Introduction
Ciara is looking to staff her new startup company which develops and provides AI-based logistics software for retailers. She needs specific roles filled to have the right expertise to build and deliver her product. This includes 2 Python programmers, 2 AI engineers, 1 web designer, 1 database administrator, and 1 systems engineer. With a pool of 7 candidates with different skills, finding the optimal assignment of people to roles is a constraint satisfaction problem (CSP) that can be modeled and solved computationally. In CSPs, the goal is to assign values to variables while satisfying certain constraints on which combinations of values are valid or desirable (Russell & Norvig, 2021).
This aligns well with assigning candidates to roles based on their skills and additional constraints like budget. This report will demonstrate CSP solutions to two staffing scenarios Ciara faces using the OR-Tools constraint programming solver. It will analyze how constraint satisfaction differs from standard algorithms and how other techniques could also solve these problems. Interactive visualizations will also be presented to communicate and explore the solutions.
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