Solving the Single-Source Capacitated Facility Location Problem Using a Crow Search-Based Hybrid Method

The Single Source Capacitated Facility Location Problem (SSCFLP) stands as a pivotal yet highly complex challenge in facility location science, underpinning real-world supply chains where single-sourcing constraints are critical for service quality and operational efficiency. This study introduces a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access Vol. 13; pp. 82847 - 82859
Main Authors: Xu, Wangyue, Luis, Martino, Yuce, Baris
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Benchmarks
binary crow search algorithm
Computational efficiency
Convergence
Costs
Crow search algorithm
exact method
Heuristic algorithms
hybrid algorithm
Markov chains
meta-heuristics
Optimization
Robustness
Search algorithms
Search problems
semi-greedy algorithm
Solution space
Supply chains
Transfer functions
Transportation
ISSN:2169-3536, 2169-3536
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The Single Source Capacitated Facility Location Problem (SSCFLP) stands as a pivotal yet highly complex challenge in facility location science, underpinning real-world supply chains where single-sourcing constraints are critical for service quality and operational efficiency. This study introduces a hybrid solution framework that merges a Binary Crow Search Algorithm (BinCSA) with an exact method, leveraging XOR logic, bit-flip mechanisms, and a dynamic awareness probability (AP) scheme to prevent premature convergence. Across medium-, large-, and extreme-scale benchmark instances, the enhanced BinCSA demonstrates accelerated convergence relative to the original BinCSA. By decomposing the SSCFLP into two subproblems, the proposed approach eases the burden on the exact solver and circumvents the need for transfer functions. A Markov chain analysis underpins BinCSA's theoretical convergence guarantees, supported by computational results that confirm irreducibility and convergence within a finite solution space. Experiments on benchmark datasets show that BinCSA yields high-quality solutions with efficient runtimes, performing competitively against existing heuristics as well as a CPLEX baseline. To the best of our knowledge, although the Crow Search Algorithm (CSA) has been applied to uncapacitated facility location problems (UFLP), the present work is among the first to adapt CSA-based methods to SSCFLP, thereby extending the algorithm's scope of applicability. Overall, these findings highlight BinCSA's robustness and efficiency in tackling large-scale, NP-hard optimization problems, paving the way for broader applications in supply chain design, telecommunication networks, and other high-tech domains.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3565153