In classical deterministic scheduling models, it is common to assume that each of the
parameters has only a single predefined value. However, in many applications this is not the …

Robust optimization is a young and emerging field of research having received a
considerable increase of interest over the last decade. In this paper, we argue that the …

Robust optimization (RO) has been recognized as an effective means to deal with
unanticipated events in highly uncertain and risky environments. This paper systematically …

We investigate the recoverable robust single machine scheduling problem under interval
uncertainty. In this setting, jobs have first-stage processing times p and second-stage …

We study robust single-machine batch scheduling problems under uncertain processing
times to minimize total flow time. Two types of batches are considered: serial batch (s-batch) …

This paper presents a distributionally robust (DR) optimization model for the single machine
scheduling problem (SMSP) with random job processing time (JPT). To the best of our …

This paper investigates a distributionally robust scheduling problem on identical parallel
machines, where job processing times are stochastic without any exact distributional form …

This paper proposes a distributionally robust optimization (DRO) model for single machine
scheduling with uncertain processing times. The processing time of each job is assumed to …

In this paper, we study a robust single-machine scheduling problem under four alternative
optimization criteria: minimizing total completion time, minimizing total weighted completion …

This work proposes exact solution approaches for the m-machine robust permutation flow
shop problem, where operation processing times are uncertain and vary in a given interval …