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Real-world decisions often involve conflicting objectives, like boosting sales while avoiding overstock. In this episode, we explore how multi-objective optimization (using NSGA-II with the pymoo library) can model and solve such dilemmas. You'll learn:

* Why traditional dispatch strategies fall short

* How Pareto fronts reveal optimal trade-offs

* How to apply ASF and Pseudo-Weights to guide final decisions

If you’d rather read than listen, the full article (with code, charts, and detailed examples) is available on Substack :

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PODCAST — Balancing Conflicting Goals: A Practical Guide to Multi-Objective Optimization with Pymoo