Intelligent Optimization Algorithm for Chain Restaurant Spatial Layout Based on Generative Adversarial Networks

Sheng Xu

doi:10.70393/6a69656173.333031

OPEN ACCESS |Research Article ||1 June 2025

Intelligent Optimization Algorithm for Chain Restaurant Spatial Layout Based on Generative Adversarial Networks

Sheng Xu

University of Southern California

eva499175@gmail.com

Architecture, University of Southern California, LA, USA.

* Corresponding Author¹: Sheng Xu, E-Mail: eva499175@gmail.com

Publication

Accepted 2025 May 28 ; Published 2025 June 1

Journal of Industrial Engineering and Applied Science, 2025, 3(3), 3005-6071.

Abstract

This research presents an intelligent optimization algorithm framework for chain restaurant spatial layout generation based on Generative Adversarial Networks (GANs). Contemporary restaurant design methodologies rely on subjective expertise and static planning approaches that inadequately address dynamic operational requirements and evolving consumer preferences. The proposed GAN-based architecture incorporates a dual-generator framework with progressive upsampling modules and multi-head attention mechanisms specifically designed for restaurant spatial optimization. The multi-objective optimization function integrates operational efficiency metrics, spatial utilization coefficients, and aesthetic quality assessments through weighted objective aggregation, achieving balanced performance across competing design criteria. Experimental validation utilizing 3,892 restaurant layouts across 47 chain brands demonstrates substantial improvements in spatial layout quality metrics. Generated layouts achieve average efficiency scores of 87.3% compared to traditional baseline measurements of 72.8%, representing a 19.9% performance enhancement. The algorithm reduces average customer movement distances by 23.4% while maintaining 92.6% regulatory compliance rates. Implementation case studies across three distinct restaurant chains validate practical deployment feasibility with measurable improvements in operational efficiency ranging from 12.9% to 24.6%. The research establishes foundational technologies for next-generation intelligent restaurant design systems that enable data-driven optimization while reducing traditional design development timelines by approximately 65%. Commercial deployment analysis indicates potential cost savings of $12,000-$18,000 per location through reduced architectural consultation requirements.

Keywords

Generative Adversarial Networks , Spatial Layout Optimization , Restaurant Design , Multi-objective Optimization .

Metadata

DOI:

10.70393/6a69656173.333031

ARK:

ark:/40704/JIEAS.v3n3a04

Pages: 32-41

References: 26

Disciplines: Artificial Intelligence Technology

Subjects: Machine Learning

Cite This Article

APA Style

Xu, S. (2025). Intelligent optimization algorithm for chain restaurant spatial layout based on generative adversarial networks. Journal of Industrial Engineering and Applied Science, 3(3), 32-41. https://doi.org/10.70393/6a69656173.333031

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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