The route optimization software market is experiencing rapid growth, with an expected Compound Annual Growth Rate (CAGR) of 14.1% from 2023 to 2030. Over recent years, this market has evolved from static to dynamic route optimization, incorporating various innovations.
Picture this scenario: a large volume of packages arrives at a destination country and needs distribution within a specific timeframe. Managing multiple fleets simultaneously becomes imperative. How does a logistics enterprise ensure on-time deliveries for each fleet, maximizing efficiency? Here is where real-time dynamic routing software comes into role play.
In this blog post, we’ll explore what real-time dynamic routing software is, its data source, and the challenges it encounters while handling real-time data. Let’s dive in.
What is Real-time Dynamic Routing Software and Its Data Sources?
Real-time dynamic routing software leverages algorithms for optimizing routes in real-time, ensuring fast and efficient deliveries even in last-minute journeys. The core purpose of this software is to help logistics companies enhance their fleet efficiency by managing and facilitating faster deliveries of on-demand orders.
Behind the scenes, algorithms work tirelessly to streamline the process. Here are some key data sources for dynamic route optimization software:
GPS devices Data (longitude, latitude, and altitude)
On-board Diagnosis Data
Sensors’ Data (fuel sensors, temperature sensors, camera, etc.)
Although dynamic route optimization software plays an essential role in effective route planning, achieving a reliable fleet management solution is still a hurdle for logistics companies. And the difficulty in storing, handling, and analyzing real-time data streaming from thousands of fleets is one of the reasons.
Let’s explore the major concerns of fleet management companies while managing dynamic route optimization software.
A Depiction of the Working of the Dynamic Route Planning Algorithm
Dijkstra's algorithm is good for finding the shortest path between two points on a map, but it doesn't take into account things like traffic and weather conditions.
A* is a variant of Dijkstra's algorithm that can take heuristics into account, which can make it faster to find paths in some cases.
Genetic algorithms are good for finding approximate solutions to complex problems, but they can be slow.
Ant colony optimization is inspired by the way that ants find paths, and it can be good for finding short paths in graphs that change over time.
Tabu search is good for finding good solutions to optimization problems, but it can be slow.
Incremental graphs are good for representing graphs that change over time, and they can be used with other algorithms to find paths in these graphs.
Real-time Data Management: An Upcoming Challenge for Route Optimization Software
Some route optimization algorithms may experience longer computation times, especially when processing a high volume of requests from numerous fleets. This can lead to application instability, particularly as road networks expand rapidly, achieving a degree of scalability with specific algorithms becomes a hard nut to crack.
Let’s explore these data-related issues that come in the way of effective route planning and optimization in detail.
Difficulty in Managing a High Influx of Real-time Data Streaming
Deriving actionable insights for effective route planning isn’t as easy as it looks. It’s an ongoing juggle of capturing, processing and analyzing real-time data streams from GPS devices, telematics systems, and various sensors within a few seconds.
Ingesting and handling this high influx of data in real-time with ease is still a castle to achieve for most fleet management companies.
What's the Solution?
Implementing a platform-based IoT solution capable of handling diverse data types and sizes effectively can streamline real-time data management processes. This solution should prioritize quick data ingestion and processing to enable prompt responses to changing conditions.
Complexities and Time Consumption in Handling System Interoperability
Integrating multiple systems and technologies for route optimization complicates operations, particularly in transforming data from various sources. Achieving seamless interoperability among different data formats and systems becomes a time-consuming process.
What's the Solution?
There should be a central hub that seamlessly transforms and integrates diverse data streams from various sources, such as GPS trackers, traffic sensors, weather updates, and customer requests. Besides that, it should hold the capabilities to prepare the data for further analysis and optimization. As a result, it will allow fleet owners to define specific rules and conditions as per their requirements, helping them predict events by analyzing certain anomalies in the datasets.
For instance, if the speed parameter changes from 100 to 20km/hr in 3 seconds, it’s considered to be harsh braking in the system. If the same happens with multiple vehicles at a specific location over a while, it will give predictive insights into unsafe driving patterns, allowing fleet owners to take preventive measures.
