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.
Agnetis, A., Salvadori, I. (2025). Scheduling Jobs on Unreliable Machines Subject to Linear Risk. LOGISTICS, 9(4) [10.3390/logistics9040157].
Scheduling Jobs on Unreliable Machines Subject to Linear Risk
Agnetis, Alessandro;Salvadori, Ilaria
2025-01-01
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.| File | Dimensione | Formato | |
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https://hdl.handle.net/11365/1303535
