存储位置分配和拣选路径问题:一种通用的分支-切割-加价算法

Thibault Prunet, Nabil Absi, Diego Cattaruzza
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引用次数: 0

摘要

存储位置分配问题(SLAP)和拣选路径问题(PRP)在仓储物流中最耗费资源的订单拣选(OP)活动中起着举足轻重的作用,因此在文献中备受关注。传统上,这两个问题是在不同的决策层面上考虑的:SLAP 是战术问题,而 PRP 则是操作问题。然而,现代电子商务仓库的出现对这一范式提出了挑战,因为现代电子商务仓库的存储决策更具动态性,而且是在操作层面上做出的,这就使得 SLAP 和 PRP 的整合成为一个值得考虑的问题。尽管具有重要的实际意义,但这两种操作的联合优化问题,即存储位置分配和拣货路径问题(SLAPRP),受到的关注却很有限。学者们研究了 SLAPRP 的几种变体,包括不同的仓库布局和路由策略。然而,现有的计算结果表明,每种变体都需要一种特别的表述方式。此外,实现这两个问题的完全整合,即最优化地解决路由问题,对于商业求解器来说仍然是遥不可及的。在本文中,我们提出了一个精确求解框架,它可以解决 SLAPRP 的多种变体,包括之前存在的所有变体。本文提出的 "分部-切分-加价 "框架基于一种具有指数级变量数的新式表述,并通过一系列新式非稳健有效不等式对其进行了强化。我们开发了一种临时分支方案,以打破对称性并保持枚举树的大小可控。计算实验表明,我们的框架可以有效地解决多个 SLAPRP 变体的中等规模实例,并且优于文献中的先进方法。
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The Storage Location Assignment and Picker Routing Problem: A Generic Branch-Cut-and-Price Algorithm
The Storage Location Assignment Problem (SLAP) and the Picker Routing Problem (PRP) have received significant attention in the literature due to their pivotal role in the performance of the Order Picking (OP) activity, the most resource-intensive process of warehousing logistics. The two problems are traditionally considered at different decision-making levels: tactical for the SLAP, and operational for the PRP. However, this paradigm has been challenged by the emergence of modern practices in e-commerce warehouses, where storage decisions are more dynamic and are made at an operational level, making the integration of the SLAP and PRP pertinent to consider. Despite its practical significance, the joint optimization of both operations, called the Storage Location Assignment and Picker Routing Problem (SLAPRP), has received limited attention. Scholars have investigated several variants of the SLAPRP, including different warehouse layouts and routing policies. Nevertheless, the available computational results suggest that each variant requires an ad hoc formulation. Moreover, achieving a complete integration of the two problems, where the routing is solved optimally, remains out of reach for commercial solvers. In this paper, we propose an exact solution framework that addresses a broad class of variants of the SLAPRP, including all the previously existing ones. This paper proposes a Branch-Cut-and-Price framework based on a novel formulation with an exponential number of variables, which is strengthened with a novel family of non-robust valid inequalities. We have developed an ad-hoc branching scheme to break symmetries and maintain the size of the enumeration tree manageable. Computational experiments show that our framework can effectively solve medium-sized instances of several SLAPRP variants and outperforms the state-of-the-art methods from the literature.
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