JUST-IN-TIME SCHEDULING UNDER SCENARIO-BASED UNCERTAINTY

This paper considers the single-machine scheduling problem, where job parameters are uncertain and the performance measure is to maximize the weighted number of just-in-time jobs, defined as jobs completed exactly on their due dates. Uncertainty is described through a finite set of well-defined scenarios. The criteria for this environment is to minimize the maximum deviation from optimality for all scenarios. We present the computational complexity results for several cases.