Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106121
Title: A near-optimal algorithm for constraint test ordering in automated stowage planning
Authors: Lee, Zhuo Qi
Fan, Rui
Hsu, Wen-Jing
Keywords: DRNTU::Engineering::Computer science and engineering
Automation
Logistics
Issue Date: 2018
Source: Lee, Z. Q., Fan, R., & Hsu, W.- J. (2018). A near-optimal algorithm for constraint test ordering in automated stowage planning. IEEE Transactions on Automation Science and Engineering, 15(3), 1298-1308. doi:10.1109/TASE.2017.2779470
Series/Report no.: IEEE Transactions on Automation Science and Engineering
Abstract: The container stowage planning problem is known to be NP-hard and heuristic algorithms have been proposed. Conventionally, the efficiency of the stowage planning algorithms are improved by pruning or reducing the search space. We observe that constraint evaluation is the core of most algorithms. In addition, the order at which the constraints are evaluated can have significant impact on the efficiency of the constraint evaluation engine. We propose random sample model (RSM) and sequential sample model (SSM) for analysis of the problem. We present and evaluate seven strategies in optimizing the constraint evaluation engine. We show how to achieve the optimal constraint ordering with respect to RSM and SSM, respectively. However, the optimal ordering for SSM requires perfect information about the states of the constraint tests, which is impractical. We present an alternative strategy and show empirically that its efficiency is close to the optimal. Experiments show that, compared to a naïve ordering, an average of 2.74 times speed up in the evaluation engine can be achieved.
URI: https://hdl.handle.net/10356/106121
http://hdl.handle.net/10220/47888
ISSN: 1545-5955
DOI: 10.1109/TASE.2017.2779470
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TASE.2017.2779470
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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