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Developing optimization models with cognitive systems engineering
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DOI：10.1080/01605682.2025.2499716.png)
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Abstract
En 中文
One goal of applied operations research is to improve decisions in practice. For an optimization model to be useful, the underlying problem must be understood, the formulation representative, the data appropriate, and the model’s recommendations understood by the decision-makers. Current approaches for developing optimization models primarily rely on past experience, rather than underlying theory, to guide decisions on how to include stakeholders in the process. In this paper, we present a new framework for applied optimization modeling with stakeholders. In each iteration, modelers develop model versions based on their current mental models of the decision process; these mental models are progressively updated based on interviews with stakeholders and thematic analyses. Stakeholders’ mental models may also update as the model versions and the context change. The theoretical foundation is based on methodologies from the field of Cognitive Systems Engineering that focus on understanding emergent patterns based on systems, stakeholders, and environments. We apply the framework to a case study of locating hand sanitizer stations in response to COVID-19 at a large academic institution. Door access control data is used to represent demand. The results illustrate how the recommended decisions from the model change as the modelers’ understanding of the problem changes.
Keywords:
Optimization
cognitive systems
resilience
applied operations research
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2.7
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456
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