Decision-Based Collaborative Optimization

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 [1,2] 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 this 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.

Target Exploration for Disconnected Feasible Regions in Enterprise-Driven Multilevel Product Design

“…When γ = |φ/α|, the radius is identical to the original radius , and when γ = 1, the radius becomes in Eq. (6). As shown in Fig.…”

“…When the next target for the engineering problem (T * + ) is obtained by solving the modified enterprise problem, the gradient of the utility function, denoted as φ, is also obtained. If the engineering problem returns the same response (T * − ) and |φ| ≤ |α|, then the constraint radius may be increased up to with the gradient ratio = |α/φ| (6) An updated target (T * + γ ) is assigned to the engineering problem and the engineering problem returns R as response, where γ is the step size for updating the new radius of the constraint that takes a value |φ/α| < γ ≤ 1. When γ = |φ/α|, the radius is identical to the original radius , and when γ = 1, the radius becomes in Eq.…”

“…(6)] from the modified enterprise-level problem guides the engineering-level problem to find the optimal response R sys2 in the other feasible domain. The corresponding utility value for R sys2 is better, the lower level response is accepted as a solution, and the algorithm is terminated.…”

Multidisciplinary Design Optimization: A Survey of Architectures

“…McAllister et al [151] present a multiobjective approach using linear physical programming. Available robust design formulations incorporate the decision-based models of Gu et al [152] and McAllister and Simpson [28], the implicit uncertainty propagation method of Gu et al [153], and the fuzzy computing models of Huang et al [154]. Zadeh and Toropov [118] integrated multiple model fidelities into CO for an aircraft design problem.…”

Target Feasibility Achievement in Enterprise-Driven Hierarchical Multidisciplinary Design

“…There has been a number of efforts to integrate customer preference information in engineering design, e.g., Donndelinger and Cook, 1 Besharati, et al, 2,3 Li and Azarm, 4 Wassenaar and Chen, 5 Cooper, et al, 6 Gu, et al, 7 Michalek, et al 8 They have demonstrated that a relationship exists between marketing and engineering in the enterprise. In this article the enterprise is defined as the organization that designs and produces an artifact to maximize its utility (e.g., profit).…”