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Flexible Travel Optimization

Note: This project is still in progress

Seli-supervised clustering system for determining the best allocation strategy for a traveling visitation system. This system is based on the k-means clustering system with conditions for the constraints of this problem detailed below:

Fixed Dynamics

"child"s are fixed agents

"EI"s are traveling agents

"child"s are assigned to one "EI" each

"EI"s must travel to visit all "child"s in their roster

Constraints

Each "EI" has a maximum capacity of their roster

All "EI"s must take on "child"s to be visited

We have no control over the "EI"s schedules, only that which is allocated to them

Adjust existing system to an optimized system moving forward with no deallocation to pivot to optimized system

Goals

Lower average traveling time for "EI"s when visiting all of their children in roster

Lower average traveling distance for "EI"s when visiting all of their children in roster

Do not disrupt existing systems in the name of optimization

System used as suggestions to make data-informed decisions as opposed to data-driven decisions, because human factors need to be accounted for

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Semi-Supervised clustering (k-means inspired) experimentation system for flexible travel optimization

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