Hurdles in Reaching the Sufficient Level of Digitalization for Quality Data
The entire route optimization algorithm runs on data. It’s also undeniable that not all the ingested data is equal. Some of the data is of poor quality too. Furthermore, the optimization solution leverages this irrelevant data for calculating impractical routes.
But why do OEMs or fleet managers fail to effectively leverage volumes of quality data to power their digital transformation initiatives? Ever thought? Here are some possible origins for the poor-quality data:
When the user accidentally enters data in the wrong field
Due to delay in the validation of loads of data packets during the migration process leading to parsing errors, memory issues, connection limits, etc
If the data and version control have been lost due to software update failures Because of improper usage of the system or bad coding
If the fleet manager renders unfit data that was initially fit
What's the Solution?
Fleet operators or owners should embed a rule engine into their existing IoT infrastructure to validate the high volume of real-time data packets streaming from various sources. This rule engine will allow them to define specific rules and conditions to handle exceptions and customize the optimization process.
Since the rule engine can access messages from different channels or protocols directly, it will reduce data latency. Besides that, it will enhance vehicle efficiency by detecting and responding to anomalies, such as unplanned vehicle downtime.
Absence of a Scalable Database Management System
One of the major issues while managing route optimization software is the unavailability of the data required for calculations. Probably, it can be due to the situational data that is only true on a given day or the data that hasn’t been entered in the database.
Enlisted below are a few examples of the day-to-day complexities related to real-time data constraints that planners and drivers have to deal with:
Driver A is late this morning and will start the journey half hour later
Driver B knows this area better than anyone else
The customer argued with Driver C and doesn’t want to be delivered by him anymore, and more.
Although the above-mentioned constraints are essential for realistic solutions while optimizing routes, fleet managers sometimes fail to find this data in any of the databases.
What's the Solution?
During the development stage of dynamic route optimization software, fleet managers should opt for an efficient database storage pipeline. Specifically, it will ensure the availability of large volumes of historical and real-time data for analysis, reporting, and future optimization improvements.
As a result, it will provide a flexible and customizable framework to fleet owners for building their route optimization solutions as per their specific requirements and business workflows. For example, fleet owners can set rules based on real-time data constraints. To name a few, automatically assigning the task of parcel delivery to the equivalent alternative of Driver A if he is late on a specific day.
Vulnerabilities Related to Data Privacy
Handling real-time data for route optimization software involves dealing with sensitive information, such as vehicle locations, customer addresses, and delivery schedules. Fleet management companies need to implement robust security measures to protect their data from unauthorized access, vulnerabilities, threats, and potential breaches.
However, it becomes a challenging task due to different types of vulnerability attacks in any manner.
What's the Solution?
Although challenges related to data privacy are a concern, overcoming them is possible by encrypting the data from the source. It is also achievable by logically isolating the data via private internal IP and making it accessible inside the VPC.
As a fleet owner, it will give you more control over your data, right from protecting it from unconsented data sharing to building customized route optimization solutions.
How Condense Manages Real-time Data for Effective Route Optimization?
Zeliot’s Condense, a verticalized mobility data platform, is a one-stop solution to all the above-discussed challenges that fleet owners face. It’s a low-code, click-to-deploy managed application that fleet operators can integrate into their existing cloud infrastructure.
In this way, they’ll have more control over the data that they will utilize to build solutions based on their specific need. In case of route optimization, fleet owners can leverage its robust map engine module to customize their application for route optimization.
With its advanced mapping capabilities, such as geocoding, routing algorithms, and spatial analysis, this module ensures detailed customization of route optimization algorithms. Live traffic conditions, road networks, and customer preferences are some of the factors.
Conclusion:
In a parallel world of advancing logistics technology, achieving cost-efficient fleet management operations is no small feat. As a fleet owner, you need a detailed customization for your dynamic route optimization algorithms to derive actionable insights.
These insights will decide how fast you can respond to time-sensitive situations. You can achieve this by deploying a verticalized data platform into your existing IoT infrastructure, enabling endless possibilities with route optimization. How? This blog post explains it well.
Does this blog post make you curious about the possible customization options for your existing fleet management solution? Feel free to contact Zeliot’s sales team to explore more about it.