Optimization Strategies for In-Store Order Picking in Omnichannel Retailing

被引：5

作者：

Chou, Xiaochen ^{[1
,2
]}

Pietri, Nicola Ognibene ^{[2
]}

Loske, Dominic ^{[3
,5
]}

Klumpp, Matthias ^{[4
,5
,6
]}

Montemanni, Roberto ^{[2
]}

机构：

[1] Dalle Molle Inst Artificial Intelligence, Lugano, Switzerland

[2] Univ Modena & Reggio Emilia, Modena, Italy

[3] Univ Murcia, Murcia, Spain

[4] Georg August Univ Gottingen, Gottingen, Germany

[5] FOM Univ Appl Sci, Essen, Germany

[6] Fraunhofer IML Dortmund, Dortmund, Germany

来源：

ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT II | 2021年 / 631卷

关键词：

In-store order picking; Omnichannel Grocery Retailing; Sequential ordering problem;

D O I：

10.1007/978-3-030-85902-2_64

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The COVID-19 pandemic is changing consumer behavior and accelerating the interest for online grocery purchases. Hence, traditional brick-and-mortar retailers are developing omnichannel solutions enabling online purchases in parallel to normal activities. Buy-OnlinePick-up-in-Store concepts are flourishing in this context, and they are the topic of this work. In this paper we propose a novel application of the sequential ordering problem to model products picking throughout the store shelves. The result is an optimized picking sequence that however takes also into account the characteristics of the goods (fragility, weight, etc.). The aim is to preserve goods integrity while allowing the pickers to optimize their route through the shop. The approach is exemplified on historical online orders of a real German shop.

引用

页码：603 / 611

页数：9

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