Scheduled Tanker Refills & Fuel Deliveries

Product Used: Route Optimization API

Background

Consider a fuel distributor serving multiple gas stations across a city. Each morning, several tanker trucks depart from the main fuel depot carrying diesel and gasoline. Throughout the day, they must complete deliveries to different stations and occasionally return to the depot to refill their tanks before continuing with the remaining deliveries. However, both depots and gas stations operate under strict time constraints. On one hand the fuel depot operates only during specific hours and can refill fuel into trucks at a limited throughput rate. On the other hand, the gas stations have their own delivery acceptance windows.

Problem Statements

In order to plan routes for fleet trucks such that depots' operating hours, refuelling throughput rates are honored while simultaneously honoring the gas station's delivery time windows, fuel dispatchers need to overcome following challenges:

Depot operating hours and throughput limits

When fuel depots restrict truck access to refilling facilities based on defined time windows and have throughput constraints which limit refilling speed due to equipment or safety limits, dispatchers must ensure the routes are carefully planned so that vehicles arrive at the depot within the operating hours without exceeding its loading capacity at a given time, to avoid bottlenecks and delays.

Delivery schedules at gas stations

Due to operational, safety, or staffing constraints most gas stations only accept fuel deliveries during specific time intervals. If a tanker arrives outside these intervals, the station may refuse the delivery. Hence, dispatchers must ensure that each delivery occurs within the station's allowed time window to avoid long waiting times and costly delivery re-assignment/rescheduling process.

Solution

NextBillion.ai's Route Optimization API supports depot timelines with throughput constraints and delivery time windows, allowing dispatchers to generate feasible and efficient routes for scheduled fuel delivery operations.

Define depot timelines and throughput limits

The API allows configuring depot's operational hours using depots.time_windows and assigning a throughput rate using depots.throughput.handling_time and depots.throughput.max_load attributes. The API also provides functionality to define penalties for unavoidable violations (emergencies/commitments) of throughput constraints. This empowers dispatchers to control the optimizer's preferences, allowing them to modify the balance between adhering to throughput limits and achieving a higher volume of deliveries during special circumstances.

Following is a sample depot time window and throughput configuration:

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"depots": [
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  {
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    "id": "Fuel Depot 1",
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    "location_index": 0,
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    "time_windows": [[1774429200, 1774436400]],
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    "throughput": {
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      "handling_duration": 1200,
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      "max_vehicles": 2,
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      "vehicle_penalty": 1000,
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      "max_load": [8000],
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      "load_penalty": [100]
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    }
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  }
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]

Configure delivery time windows for gas stations

The API allows each delivery location to be assigned a delivery time window, representing the time interval during which the station can receive fuel deliveries.

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{
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  "id": "Delivery-Gas Station 1",
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  "time_windows": [[1774436400,1774438200]]
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}

These time windows act as hard constraints during route optimization so that trucks either reach the station within its permitted delivery interval while maintaining efficient travel routes.

Sample Request and Response

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

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": "Scheduled Tanker Refills & Fuel Deliveries",
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  "result": '{...}',
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  "status": "Ok",
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  "message": ""

Observing the optimised route plan, we see that the optimizer was able to plan the trucks' arrival at the depot for refilling within the operating hours while simultaneously accounting for the time required to refill the tankers as per depot's throughput capacity. It also ensured the trucks reach gas stations within expected time windows. Also, the absence of applied penalties confirms the optimizer's success in preventing unrealistic situations where too many trucks arrive at the depot simultaneously for refilling or where trucks arrive when either the depots or the gas stations are closed.