Run main.py script - it will trigger optimization in following stages

Objective - Minimize difference between demand and supply with the existing resources.

Variables -

1. Tps (Time per practitioner and state), NonNegativeInteger
2. Lps (new license assignment per practitioner and state) - Binary

Constraints -

1. Therapist constraint - (p)∑Tps <= availability_per_therapist
2. State constraint - (s)∑Tps <= demand_per_state
3. Licensing constraint - Lps - {0, 1} if p didn’t have license in s before and p’s license_time < planning_horizon

Definitions

1. p- {set of all available therapists, P}
2. s- {set of all states with practice, S}

Objective - Minimize the supply deficit with the new resources.

Variables -

1. Tsh (Time per new hire and state),
2. Lsh (license assignment per new hire and state)

Constraints -

1. New hire constraint - (h)∑Tsh <= maximum_newhire_hrs
2. State constraint - (s)∑Tsh <= supply_deficit_per_state
3. Licensing constraint - Lps - {0, 1} if h’s license_time < planning_horizon

Definitions -

1. h- {set id of all new potential hires, H}
2. s- {set of all states with practice, S}