The research in the field of robust scheduling aims at devising schedules which are not sensitiveto a certain extentto the disruptive effects of unexpected events. Nevertheless, the protection of the schedule from rare events causing heavy losses is still a challenging aim. The paper presents a novel approach for protecting the quality of a schedule by assessing the risk associated to the different scheduling decisions. The approach is applied to a stochastic scheduling problem with a set of jobs to be sequenced on a single machine. The release dates and processing times of the jobs are generally distributed independent random variables, while the due dates are deterministic. A branch-and-bound approach is taken to minimise the value-at-risk of the distribution of the maximum lateness. The viability of the approach is demonstrated through a computational experiment and the application to an industrial problem in the tool making industry.

A branch-and-bound approach for the single machine maximum lateness stochastic scheduling problem to minimize the value-at-risk

Urgo, M.;
2019

Abstract

The research in the field of robust scheduling aims at devising schedules which are not sensitiveto a certain extentto the disruptive effects of unexpected events. Nevertheless, the protection of the schedule from rare events causing heavy losses is still a challenging aim. The paper presents a novel approach for protecting the quality of a schedule by assessing the risk associated to the different scheduling decisions. The approach is applied to a stochastic scheduling problem with a set of jobs to be sequenced on a single machine. The release dates and processing times of the jobs are generally distributed independent random variables, while the due dates are deterministic. A branch-and-bound approach is taken to minimise the value-at-risk of the distribution of the maximum lateness. The viability of the approach is demonstrated through a computational experiment and the application to an industrial problem in the tool making industry.
Stochastic scheduling; Robust scheduling; Risk; Value-at-risk
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1056573
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