Improved population-based incremental learning algorithm for vehicle routing problems with soft time windows

Xianghu Meng, Jun Li, Bin Qian, Mengchu Zhou, Xianzhong Dai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

An improved population-based incremental learning algorithm, in short IPBIL, is proposed to solve thevehicle routing problem with soft time windows (VRPSTW) with an objective to minimize the count of vehicles as well as the total travel distance.VRPSTW is subject to the soft time window constraint that allows to be violated but with penalty.In this paper, the constraint is embedded into a probability selection function and the original probability model of population-based incremental learning (PBIL) algorithm becomes 3-dimensional. This improvement guarantees that the population search of individuals is more effective by escaping from a bad solution space. Simulation of Solomon benchmark shows that the results average vehicle counts with IPBIL is reduced very significantly contrasted to those with Genetic Algorithm (GA) and PBIL, respectively. Both the average travel length and total time window violations by IPBIL are the least among these tested methods.IPBIL is more effective and adaptive than PBIL and GA.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
PublisherIEEE Computer Society
Pages548-553
Number of pages6
ISBN (Print)9781479931064
DOIs
StatePublished - Jan 1 2014
Event11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 - Miami, FL, United States
Duration: Apr 7 2014Apr 9 2014

Publication series

NameProceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014

Other

Other11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
CountryUnited States
CityMiami, FL
Period4/7/144/9/14

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Networks and Communications

Keywords

  • Global Exploration
  • Population-Based Incremental Learning Algorithm
  • Probability Model
  • Vehicle Routing Problems with Soft Time Windows

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  • Cite this

    Meng, X., Li, J., Qian, B., Zhou, M., & Dai, X. (2014). Improved population-based incremental learning algorithm for vehicle routing problems with soft time windows. In Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 (pp. 548-553). [6819685] (Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014). IEEE Computer Society. https://doi.org/10.1109/ICNSC.2014.6819685