Automatically calculating periodic timetables in public railway transport systems is an NP-complete problem – namely the Periodic Event Scheduling Problem (PESP). The original model is restricted to basic periodic timetabling. Extending the model by decisional transport networks with flows induces new possibilities in the timetabling and planning process. Subsequently, the given flexibility results in a generic model extension of PESP that can be applied in subsets of the timetabling process. The successful utilization of this approach is presented for distinct chain paths, duplicated chain paths and non-connected flow graphs that represent integration of routing and timetabling, planning of periodic rail freight train paths and track allocation, respectively. Furthermore, the encoding of this generic model into a binary propositional formula is introduced and the appropriate usage of several techniques like SAT solving and MaxSAT to calculate and optimize the corresponding instances will be presented accordingly. Computational results for real-world scenarios suggest the practical impact and give promising perspectives for further scientific research.

Automatically calculating periodic timetables in public railway transport systems is an NP-complete problem – namely the Periodic Event Scheduling Problem (PESP). The original model is restricted to basic periodic timetabling. Extending the model by decisional transport networks with flows induces new possibilities in the timetabling and planning process. Subsequently, the given flexibility results in a generic model extension of PESP that can be applied in subsets of the timetabling process. The successful utilization of this approach is presented for distinct chain paths, duplicated chain paths and non-connected flow graphs that represent integration of routing and timetabling, planning of periodic rail freight train paths and track allocation, respectively. Furthermore, the encoding of this generic model into a binary propositional formula is introduced and the appropriate usage of several techniques like SAT solving and MaxSAT to calculate and optimize the corresponding instances will be presented accordingly. Computational results for real-world scenarios suggest the practical impact and give promising perspectives for further scientific research.

Boolean Satisfiability (SAT) is a problem that holds great theoretical significance along with effective formulations that benefit many real-world applications. While the general problem is NP-complete, advanced solver algorithms and heuristics allow for fast solutions to many large industrial problems. In addition to SAT, many applications rely on generalizations of Satisfiability such as MaxSAT, and Satisfiability Modulo Theories (SMT). Much of the advancement in SAT solver performance has been in the realm of improved sequential solvers with advanced conflict resolution, learning mechanisms, and sophisticated heuristics. There have been some successful demonstrations of massively parallel and hardware-accelerated solvers for SAT, but these have failed to find their way into mainstream usage. This document first presents previous work in Hardware Acceleration of Satisfiability followed by an analysis of why these attempts failed to gain widespread acceptance. It then demonstrates an alternative, hardware-centric approach, based on distributed Stochastic Local Search (SLS) that is better suited to efficient hardware implementation. Then a parallel SLS/CDCL hybrid approach is proposed that is suitable for distributed search with minimal communication overhead while maintaining completeness. Finally the efficacy and flexibility of distributed local search is considered with an adaptation to Weighted Partial MaxSAT (WPMS) and a focused case study on converted Probabilistic Inference instances. / Ph. D. / The Boolean Satisfiability (SAT) problem is an important decision problem that asks whether there exists a solution that satisfies all given constraints over a set of variables that can assume values of either 0 or 1. May real-world decision problems can be translated into SAT, and there exist efficient sequential solvers that can quickly resolve many such instances. Less progress has been made in efficiently scaling SAT solvers to modern multi-core systems and massively parallel hardware accelerators such as GPUs and Field Programmable Gate Arrays (FPGAs). This thesis explore different approaches to solving SAT based decision and optimization problems with the goal of increasing concurrency.