摘要
针对订单农业供应链中农户实际供应与签约量不一致的问题,建立农产品供应不确定下最佳签约量决策模型。模型以农业企业的总期望成本最小为目标,在农户生产能力范围限制下同时对农户选择和签约量进行决策。考虑的成本项包括农户固定签约成本、生产服务成本、采购成本、缺货成本、过多供应处理成本。设计了求解模型的遗传算法和粒子群优化算法,通过不同规模仿真算例发现:粒子群优化算法整体上更适合求解所建立模型。参数分析表明:为了减少供应不确定风险带来的损失,总签约量应大于实际需求量;总签约量和最小期望成本随农户实际供应不稳定程度的增加而增大,随实际供应数量水平的提高而降低。
Aiming at the problem that supply of farmer is not consistent with the quantities in agreements in farming supply chain, a decision model for optimal order quantity- is developed with uncertainly supply. The goal of the model is minimization of total expected cost of agribusiness, while the selection of farmers and order quantities are decided simuhaneously with the con- straints of farmers＇ capacity-. The cost types of the model include fixed signing cost, cultivating service cost, sourcing cost, short cost, and handling cost of oversupply. The genetic algorithm and particle swarm optimization algorithm is designed respectively to solve the model. The resuhs computed from different numerical exmnples showed that PSO algorithm is more suitable to solve the model as a whole. The total contracted quantity should be larger than the actual demand in order to reduce the loss results of unsteady- supply risk. Moreover, the total contracted quantity- and minimized expected cost increase with the raise of unsteady- sup- ply extent, and reduce with the raise of actual quantities of supply.
作者
税文兵
杨汉卿
何民
SHUI Wenbing;YANG Hanqing;HE Min(School of Transportation Engineering,Kunming University of Science ＆ Technology,Kunming 650500,Chin)
出处
《物流科技》
2018年第8期32-37,共6页
Logistics Sci-Tech
基金
国家自然科学基金项目(71462024)
关键词
订单农业供应链
供应不确定
签约量
粒子群优化算法
遗传算法
contract farming supply chain
supply uncertainty
contracted quantity
particle swarm optimization algorithm
genetic algorithm
