不确定环境下的生产库存优化模型
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摘要
库存决策问题中的不确定性主要体现在需求、费用和供给等方面,随机情形下的库存问题已得到深入的研究并取得了丰硕的成果,然而在现实的库存决策问题中,不确定性还表现为模糊性、随机模糊性、模糊随机性等。目前关于这方面的研究还处于起步阶段,因此,研究如何建立不确定环境下的库存优化模型,将具有重大的应用价值和理论意义。
本文对不确定环境下的库存决策问题进行了研究,提出了一些更符合实际的库存优化模型,并设计了相应的求解算法,具体研究内容如下:
研究了模糊环境下的经济订购批量问题与经济生产批量问题,建立了不允许缺货情形下经济订购批量问题的模糊期望值模型和模糊相关机会规划模型,以及允许缺货并事后补足情形下经济生产批量问题的模糊期望值模型以及模糊机会约束规划模型,设计了模糊模拟算法和基于模糊模拟的粒子群优化算法对模型进行求解并通过数值例子说明了算法的可行性和效率。
研究了在随机和模糊双重不确定环境下的库存订购问题,在假设订购物品中不合格品的比率为随机变量而费用参数为模糊变量的情形下,建立了模糊随机期望值模型和模糊随机相关机会规划模型,设计了模拟算法和基于模糊随机模拟的粒子群优化算法来对模型进行求解并通过数值算例进行了说明。
研究了订购物品中不合格品的比率为随机模糊变量情况下的库存订购问题,建立了随机模糊期望值模型,为了求解所建立的模型,设计了基于随机模糊模拟的粒子群优化算法,最后通过数值算例进行了具体的求解和分析。
研究了连续盘点混合库存系统的优化决策问题,在假设提前期内的需求服从正态分布和自由分布,而缺货补足率等其它参数为模糊变量的情况下,分别建立了模糊随机期望值模型,然后分析了目标函数的性质,并分别设计了迭代求解算法对模型进行求解。
Uncertainty in inventory problems is shown mainly in demands, costs and replenishments. Stochastic inventory problem has been thoroughly studied and achieved fruitful results. But in the real inventory decision-making problems, there are always other uncertain phenomena, such as fuzzy phenomenon, random fuzzy phenomenon, or fuzzy random phenomenon. Till now, the research in this area is still in its infancy stage. Therefore, examine how to establish the optimize inventory model under uncertain environments, would be of great value and theoretical significance.
This dissertation considers the inventory decision-making problem in uncertain environments, and some optimization models according with the actual inventory control are proposed. Moreover, the corresponding algorithm is designed to solve these models. The contents are described as follows:
In fuzzy environments, the economic order quantity expected value model without backorder and economic order quantity dependent chance programming model without backorder are constructed. And also, the economic production quantity expected value model with backorder and economic production quantity chance constrained programming model with backorder are constructed. Then fuzzy simulation and particle swarm optimization algorithm based on the fuzzy simulation are designed to solve the dependent chance programming model and chance constrained programming model, respectively. Whereafter, numerical examples are presented to illustrate the feasibility and validity of the designed algorithm.
For the inventory problem in random and fuzzy coexisting environments, with assumptions that the percentages of nonconforming items are random variables and the cost parameters are fuzzy variables, the fuzzy random expected value model and fuzzy random dependent chance programming model are constructed, respectively. In order to solve these models, random simulation, fuzzy simulation, fuzzy random simulation and particle swarm optimization algorithm based on the fuzzy random simulation are designed. Then the validity of the designed algorithm is illustrated by numerical examples.
For the inventory problem in the case that the percentages of imperfect items are random fuzzy variables, this dissertation also constructs the random fuzzy expected value model. In order to solve the presented random fuzzy expected value model, particle swarm optimization algorithm based on the random fuzzy simulation is designed. Furthermore, a numerical example is presented to show the high efficiency and good performance of the designed algorithm.
