Enhanced Search Spring Algorithm for Green Agri-Food Supply Chain Network Design

Supply chain management plays a critical role in the process of converting raw materials into final products and services. Although, conventional supply chains may fall short of the modern expectations of efficiency, cost-efficiency and sustainability. Green Agri-Food Supply Chain Networks (GASCN) a...

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Bibliographic Details
Published in:IEEE access Vol. 13; pp. 109034 - 109045
Main Author: Hu, Xiaoya
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Agricultural products
Agriculture
Algorithms
Artificial intelligence
Costs
Efficiency
efficient management
Enhanced search spring algorithm
Food supply
green agricultural supply chain network
Heuristic methods
Mathematical models
Metaheuristics
Network design
Operating costs
Optimization
Raw materials
Resource utilization
Springs
supply chain
Supply chains
Transportation
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Supply chain management plays a critical role in the process of converting raw materials into final products and services. Although, conventional supply chains may fall short of the modern expectations of efficiency, cost-efficiency and sustainability. Green Agri-Food Supply Chain Networks (GASCN) are facing high transportation cost, long-time delivery, and inefficient resource utilization, which this study aims to address. In light of these challenges, this study presents a new Enhanced Search Spring Algorithm (ESSA), which optimizes GASCN by minimizing total transportation costs and is characterized by improved solution quality and computational efficiency. The ESSA algorithm integrates a non-linear adaptive weight factor and chaotic map strategies to lay a foundation for balancing the exploration or exploitation of the transmitted solution, allowing it to outpace existing metaheuristic algorithms. The results show that ESSA is superior to state-of-the-art algorithms. In the benchmark tests, ESSA gained much lower mean fitness values (61% lower than GA and 59% lower than PSO) and execution times (32% lower than PSO execution time). For example, in a real-world case study, ESSA minimized transportation cost at 40,000.13 units lower than GA (77,152.08 units) and PSO (77,025.35 units). Moreover, ESSA provides a stable scalability with time complexity proportional to linear growth with increasing problem size. This solution provides an efficient model for agricultural supply chain framework optimization, to a great extent attain quicker conclusions and effective asset appropriation. An important transformation from the time-tested approaches taking us toward sustainable and efficient agri-food systems, ESSA patterns focus on the urgency of these critical problems: cost reductions and operational inefficiencies.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3578286