Methods for incorporating imprecision in engineering design decision-making are briefly reviewed and compared. A tutorial is presented on the Method of Imprecision (MoI), a formal method, based on the mathematics of fuzzy sets, for representing and manipulating imprecision in engineering design. The results of a design cost estimation example, utilizing a new informal cost specification, are presented. The MoI can provide formal information upon which to base decisions during preliminary engineering design and can facilitate set-based concurrent design.

Imprecision is uncertainty that arises because of vague or incomplete information. Preliminary design information is characteristically imprecise: specifications and requirements are subject to change, and the design description is vague and incomplete. Yet many powerful evaluation tools, including finite element models, expect precisely specified data. Thus it is common for engineers to evaluate promising designs one by one. Alternatively, optimization may be used to search for the single "best" design. These approaches focus on individual, precisely specified points in the design space and provide limited information about the full range of acceptable designs. An alternative approach would be to evaluate sets of designs. The method of imprecision uses the mathematics of fuzzy sets in order to represent imprecision as preferences among designs: . Functional requirements model the customer's direct preference on performance variables based on performance considerations: the quantifi...

s (english, french and german) . . . . . . . . . . . . . . . . . . . . . . 10 1 Introduction 13 1.1 Outline, Evaluation and Design . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.2 Computer-Aided Design (CAD) and Artificial Intelligence (AI) . . . . . . . . . 17 2 State of Knowledge 21 2.1 Decision Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.1.1 Utility Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.1.2 Multiple Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.1.3 Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.1.4 Metrification Postulates . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.2 Model Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.2.1 Multiple Attribute Decision Making (MADM) . . . . . . . . . . . . . . . 29 2.2.2 Multiple Objective Decision Making (MODM) . . . . . . . . . . . . . . 30 2.2...

Partner selection continues to be one of the most important activity in industries. Selecting manufacturing partners in the new emerging world of agile manufacturing is an endeavor that is beset with the complexity and dynamics of the market driven by customer needs as well as the inherent subjectivity of the selection process. Traditional vendor selection methodologies do not lend themselves as a ready solution to these needs of agile environment. This paper develops a flexible optimization framework for optimal manufacturing partner selection in such an environment. 1. INTRODUCTION In face of fierce global competition and the brief moments of opportunity, manufacturing factories have become more focused, increasing the dependence on vendors in product realization. A multi-partnership manufacturing environment has thus emerged in which partner selection has become critical and is governed by market specific needs. `Agile Manufacturing' is a new concept that subsumes characteristics ...

Abstract—The centrality of the decision maker (DM) is widely recognized in the multiple criteria decision-making community. This translates into emphasis on seamless human–computer interaction, and adaptation of the solution technique to the knowledge which is progressively acquired from the DM. This paper adopts the methodology of reactive search optimization (RSO) for evolutionary interactive multiobjective optimization. RSO follows to the paradigm of “learning while optimizing,” through the use of online machine learning techniques as an integral part of a self-tuning optimization scheme. User judgments of couples of solutions are used to build robust incremental models of the user utility function, with the objective to reduce the cognitive burden required from the DM to identify a satisficing solution. The technique of support vector ranking is used together with a k-fold cross-validation procedure to select the best kernel for the problem at hand, during the utility function training procedure. Experimental results are presented for a series of benchmark problems. Index Terms—Interactive decision making, machine learning, reactive search optimization, support vector ranking. I.

Within a global factory network, Agile Manufacturing aims at setting up flexible and dynamic partnership among manufacturers, who by working together improve their posture and effectively penetrate specific niches in the global market. Several partners as well as several processes within their plants may be capable of accomplishing constituent responsibilities of the overall business initiative. We consider the problem of selecting the sequence of process-partner pairs that collectively optimizes multiple selection criteria. The emphases on these, usually qualitative criteria, are subjective and based on customer-driven requirements. And, the rating of an alternative with respect to the criteria may not be contained in crisp quantitative terms. The paper presents a linguistic decision making procedure that encompasses the complexity and inherent subjectivity of the selection process. Fuzzy set theory is employed to deal with the imprecision measurement of decision makers' sub...

Selecting manufacturing partners in the new emerging world of agile manufacturing is an endeavor that is beset with the complexity and dynamics of the market, driven by customer needs, as well as the inherent subjectivity of the selection process. This problem consists of determining an optimal group of manufacturing companies that collectively respond to a specific business initiative as a virtual enterprise. Considering these requirements, traditional vendor selection methodologies do not lend themselves readily applicable to the agile environment. This paper develops a flexible optimization framework for optimal manufacturing partner selection in such an environment. The framework: (1) employs a Fuzzy-AHP approach to compute attribute preferences, (2) acquires linguistically, needs of the specific business initiative, and systematically modifies attribute preferences, (3) performs a search among alternative manufacturing companies to identify and rank a small number of eff...

This paper presents N-Site, a distributed consensus building and negotiation support system, which is used to provide geographically dispersed teams with agile access to a Web-based group decision support system. Four teams located in France, Mexico, the Ukraine, and the United States participated in the N-Site project. Each team was required to research the problem using the World Wide Web (WWW). With this background, each team identified opportunities, threats and alternatives as a basis for developing a response to the Cuban Missile Crisis that confronted President Kennedy in October 1962. The strategic assessment model (SAM) (M. Tavana, J. Multi-Criteria Decision Anal. 11 (2002) 75–96; M. Tavana and S. Banerjee, Decision Sci. 26 (1995) 119–143.) was used by each team to choose a strategy that best fit the team’s perspective. SAM and WWW enabled the teams to evaluate strategic alternatives and build consensus based on a series of intuitive and analytical methods including environmental scanning, the analytic hierarchy process (AHP) and subjective probabilities. The WWW was used to achieve interaction among the international teams as they attempted to negotiate a decision framework and select a diplomatic response. The project was assessed with a Web-distributed survey instrument. This use of the WWW has implications for international diplomacy as well as global business.

An evidential reasoning (ER) approach is applied to evaluate pre-qualification criteria in selection of a main contractor. This approach has proved to be useful and practical when a decision problem under consideration includes multiple criteria, which are of both a quantitative and qualitative nature. In this type of problem, a major difficulty is how to aggregate numerical data and subjective assessments in order to evaluate potential alternative contractors. A contractor pre-qualification problem (CPP) is investigated to show how the ER approach can overcome this difficulty. The decision making process is fully explained using the ER approach together with discussions about the advantages and disadvantages of the model presented.

reasoning approach