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Title

Adaptive Scheduling with Learned Disruption Models: A Sample Manuscript

Abstract

This sample abstract shows how your text stays in clean, editable fields. We describe an adaptive scheduler and report a 12% shorter makespan on three benchmarks.

Keywords

schedulingmachine learningsample keyword

Authors

addAdd author

chevron_right Author 1Alex Morgan

Name

Alex Morgan

alex.morgan@example.edu

Affiliations

1 · Example University

Corresponding author

chevron_right Author 2Jamie Lee

7 headings · click a heading to edit, drag to reorder addAdd section

chevron_right 1Introduction

Factories must reschedule jobs the moment a machine fails. Learned schedulers adapt well morgan2021, but rare disruptions leave them little to learn from. Figure fig:1 shows how we generate those cases instead.

chevron_right 1.1Contributions48 words

chevron_right 2Related work212 words

chevron_right 3Method1 subsection

chevron_right 3.1Disruption generator96 words

chevron_right 4Results305 words

chevron_right 5Conclusion88 words

1 figure · place it in your text with Fig on the Content tab addAdd figure

Label

fig:1

Placement

Top [t]expand_more

Width

Full line widthexpand_more

pipeline.pdf · uploaded

Caption

Proposed pipeline: generated disruptions train the scheduling agent.

1 table · paste straight from Excel or Sheets addAdd table

Label

tab:1

Caption

Aboveexpand_more

Style

table_rowsBooktabs

Method	FT10	LA16	TA21
Dispatching rules	1,123	1,001	1,842
Learned baseline	1,012	958	1,730
Ours	937	946	1,622

Caption

Makespan by method (lower is better).

1 equation · write LaTeX, or build it visually addAdd equation

eq:1 Numbered calculateEquation builder

LaTeX source

Q(s_t,a_t) = r_t
  + \gamma \max_{a} Q(s_{t+1},a)

Live preview

Q(s_t, a_t) = r_t + γ max_a Q(s_t+1, a)(1)

Only cited entries are printed. check_circle complete · cancel missing a title upload_fileImport .bib addAdd reference

check_circlemorgan2021Learning to schedule under uncertainty · Morgan and Patel, 2021Cited

check_circlelee2019A short survey of dispatching rules · Lee, 2019Cited

check_circlechen2022Synthetic data for rare events · Chen et al., 2022Cited

cancelrivera2020(no title yet) · Rivera, 2020Not cited

Author contributions

A.M.: conceptualization, software. J.L.: validation, writing.

fact_checkBuild this statement from CRediT roles

Funding

This sample work was supported by the Example Research Fund under grant 000000.

Conflicts of interest · nothing to print

Success picture_as_pdfPDF folder_zipZIP descriptionWord

Manual LaTeX Editor (main.tex)

1\documentclass[journal]{IEEEtran}
2\usepackage{graphicx,booktabs,amsmath}
3\begin{document}
4\title{Adaptive Scheduling with Learned
   Disruption Models: A Sample Manuscript}
5\author{Alex~Morgan and Jamie~Lee}
6\maketitle
7\begin{abstract}
8This sample abstract shows how your text
9stays in clean, editable fields. ...
10\end{abstract}
11\section{Introduction}
12Factories must reschedule jobs the moment a
13machine fails. Learned schedulers adapt well
14\cite{morgan2021}, but rare disruptions ...
15Fig.~\ref{fig:1} shows how we generate them.
16\begin{figure}[t]
17  \centering
18  \includegraphics[width=\linewidth]{pipeline}
19  \caption{Proposed pipeline: generated
     disruptions train the scheduling agent.}
20  \label{fig:1}
21\end{figure}
22\section{Related Work}
23Classical dispatching rules are fast but
24short-sighted \cite{lee2019}. ...
25\section{Method}
26\begin{equation}
27  Q(s_t,a_t) = r_t + \gamma \max_{a} Q(s_{t+1},a)
28  \label{eq:1}
29\end{equation}
30\subsection{Disruption Generator}
31... to train against \cite{chen2022}.
32\bibliographystyle{IEEEtran}
33\bibliography{references}
34\end{document}

Adaptive Scheduling with Learned Disruption Models: A Sample Manuscript

Alex Morgan and Jamie Lee
Example University

Abstract: This sample abstract shows how your text stays in clean, editable fields. We describe an adaptive scheduler and report a 12% shorter makespan on three benchmarks.

