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作 者:周超[1] 陈明[1] 王文娟[1] ZHOU Chao;CHEN Ming;WANG Wen-juan(Key Laboratory of Fisheries Information Ministry of Agriculture, College of Information Technology/Shanghai Ocean University, Shanghai 201306, China)
机构地区:[1]上海海洋大学信息学院,农业部渔业信息重点实验室,上海201306
出 处:《山东农业大学学报(自然科学版)》2017年第3期459-463,共5页Journal of Shandong Agricultural University:Natural Science Edition
基 金:国家科技支撑计划资助项目:鲜活水产品物流过程品质维持与质量安全控制技术集成应用与示范(2013BAD19B06)
摘 要:近年来,随着电子商务与互联网的不断发展,如何能够将传统的线下商品交易与现代的互联网经营模式相结合成为研究热点。作为日常生活中的消费大户,水产品由于其易腐性,其对于生产销售的时效性要求更为突出,将其与电子商务线上交易相结合可以极大提高其销售效率。本文在研究了线上交易的背景下,水产品交易匹配优化的问题。首先定义了新的买卖双方的商品交易匹配度及其计算方法,以此为基础构建了水产品交易匹配模型以最大化双方的加权匹配度。随后通过采用鸟群觅食算法针对模型进行求解。最后通过一个实例验证了算法的可行性,并与传统遗传算法进行对比证明了其有效性。In recent years,with the continuous development of e-commerce and the internet,how to combine the traditional offline commodity trading with the modern internet business model becomes a focus.Aquatic products have so large sale volume and perishability that the timeliness of the production and sales become more prominent and the combination with e-commerce online transaction can greatly improve the efficiency of sales.This paper studied the problem of matching optimization of aquatic product trading in the context of online trade.First of all,a new trading matching degree and its calculation method were defined so as to construct the aquatic product transaction matching model to maximize the weighted matching between the two sides.Then the bird foraging algorithm was used to solve the model.Finally,an example was given to verify the feasibility of the proposed algorithm,and the effectiveness of the proposed algorithm was verified by comparison with the traditional genetic algorithm.
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