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Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Journal of Information and Optimization Sciences
Open Access Research Article

An Integrated MADM framework for parking space recommendation : Combining G1, entropy weight, and grey correlation TOPSIS method

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pp. 1187–1211Vol. 47Issue 3March 2026DOI: 10.47974/JIOS-2186XML
Received:
01 Jul 2025
Published Online:
14 Mar 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2186
Pages:
1187–1211

Abstract

Amid China’s sustained economic growth, rapid urbanization has precipitated a surge in vehicle ownership, while parking infrastructure expansion has failed to keep pace. This imbalance exacerbates urban parking scarcity, leading to inefficient unstructured parking searches by drivers. Such behavior increases energy consumption, environmental pollution, and reduces travel efficiency. The parking space recommendation problem constitutes a multi-attribute decision-making (MADM) challenge. To address this, this paper proposes an integrated optimization model combining the G1method, entropy weight method, and a grey correlation-enhanced TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach. The model employs a comprehensive evaluation index system incorporating both exact and interval-valued criteria. By fusing subjective weights (G1 method) and objective weights (entropy weight method), the framework overcomes the singularity and subjectivity limitations inherent in conventional weighting methods. Further, the integration of grey correlation analysis refines TOPSIS, enabling robust computation of comprehensive scores for parking schemes. A case study validates the model’s applicability, generating a prioritized parking spaces ranking. Results demonstrate that the model effectively recommends contextually suitable parking spaces by harmonizing users’ subjective preferences with objective scheme attributes.

Keywords

MADMG1 methodEntropy weight methodTOPSISInterval valuesParking spaces recommendation

Subject Classifications

90C29

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