In the context of aircraft assembly lines, increasing the production rate and decreasing the operating costs are two important, and sometimes contradictory, objectives. In small assembly lines, sharing production resources across workstations is a simple and efficient way to reduce operating costs. Therefore, workers are not assigned to a unique workstation but can walk between them. On the other side, paralleling workstations is an efficient way to increase the production rate. However, the combination of both strategies create complex conditions for tasks to access the production resources. This paper addresses the problem of allocating tasks to workstations and scheduling them in an assembly line where workers can freely walk across workstations, and where workstations can be organized in parallel. We model this novel problem with Constraint Programming. We evaluate it on real world industrial use cases coming from aircraft manufacturers, as well as synthetic use cases adapted from the literature.

State tracking, i.e. estimating the state over time, is always an important problem in autonomous dynamic systems. Run-time requirements advocate for incremental estimation and memory limitations lead us to consider an estimation strategy that retains only one state out of the set of candidate estimates at each time step. This avoids the ambiguity of a high number of candidate estimates and allows the decision system to be fed with a clear input. However, this strategy may lead to dead-ends in the continuation of the execution. In this paper, we show that single-state trackability can be expressed in terms of the simulation relation between automata. This allows us to provide a complexity bound and a way to build estimators endowed with this property and, moreover, customizable along some correctness criteria. Our implementation relies on the Sat Modulo Theory solver MonoSAT and experiments show that our encoding scales up and applies to real world scenarios.

We address the problem of intermittent fault diagnosis as an instance of discrete signal estimation, in the context of fault management in autonomous systems and vehicles. We propose an estimation approach based on constrained optimization using conditional preference theories. We show that in some cases, our estimator can fail to find an estimation for the system. We provide a way to detect and eliminate these cases at design time.