Research on comprehensive optimisation of AGVs scheduling at intelligent express distribution centres based on improved GA

Shuaihui Tian, Chengyang Huangfu and Xueping Deng

Journal of the Operational Research Society, 2024, vol. 75, issue 10, 1875-1892

Abstract: This study addresses optimisation challenges in scheduling automatic guided vehicles (AGVs) for express distribution centres. A comprehensive model is developed that simultaneously considers makespan, AGV usage, and AGV recharging frequency to identify the optimal scheduling strategy and determine an effective recharging threshold. To address this, an enhanced adaptive genetic algorithm called LS-AGA (L-value and S-value based Adaptive Genetic Algorithm) is proposed. The LS-AGA employs a Logistic chaotic map to create an initial population. Fitness value factors and fitness entropy are incorporated to calculate individual L-values for selection, crossover, and mutation, maintaining a balance between fitness value and population diversity. The SoftMax function is introduced to map the L-values and fitness values into the corresponding probabilities, subsequently calculating individual S-values to optimise crossover and mutation rates. The addition of a catastrophe operator further enhances optimisation. Numerical and validation experiments demonstrate that LS-AGA outperforms existing improved genetic algorithms in solving the proposed model.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2023.2283518 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:75:y:2024:i:10:p:1875-1892

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2023.2283518

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().