Effective prompting with ChatGPT for problem formulation in engineering optimization

Optimization problem formulation, a crucial but manually performed process, can present an obstacle to applying optimization in engineering since many practitioners find it challenging. This article explores the use of ChatGPT to address this challenge. It evaluates the efficacy of self-designed prompts with different ChatGPT models. Using analysis of variance and Tukey's test, assessments are conducted to determine the influence of variations in wording on the quality of solutions. The sequential learning approach is also tested to assess its impact on ChatGPT responses. This article confirms the importance of specificity in word choice and the relevance of domain-specific engineering terminology in crafting prompts for problem modelling. The analysis shows that a combination of properly selected words can lead to high-quality optimization problem formulations. Furthermore, it is found that sequential learning can enhance formulations. This work may bring more attention to the use of ChatGPT for formulating problems in engineering optimization.