Optimize or satisfice in engineering design?

In this paper, we address the issue of whether to optimize or satisfice in model-based engineering design. When dealing with operations research problems in the context of engineering design, one may encounter (i) nonlinear, nonconvex objectives and constraints, (ii) objectives with different units, and (iii) computational models that are abstractions of reality and fidelity, Seeking a single-point optimal solution that meets the necessary and sufficient Karush–Kuhn–Tucker (KKT) conditions makes it impossible to obtain a solution that satisfies all the targeted goals. Instead, a method to identify satisficing solutions that satisfies necessary KKT condition but not the sufficient condition is proposed. These solutions are relatively robust, easy to acquire, and often good enough. In this paper, we demonstrate the combined use of the compromise Decision Support Problems and the adaptive linear programming algorithm, as proposed by Mistree and co-authors. This method is appropriate in formulating design problems and obtaining solutions that satisfy only the necessary KKT condition. Further, the use of the proposed method circumvents complications associated with the use of gradient-based optimization algorithms typically used to solve optimization problems. We discuss the efficacy of our proposed method using four test problems to illustrate how the satisficing strategy outperforms the optimizing strategy in model-based engineering design.