Evolutionary multi-objective decision support systems for conceptual design

Merged with duplicate record 10026.1/2328 on 07.20.2017 by CS (TIS)In this thesis the problem of conceptual engineering design and the possible use of adaptive search techniques and other machine based methods therein are explored. For the multi-objective optimisation (MOO) within conceptual design problem, genetic algorithms (GA) adapted to MOO are used and various techniques explored: weighted sums, lexicographic order, Pareto method with and without ranking, VEGA-like approaches etc. Large number of runs are performed for findingZ Dth e optimal configuration and setting of the GA parameters. A novel method, weighted Pareto method is introduced and applied to a real-world optimisation problem. Decision support methods within conceptual engineering design framework are discussed and a new preference method developed. The preference method for translating vague qualitative categories (such as "more important 91 , 4m.9u ch less important' 'etc. ) into quantitative values (numbers) is based on fuzzy preferences and graph theory methods. Several applications of preferences are presented and discussed: * in weighted sum based optimisation methods; s in weighted Pareto method; * for ordering and manipulating constraints and scenarios; e for a co-evolutionary, distributive GA-based MOO method; The issue of complexity and sensitivity is addressed as well as potential generalisations of presented preference methods. Interactive dynamical constraints in the form of design scenarios are introduced. These are based on a propositional logic and a fairly rich mathematical language. They can be added, deleted and modified on-line during the design session without need for recompiling the code. The use of machine-based agents in conceptual design process is investigated. They are classified into several different categories (e. g. interface agents, search agents, information agents). Several different categories of agents performing various specialised task are developed (mostly dealing with preferences, but also some filtering ones). They are integrated with the conceptual engineering design system to form a closed loop system that includes both computer and designer. All thesed ifferent aspectso f conceptuale ngineeringd esigna re applied within Plymouth Engineering Design Centre / British Aerospace conceptual airframe design project.British Aerospace Systems, Warto

Robust ordinal regression in preference learning and ranking

Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking

Product Design Selection with Variability for an Implicit Value Function

Often in engineering design selection there is no one design alternative that is better in terms of all attributes, and the preferred design(s) is dependent on the preferences of the Decision Maker (DM). In addition, there is always uncontrollable variability, which is mainly of two types, that has to be accounted for. The first type, preference variability, is caused due to the DM's lack of information on end users' needs. The second type, attribute variability, is caused due to uncontrollable engineering design parameters like manufacturing errors. If variability is not accounted for, the preferred design(s) found might be erroneous. Existing methods presume an explicit form for the DM's "value function" to simplify this selection problem. But, such an assumption is restrictive and valid only in some special cases. The objective of this research is to develop a decision making framework for product design selection that does not presume any explicit form for the DM's value function and that accounts for both preference and attribute variability. Our decision making framework has four research components. In the first component, Deterministic Selection, we develop a method for finding the preferred design(s) when the DM gives crisp preference estimates, i.e., best guess of actual preferences. In the second component, Sensitivity Analysis, we develop a method for finding the allowed variation in the preference estimates for which the preferred design(s) do not change. In the third component, Selection with Preference Variability, we develop a method for finding the preferred design(s) when the DM gives a range of preferences instead of crisp estimates. Finally, in our fourth component, Selection with Preference and Attribute Variability, we develop a method in which the DM gives a range of values for attributes of the design alternatives in addition to a range for preferences. We demonstrate the methods developed in each component with two engineering examples and provide numerical experimental results for verification. Our experiments indicate that the preferred design(s) found in our first, third, and fourth components always include the actual preferred design(s) and that our second component finds the allowed variation in preference estimates efficiently

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers