A data-driven methodology for the automated configuration of online algorithms

Dunke, Fabian; Nickel, Stefan

doi:10.1016/j.dss.2020.113343

Cited by 9 publications

(5 citation statements)

References 41 publications

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“…The authors also employ numerical experiments on lot sizing with gradual lookahead to examine the value of better data accuracy, more frequent input data updates, longer lookahead horizon, and solving snapshot problems to optimality rather than heuristically. Finally, Dunke and Nickel (2020) deal with the automated generation of rule‐based online algorithms. For the lot sizing application, it is shown that (meta‐) heuristically generated data‐driven rules are in a position to yield competitive results.…”

Section: Related Workmentioning

confidence: 99%

Comparison of different approaches to multistage lot sizing with uncertain demand

Bindewald

Dunke

Nickel

2023

Int Trans Operational Res

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We study a new variant of the classical lot sizing problem with uncertain demand where neither the planning horizon nor demands are known exactly. This situation arises in practice when customer demands arriving over time are confirmed rather lately during the transportation process. In terms of planning, this setting necessitates a rolling horizon procedure where the overall multistage problem is dissolved into a series of coupled snapshot problems under uncertainty. Depending on the available data and risk disposition, different approaches from online optimization, stochastic programming, and robust optimization are viable to model and solve the snapshot problems. We evaluate the impact of the selected methodology on the overall solution quality using a methodology‐agnostic framework for multistage decision‐making under uncertainty. We provide computational results on lot sizing within a rolling horizon regarding different types of uncertainty, solution approaches, and the value of available information about upcoming demands.

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Section: Related Workmentioning

confidence: 99%

Comparison of different approaches to multistage lot sizing with uncertain demand

Bindewald

Dunke

Nickel

2023

Int Trans Operational Res

Self Cite

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“…An adaptive rule-based algorithm was developed in [13]. Instead of using a value to choose different heuristics, it automatically configured the behavior of an algorithm by computing a threshold value based on available data elements.…”

Section: Literature Review Bin-packing Algorithmsmentioning

confidence: 99%

Identify Patterns in Online Bin Packing Problem: An Adaptive Pattern-Based Algorithm

Lin

Bai

et al. 2022

Symmetry

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Bin packing is a typical optimization problem with many real-world application scenarios. In the online bin packing problem, a sequence of items is revealed one at a time, and each item must be packed into a bin immediately after its arrival. Inspired by duality in optimization, we proposed pattern-based adaptive heuristics for the online bin packing problem. The idea is to predict the distribution of items based on packed items, and to apply this information in packing future arrival items in order to handle uncertainty in online bin packing. A pattern in bin packing is a combination of items that can be packed into a single bin. Patterns selected according to past items are adopted and periodically updated in scheduling future items in the algorithm. Symmetry in patterns and the stability of patterns in the online bin packing problem are discussed. We have implemented the algorithm and compared it with the Best-Fit in a series of experiments with various distribution of items to show its effectiveness.

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“…These systems are actively used for education, e-learning, computing, programming competitions, and software engineering. The importance of empirical data-driven analysis to make critical decisions, and even to change algorithm configurations automatically, is growing [51]. However, this data-driven analytical research differs from previous research in that a real-world dataset has been used.…”

Section: Educational Data Mining and Learning Analyticsmentioning

confidence: 99%

Impact of Practical Skills on Academic Performance: A Data-Driven Analysis

Rahman¹,

Watanobe

Kiran

et al. 2021

IEEE Access

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Most academic courses in information and communication technology (ICT) or engineering disciplines are designed to improve practical skills; however, practical skills and theoretical knowledge are equally important to achieve high academic performance. This research aims to explore how practical skills are influential in improving students' academic performance by collecting real-world data from a computer programming course in the ICT discipline. Today, computer programming has become an indispensable skill for its wide range of applications and significance across the world. In this paper, a novel framework to extract hidden features and related association rules using a real-world dataset is proposed. An unsupervised k-means clustering algorithm is applied for data clustering, and then the frequent pattern-growth algorithm is used for association rule mining. We leverage students' programming logs and academic scores as an experimental dataset. The programming logs are collected from an online judge (OJ) system, as OJs play a key role in conducting programming practices, competitions, assignments, and tests. To explore the correlation between practical (e.g., programming, logical implementations, etc.) skills and overall academic performance, the statistical features of students are analyzed and the related results are presented. A number of useful recommendations are provided for students in each cluster based on the identified hidden features. In addition, the analytical results of this paper can help teachers prepare effective lesson plans, evaluate programs with special arrangements, and identify the academic weaknesses of students. Moreover, a prototype of the proposed approach and data-driven analytical results can be applied to other practical courses in ICT or engineering disciplines.

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A data-driven methodology for the automated configuration of online algorithms

Cited by 9 publications

References 41 publications

Comparison of different approaches to multistage lot sizing with uncertain demand

Comparison of different approaches to multistage lot sizing with uncertain demand

Identify Patterns in Online Bin Packing Problem: An Adaptive Pattern-Based Algorithm

Impact of Practical Skills on Academic Performance: A Data-Driven Analysis

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