Towards Arc Consistency in PLAS

Sammanfattning: The Planning And Scheduling (PLAS) module of ICE (Intelligent Control Environment) is responsible for planning and scheduling a large fleet of vehicles. This process involves the creation of tasks to be executed by the vehicles. Using this information, PLAS decides which vehicles should execute which tasks, which are modelled as constraint satisfaction problems. Solving the constraint satisfaction problems is slow. To improve efficiency, a number of different techniques exist. One of these is arc consistency, that entails taking a constraint satisfaction problem and evaluating its variables pairwise by applying the constraints among them. Using arc consistency, we can discern the candidate solutions to constraint satisfaction problems faster than doing a pure search. In addition, arc consistency allows us to detect and act early on inconsistencies in constraint satisfaction problems. The work in this master thesis includes the implementation of a constraint solver for symbolic constraints, containing the arc consistency algorithm AC3. Furthermore, it encompasses the implementation of a constraint satisfaction problem generator, based on the Erdős-Rényi graph model, inspired by the quasigroup completion problem with holes, that allows the evaluation of the constraint solver on large-sized problems. Using the constraint satisfaction problem generator, a set of experiments were performed to evaluate the constraint solver. Furthermore, a set of complementary scenarios using manually created constraint satisfaction problems were performed to augment the experiments. The results show that the performance scales up well.