Gideon Avigad

In this paper, a computational tool to support designers in choosing an engineering concept is proposed. It takes into account aspects of supply chains (with a focus on suppliers’ related uncertainties) already at the conceptual design stage. It is suggested that a decision on an engineering concept would take into account its robustness to such uncertainties. The introduction and examination of such robustness considerations within the conceptual design stage are undertaken by finding a set of variants for each concept under consideration. The robustness of a concept is gained by allowing the selected concept-related solution to the problem to be later changed to another concept-related variant in response to changes associated with supply chains. In the paper, we propose a procedure that allows a search for such variants that may serve as solutions that promote robustness to suppliers’ related uncertainties. Utilising the suggested procedure may support designers in making decisions when considering these uncertainties. We argue that considering elements of supply chains already during the conceptual design space may affect the choice of a concept. An engineering problem is utilised in order to demonstrate the methodology and its applicability to real-life engineering problems.

This paper introduces a computational approach to support concept selection in multi-objective design. It is motivated by: (1) a common need to delay some decisions during conceptual design due to the presence of uncertainties; and (2) intentional delay of decisions for the purpose of maintaining several optional concepts, as suggested by the concurrent engineering procedure of Toyota. Here, for the first time, a multi-objective set-based concept (SBC) selection problem with delayed decisions is formulated and solved. SBCs are conceptual solutions, which are represented by sets of particular solutions, with each concept having a one-to-many relation with the objective space. Several novel notions, such as higher-level concepts, multi-model concepts and robust concepts to delayed decisions, are defined and used. These lead to an auxiliary multi-objective decision problem. The auxiliary objectives are concept optimality and variability, both paramount to concept selection, with concept variability strongly supporting the idea of intentionally keeping several useful alternatives as long as possible. Academic and engineering examples are provided to demonstrate the proposed approach and its applicability to real-life problems. The results demonstrate that the suggested technique may well support the process of delayed decision either when needed or when deliberately done.

This paper presents a novel approach to support the selection of conceptual solutions to multi-objective problems. The proposed method involves a comparison between concepts, based on the performances of sets of solutions that represent them. The set-based comparison of concepts is consistent with the so-called Toyota set-based concurrent engineering process. Such an approach discourages early exploitation of solutions and promotes extended exploration of the design space by means of sets of solutions. Both optimality and variability of concepts are considered, and their measures are devised to pose the selection problem as an auxiliary multi-objective problem. The auxiliary objectives are to maximise optimality and to maximise the variability. This highlights the inherent multi-objectivity of concept selection and supports decision-making under the possible contradictory nature of optimality and variability of concepts. Both academic and engineering problems are used to demonstrate the approach and to expose the inherent subjectivity of the measures, which are dependent on the selection of a window of interest by the decision-makers.

Concept-based multi-objective path planning, involves search and optimization of conceptual path plans by way of their particular solutions. The method, which has been recently introduced by the authors in the context of interactive path planning, is considered here using the epsiv-Pareto notion. Using this approach the decision maker may explore the decision space for concepts that have performances within a user-defined distance from the Pareto-front. In particular the paper discusses applications such as interactive path planning and re-planning. A simple "safe vs. fast" example is employed to demonstrate the proposed approach. Finally, a short discussion is given to relate the ideas presented here with A-life.