The hybrid architecture analysis and design language (AADL) has been proposed to model the interactions between embedded control systems and continuous physical environment. However, the worst-case performance analysis of hybrid AADL designs often leads to overly pessimistic estimations, and is not suitable for accurate reasoning about overall system performance, in particular when the system closely interacts with an uncertain external environment. To address this challenge, this paper proposes a statistical model checking-based framework that can perform quantitative evaluation of uncertainty-aware hybrid AADL designs against various performance queries. Our approach extends hybrid AADL to support the modeling of environment uncertainties. Furthermore, we propose a set of transformation rules that can automatically translate AADL designs together with designers' requirements into networks of priced timed automata and performance queries, respectively. Comprehensive experimental results on the movement authority scenario of Chinese train control system level 3 demonstrate the effectiveness of our approach.

Branch-and-bound (B&B) approaches are widely investigated in resource constrained scheduling (RCS). However, due to the lack of approaches that can generate a tight schedule at the beginning of the search, B&B approaches usually start with a large initial search space, which makes the following search of an optimal schedule time-consuming. To address this problem, this paper proposes a parallel two-phase B&B approach that can drastically reduce the overall RCS time. This paper makes three major contributions: i) it proposes three partial-search heuristics that can quickly find a tight schedule to compact the initial search space; ii) it presents a two-phase search framework that supports the efficient parallel search of an optimal schedule; iii) it investigates various bound sharing and speculation techniques among collaborative tasks to further improve the parallel search performance at different search phases. The experimental results based on well-established benchmarks demonstrate the efficacy of our proposed approach.