需求和原料价格不确定下农产品供应链网络鲁棒优化设计

发布时间：2018-01-06 01:32

本文关键词：需求和原料价格不确定下农产品供应链网络鲁棒优化设计　出处：《管理工程学报》2017年04期 　论文类型：期刊论文

【摘要】：针对产出单一产品的多级农产品供应链网络(agri-food supply chain network,ASCN)鲁棒优化设计问题,以降低不确定性风险和总成本为目标,建立了集成生产设施选址、产能决策和物流网络运输模式选择的混合整数规划数学模型。模型中同时考虑了需求和农产品原料价格的不确定性,并采用情景法来描述有关不确定性。通过案例计算分析验证了模型的有效性。采用一种基于基因型-表现型概念的改进二元粒子群(Binary Particle swarm optimization,BPSO)算法并融合自适应变异技术,发展出一种自适应改进BPSO算法(AMBPSO)求解ASCN设计问题。通过将AMBPSO与基础BPSO(BBPSO)、带自适应变异的BBPSO(ABBPSO)算法和改进BPSO(MPSO)就三个案例的计算对比,验证了算法的有效性。计算结果表明AMPSO优于现有的BBPSO和MBPSO算法。
[Abstract]:The problem of robust optimal design for a multi-stage agricultural product supply chain network with a single product is presented in this paper, which is called agri-food supply chain network-ASCN. In order to reduce the uncertainty risk and total cost, the integrated production facility location is established. A mixed integer programming mathematical model for capacity decision and logistics network transportation mode selection. The uncertainty of demand and raw material price of agricultural products is considered in the model. A scenario method is used to describe the uncertainty. The validity of the model is verified by case analysis. An improved binary particle swarm optimization (PSO) based on the concept of genotypic phenotype is proposed. Binary Particle swarm optimization. BPSO) algorithm and fusion adaptive mutation technology. An adaptive improved BPSO algorithm is developed to solve the ASCN design problem. The comparison between the BBPSO-ABBPSO-based algorithm with adaptive mutation and the improved BPSO-MPSOs is made in three cases. The results show that AMPSO is superior to the existing BBPSO and MBPSO algorithms.
【作者单位】：南京财经大学粮食安全与战略研究中心/江苏省现代粮食流通与安全协同创新中心;东南大学机械工程学院;
【基金】：国家自然科学基金资助项目(71403114、No.71373116) 粮食公益性行业科研专项资助基金(201413005) 江苏省高校哲学社会科学研究基金资助项目(2014SJB135) 江苏高校优势学科建设工程资助项目 ‘青蓝工程’资助项目
【分类号】：F274;F304
【正文快照】： 0引言现代涉农企业之间的竞争已不单是企业之间的直接较量,而是升级为企业所处的农产品供应链之间的竞争,为提高市场份额,必须提升相应的农产品供应链的竞争力。农产品供应链一般是指围绕核心(龙头)企业,在信息共享的基础之上,由农产品的生产者(农户、生产基地)、生产商、分销

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