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Hierarchical production planning in flexible automated workshops with delay interaction

机译:具有延迟互动的灵活自动化车间中的分层生产计划

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This paper addresses the hierarchical production planning (HPP) problem for flexible automated workshops (FAWs) with delay interaction, each with a number of flexible manufacturing systems (FMSs). The delay interaction aspect arises from taking into consideration the transfer of parts between FMSs. Any job which requires processing on more than one FMS cannot be transferred directly from one FMS to the next. Instead a semi-finished-product completed in one period must be put into shop storage until the next period at which it can be transferred to the next FMS for further processing. The objective is to decompose medium-term plans (assigned to an FAW by ERP/MRP II) into short-term plans (to be executed by FMSs in the FAW) so as to obtain the lowest production cost. The HPP problem is formulated in this paper by a nonlinear programming model whose constraints are linear but whose objective junction is piecewise linear. For the convenience of solving the nonlinear programming model, it is transformed into a linear programming model. Because the model for a general workshop is too large to be solved by the simplex method on a personal computer within acceptable time, Karmarkar's algorithm and an interaction/prediction algorithm, respectively, are used to solve the model, the former for medium- or small-scale problems and the latter for large-scale problems. With the implementations of these algorithms and with many HPP examples, Karmarkar's algorithm, the interaction/prediction algorithm and the linear programming method in Matlab 5.0 are compared, showing that the proposed approaches are very effective.
机译:本文解决了具有延迟交互作用的柔性自动化车间(FAW)的分层生产计划(HPP)问题,每个车间都具有许多柔性制造系统(FMS)。延迟交互方面是由于考虑了FMS之间零件的转移而产生的。任何需要在多个FMS上进行处理的作业都不能直接从一个FMS转移到下一个。取而代之的是,必须将在一个时期内完成的半成品放入商店存储,直到下一个时期可以将其转移到下一个FMS进行进一步处理。目的是将中期计划(由ERP / MRP II分配给一汽的分解)分解为短期计划(由一汽的FMS执行),从而获得最低的生产成本。 HPP问题是由非线性规划模型提出的,该模型的约束条件是线性的,但目标结是分段线性的。为了方便求解非线性规划模型,将其转换为线性规划模型。由于一般车间的模型太大,无法在可接受的时间内用个人计算机上的单纯形法求解,因此分别使用Karmarkar算法和交互/预测算法来求解模型,前者适用于中型或小型大规模问题,而后者则是大规模问题。通过这些算法的实现以及许多HPP实例,比较了Matlab 5.0中的Karmarkar算法,交互/预测算法和线性规划方法,表明所提出的方法非常有效。

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