A new model for single machine scheduling with uncertain processing time

Uncertain single machine scheduling problem for batches of jobs is an important issue for manufacturing systems. in this paper, we use uncertainty theory to study the single machine scheduling problem with deadlines where the processing times are described by uncertain variables with known uncertainty distributions. a new model for this problem is built to maximize expected total weight of batches of jobs. then the model is transformed into a deterministic integer programming model by using the operational law for inverse uncertainty distributions. in addition, a property of the transformed model is provided and an algorithm is designed to solve this problem. finally, a numerical example is given to illustrate the effectiveness of the model and the proposed algorithm.