Adhering to Strict Appointment Schedules

Product Used: Route Optimization API

Background

A regional NEMT provider in New York City is tasked with transporting a group of dialysis patients to various treatment centers. The patients’ appointments are scheduled at different times, and traffic congestion during peak hours will make it difficult to adhere to the strict time windows. Late arrivals would mean delayed treatments, which might put patients' health at risk or could lead to penalties from healthcare providers for the transportation company. Also, these patients are often unable to wait long or endure delayed trips due to their health conditions, so timely arrivals are also a top priority.

Coordinating trips for multiple patients with overlapping appointment times, while avoiding traffic congestion, is a daily logistical challenge for the NEMT dispatch team.

Problem Statements

The NEMT provider wants to

Handle multiple patients with overlapping time windows, while adhering to appointment times at the treatment centers
Navigate traffic delays
Minimize travel time to make the trips as short as possible for patients

Solution

NextBillion.ai’s Route Optimization API’s dynamic routing capabilities can be leveraged to manage overlapping time windows and traffic aware routing to comply with appointment schedules without sacrificing the route efficiency. The optimizer takes into account the various schedules, typical traffic conditions to create a route plan which seamlessly balances task fulfillment with streamlined operations.

Manage appoint times and overlapping time windows

We add a time_windows property for each patient’s pick-up and drop-off as per their scheduled appointments. The optimizer will ensure that the patients are picked up well in time so that they reach the individual treatment center on time.

As an example, drop-off time for an appointment at 2PM on 25th August 2024 can be set to happen between 1:45 - 1:55 PM which can be configured as ”time_windows” : [[1724600700, 1724601300]].

In case of multiple overlapping time windows we just add the time windows as-is. The optimizer will either utilize multiple available vehicles to pick-up and drop-off patients in parallel. Else, it may use the same vehicle to perform pick-ups and drop-offs one after the other if the distances between them and available time slots allow it, to optimize operational costs. In general, the optimizer will prioritize fulfilling the tasks over minimizing operational costs.

Navigate traffic delays

Use the traffic_timestamp property to determine the typical traffic conditions that the drivers would face during trips. The optimizer would use the given traffic congestion information to identify actual ETAs.

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"options": {  
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       "routing": {  
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           "traffic_timestamp": 1727557200  
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           }  
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       }

Minimize travel times

In order to minimize the travel times we need to minimize the total duration that the vans drive for. This can be ensured by setting the duration to be considered as the travel_cost of the solution.

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"options": {  
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       "objective": {  
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           "travel_cost": "duration"  
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           }  
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       }

Sample request and solution

Here is a full sample JSON request with multiple patient pickups overlapping with one another and a total of 3 vehicles to serve them all.

API Request

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curl --location 'https://api.nextbillion.io/optimization/v2?key=<your_api_key>' --header 'Content-Type: application/json' --data '{...}'

API Response

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{
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    "description": "Adhering to strict timelines",  
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    "result": "{...}",
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    "status": "Ok",  
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    "message": ""  
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}

From the solution, we can observe that all the patients were picked-up timely and dropped-off at the treatment center within the stipulated time windows. The total duration of all the individual trips is also optimized by taking the best possible routes to avoid any traffic delays.