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

Wendi Xu, Xianpeng Wang (), Qingxin Guo, Xiangman Song, Ren Zhao, Guodong Zhao, Yang Yang, Te Xu and Dakuo He
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Wendi Xu: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Xianpeng Wang: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Qingxin Guo: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Xiangman Song: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Ren Zhao: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Guodong Zhao: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Yang Yang: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Te Xu: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Dakuo He: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Mathematics, 2022, vol. 10, issue 16, 1-17

Abstract: 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.

Keywords: evolutionary transfer optimization; green scheduling; transfer learning; data analytics; system optimization; carbon neutrality (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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