关键词: 物流网络, 渔业, 冷链, 多目标优化, 南海

Abstract: The waters of the South China Sea are vast and rich in fishery resources. However, due to the distance of areas such as the Nansha Islands from the mainland, fishing vessels lack the endurance for prolonged operations, thus hindering their effective exploitation. Recently, with the progress of island construction projects in the South China Sea, governments have increased their support for fishermen, enabling the establishment of an integrated fishing production system covering fishing, acquisition, storage, and transportation. Against the backdrop of escalating disputes over marine resources in the South China Sea, the Chinese government has proposed policies such as “developing the Nansha Islands with fisheries as a priority” and “setting up fishery settlements to safeguard borders”, making the waters of the Nansha Islands in China an important direction for the development of offshore fisheries. Constructing a cold chain logistics network for fisheries is crucial for the development of China’s marine economy and the safeguard of maritime interests. However, existing literature lacks a theoretical analysis of the coupling concept and operating mode of the cold chain logistics network for offshore fisheries, and there is a lack of effective modeling methods to explore the network planning issues, which directly affects the in-depth development of theory and practice.
In view of this, this paper takes the waters of the Nansha Islands in China as the background. Based on an analysis of the characteristics of cold chain logistics for offshore fisheries in distant waters, it comprehensively uses fishery resources survey data, geographic information technology, and bio-economic production models to quantitatively assess the catchable amount and potential fishing production areas. Based on this, the paper studies the layout of fishing production areas and the selection of transit cold storage sites in the Nansha Islands, considering both fishing catch volume and the total operating cost of the cold chain system. Its main contributions are as follows: (1)Establishing a bi-objective optimization model focusing on maximizing the weighted coverage rate of fishery resources and minimizing the total operating cost of the cold chain system, with primary nodes including fishing production areas, island-based transit cold storage facilities, and mainland fishing ports. The layout of fishing production areas, selection of fishing transportation modes, site selection and capacity determination of transit cold storage facilities, and configuration of refrigerated vessels are involved. (2)Introducing GIS to obtain fishery resources quantity and spatial distribution for optimization analysis, establishing the collaborative interaction between fishery information and optimization information. (3)Designing an integrated algorithm with the improved NSGA-II algorithm as the external framework and the improved k-means clustering algorithm as the internal module, determining the final optimized solution through continuous interaction between modules. (4)Optimization analysis is conducted on the real case of fishing production activities in the Nansha Islands. By analyzing the basic principles guiding decision-makers in selecting the optimal solution and filtering Pareto non-dominated solutions, the effectiveness of the proposed algorithm and model is validated.
The outcomes of this paper propose an optimization method aiming to effectively organize and manage cold chain activities in offshore fisheries. Establishing cold storage facilities of a certain scale on islands can significantly enhance the efficiency of fishing production, providing new insights for more effective development of South China Sea fishery resources and the construction of an integrated fishing production system encompassing fishing, acquisition, storage, and transportation. Therefore, it holds important theoretical and practical value.

Key words: logistics network, fish industry, cold chain, multi-objective optimization, South China Sea