Two-stage no-wait scheduling models with setup and removal times separated

This paper studies two models of two-stage processing with no-wait in process. The first model is the two-machine flow shop, and the other is the assembly model. For both models we consider the problem of minimizing the makespan, provided that the setup and removal times are separated from the processing times. Each of these scheduling problems is reduced to the Traveling Salesman Problem (TSP). We show that, in general, the assembly problem is NP-hard in the strong sense. On the other hand, the two-machine flow shop problem reduces to the Gilmore-Gomory TSP, and is solvable in polynomial time. The same holds for the assembly problem under some reasonable assumptions. Using these and existing results, we provide a complete complexity classification of the relevant two-stage no-wait scheduling models.

Single machine scheduling models with deterioration and learning: handling precedence constraints via priority generation

We consider various single machine scheduling problems in which the processing time of a job depends either on its position in a processing sequence or on its start time. We focus on problems of minimizing the makespan or the sum of (weighted) completion times of the jobs. In many situations we show that the objective function is priority-generating, and therefore the corresponding scheduling problem under series-parallel precedence constraints is polynomially solvable. In other situations we provide counter-examples that show that the objective function is not priority-generating.