A particle swarm optimization approach for constraint joint single buyer-single vendor inventory problem with changeable lead time and (r,Q) policy in supply chain
A particle swarm optimization approach for constraint joint single buyer-single vendor inventory problem with changeable lead time and (r,Q) policy in supply chain
复制标题
DOI：
10.1007/s00170-010-2689-0
复制
发表时间：
2010-12-01
影响因子：
3.4
通讯作者：
Jabbarzadeh, Armin
中科院分区：
文献类型：
--
作者：
Taleizadeh, Ata Allah;Niaki, Seyed Taghi Akhavan;Jabbarzadeh, Armin
研究了随机需求和提前期与批量成线性关系的机会约束联合单供应商-单采购商库存问题。考虑了缺货和销售损失的联合影响，需求服从均匀分布。该订单应放置在多个数据包，对每个产品的服务率限制被认为是一个机会约束，并有一个有限的预算，买方购买的产品。目标是确定每种产品的再订货点和订货量，使供应链总成本最小。这个问题的模型是一个整数非线性规划类型，为了解决它，粒子群优化（PSO）的方法。为了评估所提出的算法的效率，该模型求解使用遗传算法和模拟退火方法以及。通过算例对模型参数进行了敏感性分析，结果表明，所提PSO算法在供应链总成本方面优于其他两种方法。
In this paper, the chance-constraint joint single vendor-single buyer inventory problem is considered in which the demand is stochastic and the lead time is assumed to vary linearly with respect to the lot size. The shortage in combination of back order and lost sale is considered and the demand follows a uniform distribution. The order should be placed in multiple of packets, the service rate limitation on each product is considered a chance constraint, and there is a limited budget for the buyer to purchase the products. The goal is to determine the re-order point and the order quantity of each product such that the chain total cost is minimized. The model of this problem is shown to be an integer nonlinear programming type and in order to solve it, a particle swarm optimization (PSO) approach is used. To assess the efficiency of the proposed algorithm, the model is solved using both genetic algorithm and simulated annealing approaches as well. The results of the comparisons by a numerical example, in which a sensitivity analysis on the model parameters is also performed, show that the proposed PSO algorithm performs better than the other two methods in terms of the total supply chain costs.