easier interpretations of phenomena, etc. In the design of complex systems, as lack of data and information, heuristics are used to proceed the design, so that designers can explore the solution space and gain insight to improve the design. Those heuristics include but not limit to model structures, sub-problems identification and integration, approximation rules, and scale of details incorporated in the model. There is lacking mechanisms to evaluate the quality of the design associated with the heuristics. In this dissertation, it is hypothesized that by running the design evolution loop and exploring the solution space, designers can do the things as follows to improve the design. • Evaluating system performances associated with various heuristics (structure of the model, critical parameter setting, rules making, etc.). • Replacing the heuristics with insight obtained from exploration of the solution space to improve the design. • Managing the complexity of module structure, such as analyzing and simplifying the structure of a large number of goals. • Interpreting the behavior and the property of the model into the knowledge that supports the decision making. • Capturing and managing newly observed properties or a more detailed complexity that are not incorporated into the modeling at first – the emergent properties. • Automating the steps in the above. The intellectual merits in this dissertation are the expandable computational framework for designing complex systems and managing multiple types of uncertainty– the design evolution loop, and the methods fitting into it. By using satisficing strategy and incorporating machine learning to explore the solution space, heuristics in each of the four stages (formulation, approximation, exploration, and evaluation) can be updated or replaced by knowledge gained from experiments, calculations and analyses. In addition, knowledge on tradeoffs between different categories of design requirement – such as (but not limited to) approximation accuracy, computational complexity, design preference diversity, reformulation flexibility, and the degree of design automation – can be collected, stored and reused

Advances in Methodology and Applications of Decision Support Systems

These Proceedings are composed of a selection of papers of the Workshop on Advances in Methodology and Applications of Decision Support Systems, organized by the System and Decision Sciences (SDS) Program of IIASA and the Japan Institute of Systems Research (JISR). The workshop was held at IIASA on August 20-22, 1990. The Methodology of Decision Analysis (MDA) Project of the SDS Program focuses on a system-analytical approach to decision support and is devoted to developing methodology, software and applications of decision support systems concentrated primarily around interactive systems for data analysis, interpretation and multiobjective decisionmaking, including uncertainty analysis and group decision making situations in both their cooperative and noncooperative aspects. The objectives of the research on decision support systems (DSS) performed in cooperation with the MDA Project are to: compare various approaches to decision support systems; advance theory and methodology of decision support; convert existing theories and methodologies into usable (simple to use, user-friendly and robust) tools that could easily be used in solving real-life problems. A principal characteristic of decision support systems is that they must be tuned to specific decision situations, to complex real-life characteristics of every application. Even if the theory and methodology of decision support is quite advanced, every application might provide impulses for further theoretical and methodological advances. Therefore the principle underlying this project is that theoretical and methodological research should be strongly connected to the implementation and applications of its results to sufficiently complicated, real-life examples. This approach results in obtaining really applicable working tools for decision support. The papers for this Proceedings have been selected according to the above summarized framework of the research activities. Therefore, the papers deal both with theoretical and methodological problems and with real-life applications

Understanding Complexity in Multiobjective Optimization

This report documents the program and outcomes of the Dagstuhl Seminar 15031 Understanding Complexity in Multiobjective Optimization. This seminar carried on the series of four previous Dagstuhl Seminars (04461, 06501, 09041 and 12041) that were focused on Multiobjective Optimization, and strengthening the links between the Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) communities. The purpose of the seminar was to bring together researchers from the two communities to take part in a wide-ranging discussion about the different sources and impacts of complexity in multiobjective optimization. The outcome was a clarified viewpoint of complexity in the various facets of multiobjective optimization, leading to several research initiatives with innovative approaches for coping with complexity

An Algorithm for Integrated Subsystem Embodiment and System Synthesis

Consider the statement,'A system has two coupled subsystems, one of which dominates the design process. Each subsystem consists of discrete and continuous variables, and is solved using sequential analysis and solution.' To address this type of statement in the design of complex systems, three steps are required, namely, the embodiment of the statement in terms of entities on a computer, the mathematical formulation of subsystem models, and the resulting solution and system synthesis. In complex system decomposition, the subsystems are not isolated, self-supporting entities. Information such as constraints, goals, and design variables may be shared between entities. But many times in engineering problems, full communication and cooperation does not exist, information is incomplete, or one subsystem may dominate the design. Additionally, these engineering problems give rise to mathematical models involving nonlinear functions of both discrete and continuous design variables. In this dissertation an algorithm is developed to handle these types of scenarios for the domain-independent integration of subsystem embodiment, coordination, and system synthesis using constructs from Decision-Based Design, Game Theory, and Multidisciplinary Design Optimization. Implementation of the concept in this dissertation involves testing of the hypotheses using example problems and a motivating case study involving the design of a subsonic passenger aircraft

