• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Knowledge-Based Systems >Replenish-up-to multi-chance-constraint inventory control system under fuzzy random lost-sale and backordered quantities
【24h】

Replenish-up-to multi-chance-constraint inventory control system under fuzzy random lost-sale and backordered quantities

机译：模糊随机亏售和缺货情况下的多机会约束库存控制系统补货

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, a multiproduct multi-chance constraint stochastic inventory control problem is considered, in which the time-periods between two replenishments are assumed independent and identically distributed random variables. For the problem at hand, the decision variables are of integer-type, the service-level is a chance constraint for each product, and the space limitation is another constraint of the problem. Furthermore, shortages are allowed in the forms of fuzzy random quantities of lost sale that are backordered. The developed mathematical formulation of the problem is shown to be a fuzzy random integer-nonlinear programming model. The aim is to determine the maximum level of inventory for each product such that the total profit under budget and service level constraints is maximized. In order to solve the model, a hybrid method of fuzzy simulation, stochastic simulation, and particle swarm optimization approach (Hybrid FS-SS-PSO) is used. At the end, several numerical illustrations are given to demonstrate the applicability of the proposed methodology and to compare its performances with the ones of another hybrid algorithm as a combination of fuzzy simulation, stochastic simulation, and genetic algorithm (FS-SS-GA). The results of the numerical illustrations show that FS-SS-PSO performs better than FS-SS-GA in terms of both objective functions and CPU time.
机译：本文考虑了一种多产品多机会约束随机库存控制问题，其中两个补货之间的时间周期被假定为独立且分布均匀的随机变量。对于当前的问题，决策变量是整数类型，服务级别是每种产品的机会约束，而空间限制是问题的另一个约束。此外，允许以延期交货的模糊随机数量的销售损失来短缺商品。问题的发展数学公式显示为模糊随机整数-非线性规划模型。目的是确定每种产品的最大库存水平，以使预算和服务水平约束下的总利润最大化。为了求解模型，使用了模糊仿真，随机仿真和粒子群优化方法（Hybrid FS-SS-PSO）的混合方法。最后，给出了几个数值示例，以证明所提出方法的适用性，并将其性能与另一种混合算法（模糊仿真，随机仿真和遗传算法（FS-SS-GA）相结合）的性能进行比较。数值结果表明，无论是目标函数还是CPU时间，FS-SS-PSO的性能均优于FS-SS-GA。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号