Stacking under uncertainty: We know how to predict, but how should we act?

Hado van Hasselt, J.A. La Poutré

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

We consider the problem of stacking containers in a given set of stacks of fixed maximum capacity when the pick-up times are stochastic with unknown probability distributions. The goal is to minimize the expected number of times a container is picked up while it is not at the top of its stack. We formulate several algorithms under varying assumptions about the available knowledge about the pick-up-time distributions. We distinguish four qualitatively different settings: 1) we know nothing about the actual distributions, 2) we have point estimates of the means, 3) we have point estimates of means and variances, or 4) we have historical samples of actual pick-up times. For each assumption we propose one or more algorithms. We test the algorithms empirically in many different scenarios, considering both sequential and concurrent arrivals. Additionally, we consider the computational complexity and ease of use of each algorithm.
Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE Symposium on Computational Intelligence in Production and Logistics Systems
EditorsPN Suganthan, D Fogel
Place of PublicationPiscataway, NJ
PublisherIEEE Society
Pages25-32
Number of pages8
ISBN (Electronic)978-1-4673-5905-4
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventIEEE CIPLS, Singapore, Singapore - Piscataway, NJ, USA
Duration: 16 Apr 201319 Apr 2013

Publication series

Name
PublisherIEEE

Conference

ConferenceIEEE CIPLS, Singapore, Singapore
Period16/04/1319/04/13

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  • Cite this

    van Hasselt, H., & La Poutré, J. A. (2013). Stacking under uncertainty: We know how to predict, but how should we act? In PN. Suganthan, & D. Fogel (Eds.), Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (pp. 25-32). IEEE Society. https://doi.org/10.1109/CIPLS.2013.6595196