Can’t calculatecomute with multiple stops correctly

Title: Common Challenges in Optimizing Multi-Stop Commutes: Why AI Sometimes Misses the Mark

In today’s fast-paced world, optimizing daily commutes is a common concern for many, especially parents balancing school runs and work commitments. Recently, I encountered an interesting scenario highlighting the difficulties even advanced AI models face when solving multi-stop route optimization problems.

The Scenario:
Suppose you need to find the best place to live to minimize total travel time when dropping your daughter off at school (Point A) before heading to work (Point B). Intuitively, the optimal decision is to live as close as possible to the first stop, reducing unnecessary backtracking. This approach ensures the shortest initial commute, subsequently minimizing overall travel time.

The AI’s Response:
However, when I used large language models, such as GPT, the solution they proposed was counterintuitive—they suggested living halfway between both locations, believing this would optimize the route. Even trying alternative AI tools yielded the same answer.

Why the Discrepancy?
This highlights a common challenge in route optimization: traditional algorithms and human intuition recognize the importance of proximity to the first destination. Yet, AI models sometimes misinterpret the problem, potentially due to their attempts to analyze the entire route holistically rather than sequentially. They may lack the nuanced understanding that for multi-stop trips, prioritizing closeness to the first destination usually results in minimal total commute time.

Implications for Users:
This situation underscores the importance of understanding the limits of AI in logistical planning. While these models are powerful, they may not always align with straightforward practical reasoning, especially in complex, real-world scenarios. For tasks requiring precise optimization—such as route planning—it’s advisable to combine AI insights with established logistical principles or specialized algorithms.

Conclusion:
As AI continues to evolve, users should remain critical of its outputs, particularly in specialized domains like route optimization. Recognizing where AI may fall short enables us to better leverage its strengths while applying our own judgment for critical decisions. Ultimately, in scenarios like daily commutes, simple logical strategies—such as living near the first stop—often prove most effective.

Feel free to share your experiences or questions about AI and route optimization in the comments below!