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Intelligent Agents and Their Potential for Future Design and Synthesis Environment

This document contains the proceedings of the Workshop on Intelligent Agents and Their Potential for Future Design and Synthesis Environment, held at NASA Langley Research Center, Hampton, VA, September 16-17, 1998. The workshop was jointly sponsored by the University of Virginia's Center for Advanced Computational Technology and NASA. Workshop attendees came from NASA, industry and universities. The objectives of the workshop were to assess the status of intelligent agents technology and to identify the potential of software agents for use in future design and synthesis environment. The presentations covered the current status of agent technology and several applications of intelligent software agents. Certain materials and products are identified in this publication in order to specify adequately the materials and products that were investigated in the research effort. In no case does such identification imply recommendation or endorsement of products by NASA, nor does it imply that the materials and products are the only ones or the best ones available for this purpose. In many cases equivalent materials and products are available and would probably produce equivalent results

Managing computational complexity through using partitioning, approximation and coordination

Problem: Complex systems are composed of many interdependent subsystems with a level of complexity that exceeds the ability of a single designer. One way to address this problem is to partition the complex design problem into smaller, more manageable design tasks that can be handled by multiple design teams. Partitioning-based design methods are decision support tools that provide mathematical foundations, and computational methods to create such design processes. Managing the interdependency among these subsystems is crucial and a successful design process should meet the requirements of the whole system which needs coordinating the solutions for all the partitions after all. Approach: Partitioning and coordination should be performed to break down the system into subproblems, solve them and put these solutions together to come up with the ultimate system design. These two tasks of partitioning-coordinating are computationally demanding. Most of the proposed approaches are either computationally very expensive or applicable to only a narrow class of problems. These approaches also use exact methods and eliminate the uncertainty. To manage the computational complexity and uncertainty, we approximate each subproblem after partitioning the whole system. In engineering design, one way to approximate the reality is using surrogate models (SM) to replace the functions which are computationally expensive to solve. This task also is added to the proposed computational framework. Also, to automate the whole process, creating a knowledge-based reusable template for each of these three steps is required. Therefore, in this dissertation, we first partition/decompose the complex system, then, we approximate the subproblem of each partition. Afterwards, we apply coordination methods to guide the solutions of the partitions toward the ultimate integrated system design. Validation: The partitioning-approximation-coordination design approach is validated using the validation square approach that consists of theoretical and empirical validation. Empirical validation of the design architecture is carried out using two industry-driven problems namely the a hot rod rolling problem’, ‘a dam network design problem’, ‘a crime prediction problem’ and ‘a green supply chain design problem’. Specific sub-problems are formulated within these problem domains to address various research questions identified in this dissertation. Contributions: The contributions from the dissertation are categorized into new knowledge in five research domains: • Creating an approach to building an ensemble of surrogate models when the data is limited – when the data is limited, replacing computationally expensive simulations with accurate, low-dimensional, and rapid surrogates is very important but non-trivial. Therefore, a cross-validation-based ensemble modeling approach is proposed. • Using temporal and spatial analysis to manage the uncertainties - when the data is time-based (for example, in meteorological data analysis) and when we are dealing with geographical data (for example, in geographical information systems data analysis), instead of feature-based data analysis time series analysis and spatial statistics are required, respectively. Therefore, when the simulations are for time and space-based data, surrogate models need to be time and space-based. In surrogate modeling, there is a gap in time and space-based models which we address in this dissertation. We created, applied and evaluated the effectiveness of these models for a dam network planning and a crime prediction problem. • Removing assumptions regarding the demand distributions in green supply chain networks – in the existent literature for supply chain network design, there are always assumptions about the distribution of the demand. We remove this assumption in the partition-approximate-compose of the green supply chain design problem. • Creating new knowledge by proposing a coordination approach for a partitioned and approximated network design. A green supply chain under online (pull economy) and in-person (push economy) shopping channels is designed to demonstrate the utility of the proposed approach