Mixed Fleet VS Homogeneous Fleet Route Optimization

Small, Medium, and Enterprise delivery, distribution, and service businesses can use the same or different vehicles and vehicle types that together can comprise a Homogeneous or Mixed Fleet. Whether a company chooses to use a Homogeneous or Mixed vehicle fleet can depend on multiple factors. Such factors can be industry-specific operating needs, vehicle availability, driver skills, environmental impact, logistics balancing, and many other business requirements.

Route4Me’s proprietary optimization engine is capable of processing hundreds of thousands of addresses with multiple routing business rules to optimal routes for both Homogeneous and Mixed Fleets.

Route optimization for Homogeneous Fleet and Mixed Fleet with business routing rules and constraints.

 

 

What Is a Homogeneous or Homogeneous Fleet?

A Homogeneous Fleet is a fleet of vehicles with similar vehicle parameters such as vehicle carrying capacity, weight, height, length, allowed weight per axel, etc. A Homogeneous Fleet is the opposite of a Mixed Fleet, where fleet vehicles have different vehicle parameters.

An example of a Homogeneous Fleet is the fleet that consists of pickups that have relatively the same parameters such as weight, height, length, allowed weight per axel, etc.

 

What Is a Mixed Fleet?

A Mixed Fleet is a fleet of vehicles with different vehicle parameters such as vehicle carrying capacity, weight, height, length, allowed weight per axel, etc. A Mixed Fleet is the opposite of a Homogeneous Fleet, where fleet vehicles have the same or similar vehicle parameters.

An example of a Mixed Fleet is the fleet that consists of vans, trucks, pickup trucks, regular cars, and any other class 1-8 vehicle types that have different parameters and carrying capacity.

 

What Is a Collection of Optimization Constraints?

A collection of optimization constraints is a set of such routing business rules as the maximum weight per vehicle, maximum revenue per drivermaximum cubic volume per vehicle, maximum number of items per vehicle, etc. When using Homogeneous Fleet Optimization, Route4Me applies a single collection of optimization constraints to all imported addresses with routing business rules. When using the Mixed Fleet Optimization, Route4Me can apply multiple collections of optimization constraints to all imported addresses with routing business rules.

Collection of optimization constraints is a set of routing business rules as revenue, weight, etc.

 

Homogeneous Fleet Route Optimization

Homogeneous Fleet Optimization is designed to plan routes for a Homogeneous Fleet – the fleet of vehicles with the same or similar parameters and carrying capacity. Homogeneous Fleet Optimization accounts for a single collection of optimization constraints. A single collection of optimization constraints allows planning optimal routes for vehicles with similar parameters that match the collection of constraints you specify.

Uniform Fleet Optimization accounts for a single routing business rules and constraints collection.

 

For example, you may be running a service business, and you need to service 100 customers a day with a Homogeneous Fleet of 10 vehicles with the same parameters. You can create a single collection of optimization constraints, and Route4Me will use this collection to generate an optimal number of routes with the most cost-efficient number of stops for each vehicle without having to account for the different parameters of multiple vehicles.

 

Mixed Fleet Optimization

Mixed Fleet Optimization is designed to plan routes for a Mixed Fleet – the fleet of vehicles with different parameters and carrying capacity. Mixed Fleet Optimization can account for multiple collections of optimization constraints. Using multiple collections of optimization constraints allows planning optimal routes for vehicles with different parameters that match one of the specified constraints collections.

Mixed Fleet Optimization accounts for multiple routing rules and vehicle constraints collections.

For example, you may be running a delivery business, and you need to deliver 500 10 lbs packages a day with a Mixed Fleet of 10 vehicles, where each vehicle can fit a different number of packages. You can create multiple optimization collections with different routing business rules that fit each vehicle in your Mixed Fleet. Route4Me will use multiple optimization constraints collections and generate an optimal number of routes. Each route will have the most cost-efficient number of stops and maximize the capacity of all vehicles in your fleet, allowing you to save more money on cutting out unnecessary vehicles by making more deliveries with fewer vehicles.

 

Homogeneous Fleet Optimization for a Mixed Fleet

Problem: Using the Homogeneous Fleet Optimization for a Mixed Fleet can lead to not maximizing the capacity of each vehicle in your fleet. This way, you may end up using too many vehicles, paying too many drivers, burning too much fuel, having unrouted destinations, and experiencing other expensive routing inefficiencies.

Using Uniform Fleet Optimization for a Mixed Fleet results in unequal routing and load distribution.

Solution: Using Route4Me’s Mixed Fleet Optimization for a Mixed Fleet will help you ensure that each vehicle in your fleet is used to its maximum capacity and you can make more visits and deliveries with fewer vehicles.

 

Mixed Fleet Optimization SDK

Here you can download the Route4Me Mixed Fleet Optimization SDK.

 


 

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About Route4Me

Route4Me has over 40,000 customers globally. Route4Me's Android and iPhone mobile apps have been downloaded over 2 million times since 2009. Extremely easy-to-use, Route4Me's apps create optimized routes, synchronize routes to mobile devices, enable communication with drivers and customers, offer turn-by-turn directions, delivery confirmation, and more. Behind the scenes, Route4Me's operational optimization platform combines high-performance algorithms with data science, machine learning, and big data to plan, optimize, and analyze routes of almost any size in real-time.