.png){kind=icon}
.png){kind=icon}
Return
Robust optimization for premarshalling with uncertain priority classes
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2020.04.049.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we consider the premarshalling problem, where items in a storage area have to be sorted for convenient retrieval. A new model for uncertainty is introduced, where the priority values induced by the retrieval sequence of the items are uncertain. We develop a robust optimization approach for this setting, study complexity issues, and provide different mixed-integer programming formulations. In a computational study using a wide range of benchmark instances from the literature, we investigate both the efficiency of the approach as well as the benefit and cost of robust solutions. We find that it is possible to achieve a considerably improved level of robustness by using just a few additional relocations in comparison to solutions which do not take uncertainty into account. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Logistics
Premarshalling
Robust optimization
Storage
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W