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考虑客户购买行为的易腐食品生产配送协同研究 被引量:1

Research on integrated production-distribution planning for perishable food considering consumer purchasing behavior
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摘要 随着客户对易腐食品新鲜度的要求不断提高,为了更好地满足客户需求,避免生产配送计划与客户购买行为间的信息不对称,文章以生产商利润最大为目标,提出店铺直送(direct store delivery,DSD)经营模式下的单生产商、多产品、多零售商、多时段的易腐食品生产配送协同计划模型,推导出关于产品新鲜度和质量风险的需求函数,模仿消费者对易腐食品的购买态度,在生产配送计划中加入保质期和客户购买行为约束,并采用蓄冷式多温共配模式对产品进行配送;最后用数值例子验证模型的有效性。研究结果表明,在易腐食品生产配送模型中考虑客户购买行为会提高生产商的利润,且产品质量风险越高,保质期越短,利润提高越多。 With the increasing demand of customers for freshness of perishable food,and in order to better meet the needs of customers and avoid the information asymmetry between the production-distribution planning and the consumer purchasing behavior,a single-manufacturer,multi-product,multi-retailer and multi-period production-distribution coordination model for perishable food under the direct store delivery(DSD) is proposed,aiming at maximizing the profit of the manufacturer. The demand function for product freshness and quality risk is deduced,the consumers’ attitude towards the perishable food is imitated,the shelf life and consumer purchasing behavior constraints are considered in the production-distribution planning,and products are delivered with a cold accumulation and insulated box multi-temperature joint distribution mode. Finally,the numerical example is used to verify the effectiveness of the model. The result shows that considering the consumer purchasing behavior in the production-distribution planning for perishable food will improve the manufacturer’s profit,and the higher the quality risk,the shorter the shelf life of the products,the more the profit increases.
作者 李畅 陈淮莉 LI Chang;CHEN Huaili(Institute of Logistics Science and Engineering,Shanghai Maritime University,Shanghai 201306,China)
出处 《合肥工业大学学报(自然科学版)》 CAS 北大核心 2019年第6期840-847,共8页 Journal of Hefei University of Technology:Natural Science
基金 国家自然科学基金资助项目(41505001) 上海市科学技术委员会重点资助项目(16040501800) 上海市科委科研计划资助项目(14DZ2280200)
关键词 易腐食品 生产配送 客户购买行为 协同优化 perishable food production and distribution consumer purchasing behavior collaborative optimization
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