Identify Optimal Waste Collection Points

Product Used: Clustering API

Background

A waste collection company operates in a densely populated city with numerous small businesses, residential buildings, and public waste bins that need regular collection. Waste generation and collection points are concentrated in certain neighborhoods, leading to inefficient routing when multiple trucks are dispatched to overlapping areas. Without clustering waste points, each truck ends up making many small stops close to each other, leading to frequent stops, delays, and increased fuel consumption.

Problem Statements

The waste collection company struggles with:

1. Inefficient Use of Fleet Resources: With many stops in close proximity, trucks make frequent stops, which increases travel time, fuel consumption, and emissions.
2. Route Overlaps: Trucks often cover overlapping areas due to the lack of clustered stops, resulting in unnecessary routes and duplicated collections.
3. Increased Operational Costs: The lack of efficient clustering leads to higher costs, more driver hours, and extra maintenance due to wear on trucks from repeated stopping and starting.

Solution

NextBillion.ai Clustering API helps the waste management company to identify optimal collection points based on a desired criteria. By clustering collection points within close proximity, the company can reduce the number of stops each truck makes and minimize overlap, resulting in more efficient routes

Organizing Stops by Proximity

The clustering service can group the pick-up points that are near each other and assign common collection points. These collection points can then be used to plan routes, reducing the total number of routes, minimizing overlap and ensuring each truck services a well-defined, compact area. Define the maximum radius of the cluster containing multiple waste pickup points:

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"constraint": {
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"max_cluster_radius": 1000
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}

Prevent vehicle overload from collected waste

By specifying the max_cluster_quantity we can limit the amount of load that is collected at each point such that it doesn’t overload / overflow the truck assigned to pick it up. Once specified, the clustering service will intelligently group tasks while honoring the load limits for each cluster. To specify the quantity limits use:

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"constraint": {
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"max_cluster_quantity": 5
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}

Sample API request and response

Here is a complete JSON request that we built for this case and the corresponding response.

API Request

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

API Response

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{
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     "id": "734d264514f82ee2fa2dc9a9504ded0b",
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    "status": "Ok",
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    "description": "Identify Optimal Waste Collection Points",
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    "result": '{...}'
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}

We can see from the response that each cluster groups the closely located waste pick-up points and assigns an optimal collection point, center_location_index, while ensuring the load doesn’t exceed the prescribed limits. Clustering waste collection points allows the waste management company to optimize collection points in densely populated areas. It leads to efficient routing which minimizes fuel costs, emissions, and route overlaps ensuring streamlined, efficient operations, reduced costs, and improved fleet utilization.