Abstract: Establishing the cold chain logistics system of remote islands has a great significance. It is important to develop the fishing of aquatic products in remote sea, which can improve safeguard national marine rights and interests. Firstly, based on the characteristics of the cold chain logistics, two modes of transport organization including transiting to the warehouse built in the islands and directly transporting by refrigerated ships were considered. Next, viewing the least total cost as the decision object, the optimization model is constructed, which is involved in the two modes of transport, and an integrated algorithm is proposed according to the multiplicity of optimization routings in the model and the advantages of related algorithms. This integrated algorithm combines genetic algorithm as the external frame with improved plant growth simulation algorithm as internal module, and transfers the information between the inside and outside. Finally, the cold chain logistics system in South China Sea is used as an case to conduct the analysis. The results indicate that in the 10 fishing grounds of the remote islands in the South China Sea, both directly transporting by refrigerated ships and indirectly transiting to the warehouse are considered. On the one hand, four fishing grounds are sent to the cold storage for transfer and then transported by a refrigerated transport ship to the mainland fishing ports. On the other hand, the other 6 fishing grounds are directly purchased by two refrigerated processing ships and frozen and then transported back to the mainland fishing ports. Compared with the traditional genetic algorithm, the total cost decreases by 10.97%, the standard deviation and the variance decreases by 50% and 75%, and the calculation time decreases by 59.94%. Overall, the computing speed, the precision and the stability of calculation are obviously improved. It examines that the model and algorithm in this study are more valid and reasonable.

Key words: cold chain logistics, location, route, genetic algorithm, plant growth simulation