Abstract:

With the rapid development of E-commerce， several farmers have opened online sales channel and shifted to a sale model of agricultural products through both online and offline. Fresh agricultural products need low temperature storage after harvest because of their perishable characteristic. Small-scale farmers in China often seek third-party cold storage services. However， because of the small scale and scattered production of farmers， their cold storage services put forward new requirements of convenience and availability. Conventional cold storage operators mostly serve enterprises， wholesalers and cooperatives. Meanwhile， the existing layout of cold storage is hard to meet the specific requirements of small-scale farmers. Therefore， how to choose the locations of cold storages and determine the service relationship of each storage to farmers become a key issue for many cold storage operators. In view of the location problem， many previous models mainly pursued to minimize costs while ignoring customers' satisfaction. Combined with the characteristics of small and dispersed cold storage demand of E-commerce farmers， farmers' satisfaction is measured from two dimensions： reliability of cold storage service and freshness of fresh products in transportation. Motivated by these observations， in the work a mixed integer linear programming model is proposed， considering factors such as distance， demand， service radius， fixed cost， variable cost， transportation cost and farmers' satisfaction. The farmers' satisfaction is introduced into the model in the form of penalty cost. The objective of the model is to minimize the total cost by optimizing the location of each cold storage and identifying the service relationship between cold storage and the farmers. In addition， an improved artificial bee colony algorithm is proposed， where the chaotic mapping is introduced in the initialization and scout bee search stages to improve the speed of the algorithm. The effectiveness and advantage of the improved artificial bee colony algorithm （IABC） are demonstrated by practical and random examples. Compared with the artificial bee colony algorithm （ABC） and particle swarm optimization algorithm （PSO）， the average solution time of IABC is reduced by 10.77% and 51.6% respectively. In the case study， the example data of apple cold storage locations in Luochuan County are taken as an example to verify the effectiveness of the work， and the influences of demand size， transportation distance and farmer satisfaction penalty cost on total cost， location results and service relationship are explored. Experimental results show that the total cost of cold storage is optimal when constructing 7 cold storages in the case of Luochuan County. The farmer's satisfaction has an important impact on the location of cold storage. Moreover， when the demand and transportation distance increase， increasing the number of cold storage is more advantageous than increasing the capacity of cold storage， which can effectively assist cold storage operators to make location decisions. Reasonable cold storage location decisions can effectively reduce postharvest losses of fresh agricultural products and have important significance to entire supply chain. Considering cold storage reliability and freshness of transportation services for farmers' satisfaction provides a new method of measuring the cold storage service.

Key words: fresh agricultural produce E-commerce, origin-based cold storage, location optimization, farmer satisfaction