Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/128270
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Type: Conference paper
Title: Evolutionary bi-objective optimization for the dynamic chance-constrained knapsack problem based on tail bound objectives
Author: Assimi, H.
Harper, O.
Xie, Y.
Neumann, A.
Neumann, F.
Citation: Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), as published in Frontiers in Artificial Intelligence and Applications, 2020 / vol.325, pp.307-314
Publisher: IOS Press BV
Publisher Place: Amsterdam, Netherlands
Issue Date: 2020
Series/Report no.: Frontiers in Artificial Intelligence and Applications; 325
ISBN: 9781643681009
ISSN: 0922-6389
1879-8314
Conference Name: 24th European Conference on Artificial Intelligence (ECAI) (29 Aug 2020 - 08 Sep 2020 : Santiago de Compostela, Spain)
Statement of
Responsibility: 
Hirad Assimi, Oscar Harper, Yue Xie, Aneta Neumann and Frank Neumann
Abstract: Real-world combinatorial optimization problems are often stochastic and dynamic. Therefore, it is essential to make optimal and reliable decisions with a holistic approach. In this paper, we consider the dynamic chance-constrained knapsack problem where the weight of each item is stochastic, the capacity constraint changes dynamically over time, and the objective is to maximize the total profit subject to the probability that total weight exceeds the capacity. We make use of prominent tail inequalities such as Chebyshev’s inequality, and Chernoff bound to approximate the probabilistic constraint. Our key contribution is to introduce an additional objective which estimates the minimal capacity bound for a given stochastic solution that still meets the chance constraint. This objective helps to cater for dynamic changes to the stochastic problem. We apply single- and multi-objective evolutionary algorithms to the problem and show how bi-objective optimization can help to deal with dynamic chance-constrained problems.
Rights: © 2020 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
RMID: 1000020932
DOI: 10.3233/FAIA200107
Grant ID: http://purl.org/au-research/grants/arc/DP160102401
Published version: http://ebooks.iospress.nl/doi/10.3233/FAIA325
Appears in Collections:Computer Science publications

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