Index Terms: scheduling, machine learning, sample keyword.

I. Introduction

Factories must reschedule jobs the moment a machine fails. Learned schedulers adapt well [1], but rare disruptions leave them little to learn from. Fig. 1 shows how we generate those cases.

Fig. 1. Proposed pipeline: generated disruptions train the scheduling agent.

II. Related Work

Classical dispatching rules are fast but short-sighted [2]. Learned schedulers close that gap.

III. Method

The agent observes machine states and picks the next job, trained toward the target in (1).

Q(s_t, a_t) = r_t + γ max_a Q(s_t+1, a)(1)

A. Disruption Generator

A generator proposes realistic breakdown sequences for the agent to train against [3].

TABLE I
Makespan by Method (Lower Is Better)

Method	FT10	LA16	TA21
Dispatching rules	1,123	1,001	1,842
Learned baseline	1,012	958	1,730
Ours	937	946	1,622

IV. Results

Table I compares makespan across three benchmarks.

V. Conclusion

Generated disruptions make learned schedulers robust where real data is scarce.

Author Contributions

A.M.: conceptualization, software. J.L.: validation, writing.

Funding

This sample work was supported by the Example Research Fund under grant 000000.

References

A. Morgan and R. Patel, “Learning to schedule under uncertainty,” Sample J. Mach. Learn., vol. 1, pp. 1–10, 2021.
J. Lee, “A short survey of dispatching rules,” Example Trans. Oper., vol. 2, pp. 11–20, 2019.
W. Chen et al., “Synthetic data for rare events,” in Proc. Sample Conf., 2022, pp. 21–30.

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Adaptive Scheduling with Learned Disruption Models: A Sample Manuscript

Alex Morgan and Jamie Lee
Example University

Abstract: This sample abstract shows how your text stays in clean, editable fields. We describe an adaptive scheduler and report a 12% shorter makespan on three benchmarks.

Index Terms: scheduling, machine learning, sample keyword.

I. Introduction

Factories must reschedule jobs the moment a machine fails. Learned schedulers adapt well [1], but rare disruptions leave them little to learn from. Fig. 1 shows how we generate those cases.

Fig. 1. Proposed pipeline: generated disruptions train the scheduling agent.

II. Related Work

Classical dispatching rules are fast but short-sighted [2]. Learned schedulers close that gap.

III. Method

The agent observes machine states and picks the next job, trained toward the target in (1).

Q(s_t, a_t) = r_t + γ max_a Q(s_t+1, a)(1)

A. Disruption Generator

A generator proposes realistic breakdown sequences for the agent to train against [3].

TABLE I
Makespan by Method (Lower Is Better)

Method	FT10	LA16	TA21
Dispatching rules	1,123	1,001	1,842
Learned baseline	1,012	958	1,730
Ours	937	946	1,622

IV. Results

Table I compares makespan across three benchmarks.

V. Conclusion

Generated disruptions make learned schedulers robust where real data is scarce.

Author Contributions

A.M.: conceptualization, software. J.L.: validation, writing.

Funding

This sample work was supported by the Example Research Fund under grant 000000.

References

A. Morgan and R. Patel, “Learning to schedule under uncertainty,” Sample J. Mach. Learn., vol. 1, pp. 1–10, 2021.
J. Lee, “A short survey of dispatching rules,” Example Trans. Oper., vol. 2, pp. 11–20, 2019.
W. Chen et al., “Synthetic data for rare events,” in Proc. Sample Conf., 2022, pp. 21–30.

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