Route optimization is the process of determining the shortest possible routes to reach a location. This methodology has gained popularity in the transport and logistics industry. Since it reduces the time spent traveling and at the same time reduces the incurred cost in the process.
Before a company or an organization can employ this strategy, it has to first be able to document all of its routes of business after which experts can then use the provided data to predict the best possible routes.
In the course of determining the best possible routes, there is usually a simulation of different scenarios. And ever since route optimization has gone digital with lots of software available today, the prediction of these routes is now done using the route optimization algorithm.
To better appreciate and understand what route optimization algorithm is expected to solve, knowledge of vehicle routing problem is important.
Whenever there is a discussion of vehicle routing problem, there is always a mention of the travel salesman problem (TSP). This problem is assumed to be the genesis of the vehicle routing problem.
First defined more than 150 years ago, the traveling salesmen problem was concerned with how salesmen can visit each of their customers once through the shortest possible route and return to their starting destination.
Over time as business strategy evolved, the travel salesmen problem now interpreted as vehicle routing problem (VRP) has become more complex.
Vehicle Routing Problem, first published by George Dantzig and John Ramser in 1959 is concerned with the best optimal routes for vehicles to deliver to a specific group of customers.
The publication of George Dantzig and John Ramser in 1959 is the first documentation of the use of an algorithm to solve TSP. Route optimization algorithm are groups of computer permutation used to solve specific routing problem. These algorithms are designed differently to achieve certain leverage such as reduction in travel time, cost or to ensure the maximal productivity of workers and trucks. Some algorithms may be designed to achieve all the stated leverage while others may focus on a few or just one.
Dantzig and Ramser employed the algorithm to attempt to solve the problem of delivering gasoline to service stations.
As stated earlier, the application of route optimization algorithm is dependent on the type of vehicle routing problem in consideration. Some of the VRP that exist are;
The problem in focus, in this case, is more similar to the general overview of route optimization. Certain goods are identified with the need to be delivered from a pick-up location. The aim of the algorithm, in this case, is to find the shortest possible route for vehicles to shuttle the pickup and delivery locations.
In this scenario, the time frame to deliver is the main consideration. Take, for instance, a logistics company with the promise of 24 hours delivery after an order will device its algorithm to solve how the goods can get to the destination within the given timeframe.
LIFO simply means Last In First Out. The problem of any algorithm used in this context is to solve is quite similar to that of VRPPD only that there is an attaching clause. The vehicle must be loaded with a certain amount of goods at any delivery location. To achieve this, the item to be delivered will be the one that was just added to the truck. The time spent unloading is aimed to reduce since only the recent ones are to be dropped off.
The devised algorithm, in this case, is concerned with how the fleet of vehicles can be loaded with a specific amount of goods no more no less. And the delivery time and cost are improved at the same time with maximal productivity of drivers.
Vehicle Routing Problem with Multiple Trips (VRPMT) In this case, the algorithm is meant to focus on how a single truck can make more than one trip and remain effective in cutting cost, travel distance and better employee (drivers) productivity.
The open vehicle routing problem algorithm does not need to bother about the return of the vehicle to the pickup location. The optimization task ends as soon as the vehicle reaches the destination. Although this problem looks simpler than the rest, it can be implemented in a complex way. Such as making each trip a different route. The pickup to the delivery might be treated like a trip while the return can also be treated as same in instances the truck may be delivering goods on its return too.
The classification of the vehicle routing algorithm is determined by each pundit’s perception. This is why different scholastic publication can name the same thing with different name.
High Number of Stops AlgorithmThis type of algorithm is used for route optimization with many numbers of stops ― more than 150. Industries it is usually used include newspaper delivery, waste collection, postal deliveries, and a school bus. These stops are usually arranged to be closer to each other and also needs to be visited at the same time.
Few Number of Stops AlgorithmThe name says it all. It is the direct opposite of the high number of stops algorithm. The algorithm is programmed to tackle a route optimization problem with lesser than 150 stops. Things are however made more complicated in this case. The stops are not necessarily to be close to each other. They only have to share the time of need to visit.
Special Trip Interval AlgorithmThe algorithm is expected to factor in the fact that the vehicle will be making some interval trip on its way to the destinations. For example, the vehicle may have 50 stops and after every five stops, it is expected to make a different trip such as visiting a filling station or grocery store to buy some stuff.
Route Optimization Algorithm and Big Data
For a successful algorithm implementation, it is required to note all involved data involve throughout the delivery system. This data may include;
How frequent customers order goods
Statistics of customers order
Areas with the most order and those with the least
Number of vehicles available for delivery
Geographical distance of pickup location to delivery
Some of these data are considered enough before the advancement of technology. Now, it is important customers are able to track their order. They need to be able to know the current location it is and how long it will take to get to them. Aside from that, the algorithm needs to be improved from simulations to being capable of instant permutation in the real world.
All of these are only possible through the integration of “big data”. Big data refers to a wide collection of data that can be used to improve how a company or software operate. As opposed to conventional software analytics, big data is used to gather data that surpass what traditional software can handle not even after multiple upgrades.
The gathered data are not mandatory to follow a specific pattern. A few years ago, all the unstructured data collected using big data compatible software would have been considered useless. Another key point to note about big data is that they are gathered from multiple sources and stored in multiple formats.
For instance, big data may include voice conversation of a customer representative with a customer, and at the same time stored along with the tracking information of the ordered goods. It may go further by including other data like the signed document of receipt and a picture of the order.
How Big Data Can Disrupt the Route Optimization Algorithm
Big data can be used by an electronic appliance manufacturer to track the performance of their product in homes of consumers. Whenever a product breaks down, the data is sent directly to the company through the embedded chip and a vehicle is scheduled to pick it up for repair even before the customer makes the call.
In instances when there is a need for a vehicle to pick up a product from a certain location, the use of big data can be employed to track the nearest vehicle and the pickup information can be instantly relayed.
Another way big data can transform the route optimization algorithm is by correctly predicting the market trend (i.e., daily order rate). News data, past orders trend, climate, and state of the economy are vital information that can be collected to forecast future orders and thus proper algorithm preparation of how to handle the orders.
Even though conventional route optimization is effective to some extent in predicting the optimal route for vehicles, big data can be used to detect the most efficient speed, time of the day and amount of fuel required to efficiently navigate these routes and probably even faster.
The use of route optimization has indeed disrupted how delivery system is operated saving companies extra cost and at the same time improving customers satisfaction. With the implementation of big data, the benefits experienced so far will be seen as the tip of an iceberg. Companies will be able to discover the number of orders coming in for the next day even when they are not yet in. They will also be able to gauge how satisfied their customers are and on top of it all, monitor the progress of drivers.
This technological invention is a way to improve the customers’ experience at a lesser cost.