Decision-Making Models in Systems Engineering

In systems engineering, where resources are limited, effective decision-making is paramount. Decision-making involves selecting the best option from a set of alternatives to achieve a specific goal. It is a process rife with information and varying degrees of uncertainty. This chapter explores decision-making models in systems engineering, covering both risk and uncertainty scenarios. Under uncertainty, criteria like Laplace, maximax, maximin, Hurwicz, and minimax regret are discussed. In the context of risk-based decisions, the expected value criterion is introduced. The expected value model does not account for diverse risk attitudes among decision-makers. Von Neumann and Morgenstern's utility theory addresses this, allowing for the incorporation of risk attitudes into decisions. For multi-criteria decision problems, decision trees are used to navigate a sequence of decisions, enabling optimal decision paths. The expected value of sample information (EVSI) and the expected value of perfect information (EVPI) values help assess feasibility when prior knowledge is available. In complex decision scenarios, the Analytical Hierarchical Process (AHP) aids in ranking criteria and alternatives. AHP employs pairwise comparisons and a four-step algorithm, ensuring consistency. An Excel template is provided for efficient AHP implementation.