For the continuous review inventory problem with backorders and lost sales for variable lead time in fuzzy random environments, with assumptions that the demand in leadtime follows the normal distribution and the free distribution, this dissertation constructs the fuzzy random expected value models, respectively. Then the propositions of the objective function are analysed. In addition, the iterative algorithms are designed to solve these models.
本文对不确定环境下的库存决策问题进行了研究,提出了一些更符合实际的库存优化模型,并设计了相应的求解算法,具体研究内容如下:
研究了模糊环境下的经济订购批量问题与经济生产批量问题,建立了不允许缺货情形下经济订购批量问题的模糊期望值模型和模糊相关机会规划模型,以及允许缺货并事后补足情形下经济生产批量问题的模糊期望值模型以及模糊机会约束规划模型,设计了模糊模拟算法和基于模糊模拟的粒子群优化算法对模型进行求解并通过数值例子说明了算法的可行性和效率。
研究了在随机和模糊双重不确定环境下的库存订购问题,在假设订购物品中不合格品的比率为随机变量而费用参数为模糊变量的情形下,建立了模糊随机期望值模型和模糊随机相关机会规划模型,设计了模拟算法和基于模糊随机模拟的粒子群优化算法来对模型进行求解并通过数值算例进行了说明。
研究了订购物品中不合格品的比率为随机模糊变量情况下的库存订购问题,建立了随机模糊期望值模型,为了求解所建立的模型,设计了基于随机模糊模拟的粒子群优化算法,最后通过数值算例进行了具体的求解和分析。
研究了连续盘点混合库存系统的优化决策问题,在假设提前期内的需求服从正态分布和自由分布,而缺货补足率等其它参数为模糊变量的情况下,分别建立了模糊随机期望值模型,然后分析了目标函数的性质,并分别设计了迭代求解算法对模型进行求解。
Uncertainty in inventory problems is shown mainly in demands, costs and replenishments. Stochastic inventory problem has been thoroughly studied and achieved fruitful results. But in the real inventory decision-making problems, there are always other uncertain phenomena, such as fuzzy phenomenon, random fuzzy phenomenon, or fuzzy random phenomenon. Till now, the research in this area is still in its infancy stage. Therefore, examine how to establish the optimize inventory model under uncertain environments, would be of great value and theoretical significance.
This dissertation considers the inventory decision-making problem in uncertain environments, and some optimization models according with the actual inventory control are proposed. Moreover, the corresponding algorithm is designed to solve these models. The contents are described as follows:
In fuzzy environments, the economic order quantity expected value model without backorder and economic order quantity dependent chance programming model without backorder are constructed. And also, the economic production quantity expected value model with backorder and economic production quantity chance constrained programming model with backorder are constructed. Then fuzzy simulation and particle swarm optimization algorithm based on the fuzzy simulation are designed to solve the dependent chance programming model and chance constrained programming model, respectively. Whereafter, numerical examples are presented to illustrate the feasibility and validity of the designed algorithm.
For the inventory problem in random and fuzzy coexisting environments, with assumptions that the percentages of nonconforming items are random variables and the cost parameters are fuzzy variables, the fuzzy random expected value model and fuzzy random dependent chance programming model are constructed, respectively. In order to solve these models, random simulation, fuzzy simulation, fuzzy random simulation and particle swarm optimization algorithm based on the fuzzy random simulation are designed. Then the validity of the designed algorithm is illustrated by numerical examples.
For the inventory problem in the case that the percentages of imperfect items are random fuzzy variables, this dissertation also constructs the random fuzzy expected value model. In order to solve the presented random fuzzy expected value model, particle swarm optimization algorithm based on the random fuzzy simulation is designed. Furthermore, a numerical example is presented to show the high efficiency and good performance of the designed algorithm.
For the continuous review inventory problem with backorders and lost sales for variable lead time in fuzzy random environments, with assumptions that the demand in leadtime follows the normal distribution and the free distribution, this dissertation constructs the fuzzy random expected value models, respectively. Then the propositions of the objective function are analysed. In addition, the iterative algorithms are designed to solve these models.
引文
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