Models and algorithms for a partition problem arising in warehousing
Publication date
2017
Document type
Meeting Abstract
Author
Organisational unit
Universität Siegen
Conference
13th Workshop on Models and Algorithms for Planning and Scheduling Problems (MAPSP 2017) ; Seeon Abbey, Germany ; June 12–16, 2017
Publisher
Universität Bremen
Book title
Proceedings of the 13th Workshop on Models and Algorithms for Planning and Scheduling Problems
First page
86
Last page
88
Peer-reviewed
✅
Part of the university bibliography
Nein
Language
English
Abstract
The storage assignment problem is among the most essential decision problems to be solved in any warehouse. Each stock keeping unit (SKU) is to be assigned a storage position from where it is to be retrieved during order picking. In many warehouses, the detailed slotting, i.e., the decision on the specific shelf each SKU is stored in, is less of an issue, but the problem rather reduces to a partition problem; SKUs are to be jointly stored in one area or group, so that picking-orders can efficiently and conveniently be retrieved without having to access too many groups. In this context, we treat the following basic partition problem, which we denote as the SKU partition problem (see our paper [4], which is the foundation of this extended abstract): Consider a given set of SKUs, which are to be partitioned into groups of equal size, and a deterministic set of (weighted) picking-orders each defining a subset of SKUs demanded by an order’s
customer. Depending on the partitioning of items, orders require different numbers of groups to be accessed during order picking. We refer to these numbers as the orders’ group numbers. Our objective is to find a partitioning of SKUs that minimizes the weighted sum of group numbers over all picking-orders.
customer. Depending on the partitioning of items, orders require different numbers of groups to be accessed during order picking. We refer to these numbers as the orders’ group numbers. Our objective is to find a partitioning of SKUs that minimizes the weighted sum of group numbers over all picking-orders.
Version
Published version
Access right on openHSU
Metadata only access