Figure 1
Conceptual overview of Goal Linear Programming: PyGuLP distinguishes between hard feasibility constraints, which define the admissible solution space, and aspirational goals, which represent target outcomes that may be achieved with allowable deviations. The package automatically constructs under- and over-deviation variables and applies weighted penalties to deviations from competing targets, enabling transparent multi-objective optimization and reproducible decision support within the Python ecosystem.