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Robust spotter scheduling in trailer yards

Tadumadze, Giorgi ; Boysen, Nils ; Emde, Simon (2024)
Robust spotter scheduling in trailer yards.
In: OR Spectrum : Quantitative Approaches in Management, 2020, 42 (4)
doi: 10.26083/tuprints-00023872
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Robust spotter scheduling in trailer yards
Language: English
Date: 8 April 2024
Place of Publication: Darmstadt
Year of primary publication: December 2020
Place of primary publication: Berlin ; Heidelberg ; New York
Publisher: Springer
Journal or Publication Title: OR Spectrum : Quantitative Approaches in Management
Volume of the journal: 42
Issue Number: 4
DOI: 10.26083/tuprints-00023872
Corresponding Links:
Origin: Secondary publication DeepGreen

Spotters (also denoted as switchers) are specialized terminal tractors, which are dedicated to the rapid maneuvering of semitrailers between parking lot and dock doors in large trailer yards. This paper is dedicated to spotter scheduling, i.e., the assignment of predefined trailer movements to a given fleet of spotters. The limited number of dock doors for loading and unloading is often the scarce resource during trailer processing, so that idle time of the bottleneck, e.g., caused by unforeseen delay in the yard, is to be avoided. In this setting, we aim to insert time buffers between any pair of subsequent jobs assigned to the same spotter, so that small delays are not propagated and subsequent jobs can still be executed in a timely manner. We formalize two versions of the resulting robust spotter scheduling problem and provide efficient algorithms for finding optimal solutions in polynomial time. Furthermore, we simulate delays during the execution of spotter schedules and show that the right robustness objective can greatly improve yard performance.

Uncontrolled Keywords: Yard operations, Truck scheduling, Terminal tractor scheduling, Robustness
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-238721
Classification DDC: 600 Technology, medicine, applied sciences > 650 Management
Divisions: 01 Department of Law and Economics > Betriebswirtschaftliche Fachgebiete > Fachgebiet Management Science / Operations Research
Date Deposited: 08 Apr 2024 12:41
Last Modified: 10 Apr 2024 06:00
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23872
PPN: 51699851X
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