Abstract

This article intends to provide a computational tool that integrates and provides optimized solutions to two interdependent problems called Optimized Billing Sequencing (OBS) and Optimized Picking Sequence (OPS). These problems are addressed separately by the existing literature and refer respectively to the optimization of billing and picking processes in a typical warehouse with low-level picker-to-parts system. Integration literature is, therefore, limited and there is a demand for more robust OBS/OPS optimization methods. This approach will deal with practical dilemmas that have not been addressed by researchers yet to propose an extension to the OBS model by Pinto et al. (J Intell Manuf 29(2):405–422, 2018) along with a specific variation of the Order Batching and Sequencing Problem. The premise is to prove to managers the possibility of making more consistent decisions about the trade-off between the level of customer service and the warehouse efficiency. The proposed tool is formulated by the integration of two Genetic Algorithms called GA-OBS and GA-OPS where GA-OBS maximizes the order portfolio billing and generates the picking order to the OPS, whereas GA-OPS comprises the iteration of batch and routing algorithms to minimize picking total time and cost to the OPS. Experiments with problems with different complexity levels showed that the proposed tool produces solutions of satisfactory quality to OBS/OPS. The approach proposed fills a gap in the literature and makes innovative contributions to the development of more suitable optimization methods to the reality of warehouses.

中文翻译：

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遗传算法应用于记账和领料流程的集成和优化

摘要

本文旨在提供一种计算工具，该工具可以集成和提供针对两个相互依赖的问题的优化解决方案，这两个问题称为“优化帐单排序”（OBS）和“优化拣选序列”（OPS）。这些问题由现有文献分别解决，分别涉及在具有低级别拣货员到零件系统的典型仓库中对开票和拣货流程的优化。因此，集成文献有限，并且需要更强大的OBS / OPS优化方法。这种方法将解决实际的难题，研究人员尚未解决这些难题，Pinto等人尚未提出对OBS模型的扩展。（j INTELL MANUF 29（2）：405-422，2018）以及“订单批处理和排序问题”的特定变体。前提是向管理者证明在客户服务水平和仓库效率之间进行权衡的更一致决定的可能性。拟议的工具由两个遗传算法GA-OBS和GA-OPS集成而成，其中GA-OBS最大化了订单组合的账单并生成了OPS的拣配订单，而GA-OPS则包含了批处理和工艺路线算法的迭代以最大程度减少OPS的采摘总时间和成本。对具有不同复杂度级别的问题进行的实验表明，该工具可为OBS / OPS提供令人满意的质量的解决方案。