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System and method for routing optimization

A routing and determining routing technology, applied in data processing applications, character and pattern recognition, instruments, etc., can solve the problems of testing candidate routes, difficulties, etc., and achieve the effect of improving the accuracy of routing optimization and short response time

Pending Publication Date: 2021-03-26
ALIBABA GRP HLDG LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It is well known that determining the optimal solution for VRP is NP-hard
In practice, it is often impossible to test every possible candidate route by trial and error due to constraints in resources, time, etc.

Method used

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  • System and method for routing optimization

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Embodiment Construction

[0041] Traditionally, due to the size and frequency of the real-life vehicle routing problems that need to be solved, heuristics are used to explore optimal routing schemes. A framework of heuristic algorithms such as classical hill climbing simply perturbs the candidate solutions in iterations until convergence. However, only perturbing candidates is time-consuming and limits the applicability of heuristic algorithms in real-life scenarios. Several other current works look at training end-to-end frameworks to solve TSP, VRP, and other combinatorial problems based on supervised machine learning algorithms. However, supervised learning requires the best solution for instances of the same VRP series to train the model, and it is often difficult to obtain the best solution.

[0042] Embodiments described herein provide methods, systems and apparatus for efficiently combining heuristics and RL to determine routes. The determined optimal routing solution may be provided as naviga...

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Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining routing using reinforcement learning (RL) are provided. One of the methods includes: initializing a state of an RL model based on a routing solution, wherein the RL model comprises a plurality of improvement actions for applying to the state; applying one or more of the plurality of improvement actions to the state to obtain updated routing solutions until a predetermined condition is satisfied; applying a perturbation action to obtain a perturbed routing solution and feeding the perturbed routing solution back to the RL model for the RL model to perform the applying one or more of the plurality of improvement actions according to the policy; and determining a routing solution with a minimum cost from the updated routing solutions.

Description

technical field [0001] The present application relates generally to systems and methods for determining routes, and more particularly to systems and methods for determining routes using reinforcement learning (RL). Background technique [0002] Route optimization may aim at identifying the routing solution with the best cost from a finite set of candidates. The classic Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP) are some examples of variations of routing optimization problems. Practical applications of route optimization can be found in areas such as telecommunications network design, task scheduling, transportation system planning, energy, finance, and supply chain. Routing optimization problems that involve finding efficient routes for vehicles are often referred to as Vehicle Routing Problems (VRP). There are many variants of VRP, such as loading and unloading VRP (VRPPD), LIFO VRP, VRP with time window (VRPTW), and VRP with capacity constraints. ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/08G06Q50/30
CPCG06Q10/047G06Q10/08355G06Q50/40G01C21/3453G01C21/3415G06N20/00G06N3/006G06F18/217
Inventor 张兴文陆昊华致刚杨双红
Owner ALIBABA GRP HLDG LTD
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