Scheduling Jobs on Unreliable Machines Subject to Linear Risk

Background: This paper addresses a new class of scheduling problems in the context of machines subject to (unrecoverable) interruptions; i.e., when a machine fails, the current and subsequently scheduled work on that machine is lost. Each job has a certain processing time and a reward that is attained if the job is successfully completed. Methods: For the failure process, we considered the linear risk model, according to which the probability of machine failure is uniform across a certain time horizon. Results: We analyzed both the situation in which the set of jobs is given, and that in which jobs must be selected from a pool of jobs, at a certain selection cost. Conclusions: We characterized the complexity of various problems, showing both hardness results and polynomial algorithms, and pointed out some open problems.

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Publication Details

Journal
Logistics
Published
2025-11-04
DOI
https://doi.org/10.3390/logistics9040157
Primary Topic
Optimization and Search Problems
Type
article
Field-Weighted Citation Impact
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article

Scheduling Jobs on Unreliable Machines Subject to Linear Risk

Alessandro Agnetis, Ilaria Salvadori
Logistics
Optimization and Search Problems
article

Scheduling Jobs on Unreliable Machines Subject to Linear Risk

Alessandro Agnetis, Ilaria Salvadori
article en

Abstract

Background: This paper addresses a new class of scheduling problems in the context of machines subject to (unrecoverable) interruptions; i.e., when a machine fails, the current and subsequently scheduled work on that machine is lost. Each job has a certain processing time and a reward that is attained if the job is successfully completed. Methods: For the failure process, we considered the linear risk model, according to which the probability of machine failure is uniform across a certain time horizon. Results: We analyzed both the situation in which the set of jobs is given, and that in which jobs must be selected from a pool of jobs, at a certain selection cost. Conclusions: We characterized the complexity of various problems, showing both hardness results and polynomial algorithms, and pointed out some open problems.

LogisticsVol. 9(4)
University of Siena (IT)
Openalex Percentile: Top 62%
Optimization and Search Problems
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Scheduling Jobs on Unreliable Machines Subject to Linear Risk — Alessandro Agnetis, Ilaria Salvadori · Logistics (2025) | TGRS Research Map | TGRS