Fixed versus variable time window warehousing strategies in real time
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Fixed versus variable time window warehousing strategies in real time Sergio Gil-Borrás1
· Eduardo G. Pardo2
· Antonio Alonso-Ayuso2
· Abraham Duarte2
Received: 8 May 2020 / Accepted: 31 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Warehousing includes many different regular activities such as receiving, batching, picking, packaging, and shipping goods. Several authors indicate that the picking operation might consume up to 55% of the total operational costs. In this paper, we deal with a subtask arising within the picking task in a warehouse, when the picking policy follows the order batching strategy (i.e., orders are grouped into batches before being collected) and orders are received online. Particularly, once the batches have been compiled it is necessary to determine the moment in the time when the picker starts collecting each batch. The waiting time of the picker before starting to collect the next available batch is usually known as time window. In this paper, we compare the performance of two different time window strategies: Fixed Time Window and Variable Time Window. Since those strategies cannot be tested in isolation, we have considered: two different batching algorithms (First Come First Served and a Greedy algorithm based on weight); one routing algorithm (S-Shape); and a greedy selection algorithm for choosing the next batch to collect based on the weight. Keywords Time window · Fixed time window · Variable time window · Online order batching · Warehousing
1 Introduction The picking operation in a warehouse consist of collecting all the items, demanded by the customers, which are stored in the warehouse. This operation might consume up to 55% of the total operational costs of the warehouse [34]. It is well documented that the picking is highly influenced by the layout This research was partially funded by the projects: RTI2018-094269-B-I00 and PGC2018-095322-B-C22 from Ministerio de Ciencia, Innovación y Universidades (Spain); by Comunidad de Madrid and European Regional Development Fund, Grant Ref. P2018/TCS-4566; and by Programa Propio de I+D+i de la Universidad Politécnica de Madrid (Programa 466A).
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Eduardo G. Pardo [email protected] Sergio Gil-Borrás [email protected] Antonio Alonso-Ayuso [email protected] Abraham Duarte [email protected]
1
Departamento de Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, Spain
2
Department of Computer Sciences, Universidad Rey Juan Carlos, Móstoles, Spain
of the warehouse, the storage policy, and the routing strategy followed by the pickers [27]. The picking operation can be divided into two main groups: strict order picking (each order is collected individually) and order batching (orders are grouped into batches before being collected). In this paper, we focus our attention on the picking policy that follows an order batching strategy. When using this strategy, a family of related optimization problems (order batching problems) emerges. Order batching problems usua
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