An Optimal Allocation Method for Shared Private Parking Spaces Based on Lagrangian Relaxation.

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Bibliographic Details
Title:	An Optimal Allocation Method for Shared Private Parking Spaces Based on Lagrangian Relaxation.
Authors:	Guo, Zhongkai¹ guozhongkai@lzjtu.edu.cn, Wang, Xiaopeng² 2269193701@qq.com, Li, Tong² 2435268022@qq.com
Source:	IAENG International Journal of Applied Mathematics. May2026, Vol. 56 Issue 5, p1896-1910. 15p.
Subjects:	Resource allocation, Heuristic algorithms, Optimization algorithms, Lagrange multiplier, Smart parking systems, Parking facilities
Abstract:	This study first proposes an optimized parking allocation mechanism from the platform operator's perspective, which aims to enhance parking seekers' efficiency in identifying demand responsive spaces. And we develop a Lagrangian relaxation based heuristic algorithm for near optimal solutions. Numerical experiments demonstrate the superiority of our framework through systematic comparisons with the commercial solver CPLEX: the proposed algorithm achieves a 68.73% average reduction in computational time. Additionally, solution quality remains robust across all scenarios, with optimality gaps of 5.88% (max), 0% (min), and 3.64% (avg). The experimental design fully considers parameter variations across different city scales, ensuring that the conclusions have general applicability. Therefore, our proposed model and algorithm demonstrate significant potential for optimizing shared parking resource allocation.. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This study first proposes an optimized parking allocation mechanism from the platform operator's perspective, which aims to enhance parking seekers' efficiency in identifying demand responsive spaces. And we develop a Lagrangian relaxation based heuristic algorithm for near optimal solutions. Numerical experiments demonstrate the superiority of our framework through systematic comparisons with the commercial solver CPLEX: the proposed algorithm achieves a 68.73% average reduction in computational time. Additionally, solution quality remains robust across all scenarios, with optimality gaps of 5.88% (max), 0% (min), and 3.64% (avg). The experimental design fully considers parameter variations across different city scales, ensuring that the conclusions have general applicability. Therefore, our proposed model and algorithm demonstrate significant potential for optimizing shared parking resource allocation.. [ABSTRACT FROM AUTHOR]
ISSN:	19929978