Cost-Efficient Fuel Fleet Operations

Product Used: Route Optimization API

Background

Different trucks can have different cost structures depending on factors such as vehicle size, fuel efficiency, maintenance requirements, and contractual driver arrangements. Consider a fuel distributor operating a fleet with several types of tanker trucks where each vehicle incurs costs in different ways – some have high fuel consumption, others incur higher driver wages, and some trucks have fixed deployment costs per route. Ignoring vehicle-specific costs while optimizing can lead to routes that are efficient in time/distance but fail to minimize actual operational costs.

Problem Statements

When dealing with vehicles having different cost structures, the dispatchers face problems with optimum utilization of the fleet due to following reasons:

Handle multiple cost structures

Deploying a large tanker truck consuming significantly more fuel per kilometer than a smaller vehicle, for a long-distance route, might be inefficient. Similarly, drivers with higher wages and overtime payments terms could be inefficient for longer hours of operation. Factoring order-handling and compliance costs on top of such costly fleet operations can cause these inefficiencies to drastically raise overall delivery expenses. In order to efficiently manage the overall operational cost of the fleet, the routing workflow should be able to account for all types of costs involved for fulfilling all deliveries and mitigate them through intelligent use of available vehicles.

Deploy vehicles economically

Optimizing vehicle deployment in dynamic fuel delivery operations, given each vehicle's cost structure, is often slow and can lead to unnoticed inefficiencies. To run cost-efficient operations, routing decisions should account for the true cost of using each vehicle, including distance, time, order handling, and fixed deployment costs. The preferred solution is a system that swiftly identifies opportunities for a favorable Return on Investment (ROI) by strategically using specific vehicles to handle the demand.

Solution

NextBillion.ai's Route Optimization API that supports vehicle cost parameters allows dispatchers to generate routing plans that minimize actual operational costs, rather than simply minimizing travel distance or time.

Define operating costs for each vehicle

The API allows each vehicle to be assigned a per-kilometer, per-hour, per-order and a fixed cost representing different types of expense incurred by every vehicle. Any combination of these costs can be used for a given vehicle. Specifying all these costs enables the optimizer to allocate routes in a way that reduces the overall operating expenses of the fleet. Following is a sample cost configuration for a vehicle:

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"costs": {
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               "fixed": 2000,
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               "per_hour": 500,
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               "per_order": 200
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          }

Optimize utilization of vehicles

During route optimization various vehicle-specific costs are considered to determine delivery assignments that minimize total service expenditure. Vehicles with low Return on Investment (ROI) are either not preferred or strategically assigned to tasks that mitigate their inherent cost structure, only when more cost-effective options are unavailable.

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": "Cost Efficient Fuel Fleet Operations",
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  "result": '{...}',
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  "status": "Ok",
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  "message": ""

In the optimized solution, we notice the optimizer chose to ignore the truck - Fuel Truck 3 - with a high fixed cost and instead chose to use those with lower costs of operations, while assigning only 1 delivery to the truck with highest per-order cost - Fuel Truck 2. We can see how the optimizer evaluates the ROI for each vehicle when using them to generate routing plans to balance fleet utilization with operational cost efficiency.