On the online track assignment problem

Demange, Marc; Stefano, Gabriele Di; Leroy-Beaulieu, Benjamin

doi:10.1016/j.dam.2012.01.002

Cited by 13 publications

(10 citation statements)

References 19 publications

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“…Tierney et al [14] show that it is possible to decide in polynomial time whether the items can be stacked using a fixed number of bounded capacity stacks but the running time of their offline algorithm is very high even if the fixed number of stacks is small. For the online case, the competitive ratio is unbounded for any online algorithm for unbounded stack capacity as shown by Demange et al [5].…”

Section: Related Workmentioning

confidence: 99%

“…Demange et al [5] develop lower and upper bounds for the competitive ratio for online stacking algorithms in the context of assigning trains to tracks at a train station and Demange and Olsen [4] present some improvements both for the offline and online case. Simple heuristics for online stacking are presented by Borgman et al [2], Duinkerken et al [6], Hamdi et al [7], and Wang et al [15].…”

Section: Related Workmentioning

confidence: 99%

“…The challenge of stacking items temporarily in a storage area in an optimal manner is a problem that has many applications within logistics. Some notable examples of items to consider are containers in a container terminal or on a container ship [2], trains at a train station [3,5], or steel bars [12].…”

Section: Introductionmentioning

confidence: 99%

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Online Stacking Using RL with Positional and Tactical Features

Olsen

2020

Lecture Notes in Computer Science

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We study the scenario where some items are stored temporarily in stacks and where it is not allowed to put an item on top of another item leaving earlier. An arriving item is assigned to a stack based only on information on the arrival and departure times for the new item and items currently stored. The objective is to minimize the maximum number of stacks used over time. This problem is referred to as online stacking. We use Reinforcement Learning (RL) techniques to improve heuristics earlier presented in the literature. Using an analogy to chess, we look at positional and tactical features where the former give high priority to stacking configurations that are well suited to meet the challenges on a long-term basis and the latter focus on using few stacks on a short-term basis. We show how the RL approach finds the optimal mix of positional and tactical features to be used at different stages of the stacking process. We document quantitatively that positional features play a bigger role at stages of the stacking process with few items stored. We believe that the RL approach combining positional and tactical features can be used in many other online settings within operations research.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Online Stacking Using RL with Positional and Tactical Features

Olsen

2020

Lecture Notes in Computer Science

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“…Cornelsen and Di Stefano [5] and Demange et al [7] consider the problem in the context of assigning tracks to trains arriving at a train station/depot. Cornelsen and Di Stefano look at unbounded capacity stacks (train tracks) whereas Demange et al consider unbounded as well as bounded capacity stacks.…”

Section: Related Workmentioning

confidence: 99%

“…As an example, the items could be containers, and the storage location could be a container terminal or a container ship [4]. The items could also be steel bars [16] and trains [7], or the storage location could simply be a warehouse storing any type of objects stacked on top of each other.…”

Section: Introductionmentioning

confidence: 99%