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首页> 外文期刊>European Journal of Operational Research >Order batching in an automated warehouse with several vertical lift modules: Optimization and experiments with real data
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Order batching in an automated warehouse with several vertical lift modules: Optimization and experiments with real data

机译:使用多个垂直升降机模块的自动仓库中的订单批量:实际数据优化和实验

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摘要

The vertical lift module is an automated storage and retrieval system widely used in warehouses. The performance of a warehouse with vertical lift modules is highly correlated with the efficiency of the order picking. Order batching, namely regrouping customers' orders into batches to be collected from the module, constitutes a critical decision impacting the picking efficiency. In this paper, we provide optimization models for order batching, with the objective of minimizing total completion time, that is, the time required to collect a given set of customers' orders. We first consider the case of one vertical lift module and then extend our approach to study a warehouse with several modules. We use real data from two companies operating in different sectors in order to test and validate our models. Numerical experiments show that our models perform much better than the batching method currently used by these companies. For complex cases that cannot be solved within a reasonable timeframe with Cplex, we develop a metaheuristic approach, which generally yields very good solutions in less than one minute. This paper investigates problems that are firmly grounded in practice. Our batching models and metaheuristic approach have been implemented in practice and are currently used by some companies. (C) 2018 Elsevier B.V. All rights reserved.
机译:垂直提升模块是广泛应用于仓库的自动存储和检索系统。具有垂直升降模块的仓库的性能与订单拣选的效率高度相关。订单批处理,即重新组合客户的订单批次从模块收集,构成影响拣选效率的关键决策。在本文中,我们为订单批处理提供优化模型,目的是最大限度地降低总完成时间,即收集一套客户订单所需的时间。我们首先考虑一个垂直提升模块的情况,然后扩展我们使用多个模块的仓库的方法。我们使用在不同扇区运营的两家公司的真实数据,以便测试和验证我们的模型。数值实验表明,我们的模型比这些公司目前使用的批次方法更好地表现得多。对于在与CPLEX中合理的时间范围内无法解决的复杂案例,我们开发了一种成群质方法,通常在不到一分钟内产生非常好的解决方案。本文调查了在实践中坚定地接地的问题。我们的批处理模型和成群质方法已经在实践中实施,目前由某些公司使用。 (c)2018年elestvier b.v.保留所有权利。

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