WeChat Mini Program
Old Version Features

An Efficient Route Evaluation Method for the Vehicle Routing Problem with Linear Constraints.

IEEM(2022)

Tokyo University of Marine Science and Technology

Cited 0|Views5
Abstract
The vehicle routing problem is the problem of minimizing the traveling distance of vehicles under the condition that every customer must be serviced by a vehicle. In some cases, a solution consists of not only vehicle routes but also the schedules of the vehicles along the routes. The scheduling problem for a vehicle route can often be formulated as a linear programming problem. In this paper, we propose the vehicle routing problem with linear constraints that a vehicle route can be evaluated as a linear programming problem. Many heuristic algorithms for vehicle routing problems use local search methods and the 2-opt* neighborhood, the cross exchange neighborhood and the Or-opt neighborhood are often used. We call them the standard neighborhoods. In this paper, we propose an efficient evaluation method for those neighborhoods for the vehicle routing problem with linear constraints. The computational results for randomly generated instances showed the effect of the proposed method.
More
Translated text
Key words
vehicle routing problem,linear constraints,local search
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined