Gathering Strength, Gathering Storms: Knowledge Transfer via Selection for VRPTW

Recently, due to the growth in machine learning and data mining, for scheduling applications in China’s industrial intelligence, we are quite fortunate to witness a paradigm of evolutionary scheduling via learning, which includes a new tool of evolutionary transfer optimization (ETO). As a new subse...

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Bibliographic Details
Published inMathematics (Basel) Vol. 10; no. 16; p. 2888
Main Authors Xu, Wendi, Wang, Xianpeng, Guo, Qingxin, Song, Xiangman, Zhao, Ren, Zhao, Guodong, Yang, Yang, Xu, Te, He, Dakuo
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2022
Subjects
carbon neutrality
data analytics
Data mining
Design
evolutionary transfer optimization
Genetic algorithms
green scheduling
Industrial applications
Knowledge
Knowledge management
Logistics
Machine learning
Mathematical programming
Mathematical research
Mathematics
Multiple objective analysis
Optimization
Route planning
Scheduling
Storms
system optimization
transfer learning
Travel
Traveling salesman problem
Vehicle routing
China
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Summary:Recently, due to the growth in machine learning and data mining, for scheduling applications in China’s industrial intelligence, we are quite fortunate to witness a paradigm of evolutionary scheduling via learning, which includes a new tool of evolutionary transfer optimization (ETO). As a new subset in ETO, single-objective to multi-objective/many-objective optimization (SMO) acts as a powerful, abstract and general framework with wide industrial applications like shop scheduling and vehicle routing. In this paper, we focus on the general mechanism of selection that selects or gathers elite and high potential solutions towards gathering/transferring strength from single-objective problems, or gathering/transferring storms of knowledge from solved tasks. Extensive studies in vehicle routing problems with time windows (VRPTW) on well-studied benchmarks validate the great universality of the SMO framework. Our investigations (1) contribute to a deep understanding of SMO, (2) enrich the classical and fundamental theory of building blocks for genetic algorithms and memetic algorithms, and (3) provide a completive and potential solution for VRPTW.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10162888