两阶段模糊生产计划期望值模型

A General Two-stage Fuzzy Production Planning Expected Value Model

在现实的生产系统中,生产计划问题常常是一个确定的线性规划问题。但是,在许多的实际情况中,由于生产系统中不确定性因素的影响,带有常系数的线性规划模型不能合理地描述现实的决策环境。为了准确有效地描述生产决策环境,本文提出一类新的带有模糊参数的两阶段生产计划期望值模型并且讨论模型的一些基本性质。然后,讨论补偿函数的逼近并且设计一个基于逼近方法、神经网络和遗传算法的启发式算法来求解这个两阶段模糊生产计划模型。最后,给出一个数值例子来表明所设计算法的可行性和有效性。

In realistic production system, production planning problem is often a deterministic linear programming problem. But, in many actual case, the linear programming model with constant cofficients does not properly describe realistic decision-making environment because of the influence of the uncertainty factors in production system. In order to precisely and effectively describe production decision-making environment, this paper will present a new class of two-stage production planning expected value model with fuzzy parameters and deal with some basic properties of model. Then it will deal with the approximation of recourse function and design a heuristic algorithm, which combines approximation approach, neural network and genetic algorithm to solve this two-stage fuzzy production planning model. Finally, a numerical example is given to show the feasibility and effectiveness of the designed algorithm.