Development in Vehicle Routing and Scheduling Market: Researchers Develop an Approach to Improve Efficiency of Algorithms Designed to Handle Vehicle Routing

• Analysis
• 16-December-2021

A complex arithmetic problem needs to be solved before the delivery truck can arrive at the consumer's home. While algorithms are designed to handle this problem for a few hundred cities, they become excessively slow when applied to a more extensive list of cities.

Researchers have now devised a plan that could hasten the solution. They developed a machine-learning method that speeds up some of the most potent algorithmic solvers by tens to hundreds of times. The technique would be an outstanding contribution towards Vehicle Routing and Scheduling Market. This is because it can solve vehicle routing issues like last-mile delivery. The goal is to distribute items from a central depot to several locations while minimizing trip costs.

The solver algorithms function by breaking down the delivery problem into smaller subproblems to solve—for example, 200 subproblems for truck routing between 2,000 cities. The team added this process with a new machine-learning method that, rather than addressing all of the subproblems, identifies the most useful subproblems to tackle, allowing them to improve the quality of the answer while consuming orders of magnitude less computing. This is because it would be based on addressing the issue instead of tackling all of them.

The researchers claim that their method, which they name "learning-to-delegate," may be used to a wide range of solvers and problems, including scheduling and pathfinding for warehouse robots.

Prior works have introduced a range of techniques to solve combinatorial issues during the last several decades quickly. They typically achieve this by beginning with a substandard but workable solution and gradually improving it, for example, by testing with minor changes to enhance routing between neighbouring cities. However, this method takes far too long for a significant problem like a 2,000-city routing challenge.

Machine-learning approaches have been created more recently to overcome the problem, but while they are faster, they are less accurate, even at the scale of a few dozen cities. Thus, researchers decided to test if combining the two methodologies may help them quickly identify high-quality answers in the present study.

In this case, the researchers ran sets of subproblems through a neural network. The network was primarily created to find the subproblems autonomously. Specifically, the difficulties that, if solved, would result in the most remarkable improvement in the quality of the solutions. The results showed that the process could speed up the subproblem selection process by 1.5 to 2 times. The team added that since the method can work with various solvers, it may be helpful for a variety of resource allocation problems. Now, there is an opportunity to unlock new applications. This is only possible because the cost of solving the problem is 10 to 100 times less.

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