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Optimal Control Of A Nested-multiple-product Assemble-to-order System

机译:嵌套多产品按订单组装系统的最优控制

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In this paper, we study an assemble-to-order system consisting of n products assembled from a subset of m distinct components where the products have a modular nested design, i.e. product i has only one additional component more than product i-1. In particular, we study the optimal production and inventory allocation policies of such systems. Components are produced on independent production facilities one unit at a time, each with a finite production rate and exponentially distributed production times. The components are stocked ahead of demand and therefore incur a holding cost per unit per unit of time. Demand from each product occurs continuously over time according to a Poisson process. The demand for a particular product can be either satisfied (provided all its components are available in stock) or rejected. In the latter case, a product-dependent lost sale cost is incurred. In this situation, a manager is confronted with two decisions: when to produce a component and whether or not to satisfy an incoming product order from on-hand inventory. We show that, for the production of a component, the optimal policy is a base-stock type where the base-stock level depends on all other components' inventory. We also show that, for inventory allocation, the optimal policy is a multi-level rationing policy where the rationing levels depend on all other components' inventory. We propose a simple heuristic that we numerically compare against the optimal policy and show that, when carefully designed, it can be very effective.
机译:在本文中,我们研究了一个由n个产品组成的按订单组装系统,该产品由m个不同组件的子集组装而成,这些产品具有模块化嵌套设计,即产品i仅比产品i-1多一个组件。特别是,我们研究了此类系统的最佳生产和库存分配策略。组件是在独立的生产设备上一次生产一个组件的,每个组件的生产率有限,生产时间呈指数分布。这些组件的库存要比需求提前,因此会导致每单位时间单位成本的保持。根据泊松过程,每种产品的需求随时间连续发生。可以满足特定产品的需求(只要其所有组件都有现货),也可以拒绝。在后一种情况下,将产生与产品有关的销售损失成本。在这种情况下,经理面临两个决定:何时生产组件以及是否满足从现有库存中收到的产品订单。我们表明,对于组件的生产,最佳策略是基本库存类型,其中基本库存水平取决于所有其他组件的库存。我们还表明,对于库存分配,最佳策略是多级配给策略,其中配给级别取决于所有其他组件的库存。我们提出了一种简单的启发式方法,我们将其与最优策略进行数值比较,并表明,经过精心设计,它可以非常有效。

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