Encoding finite linear CSPs as Boolean formulas and solving them by using modern SAT solvers has proven to be highly effective by the award-winning sugar system. We here develop an alternative approach based on ASP that serves two purposes. First, it provides a library for solving CSPs as part of an encompassing logic program. Second, it furnishes an ASP-based CP solver similar to sugar. Both tasks are addressed by using first-order ASP encodings that provide us with a high degree of flexibility, either for integration within ASP or for easy experimentation with different implementations. When used as a CP solver, the resulting system aspartame re-uses parts of sugar for parsing and normalizing CSPs. The obtained set of facts is then combined with an ASP encoding that can be grounded and solved by off-the-shelf ASP systems. We establish the competitiveness of our approach by empirically contrasting aspartame and sugar

This paper heuristically tackles a challenging scheduling problem arising in the field of hydraulic distribution systems in case of a contamination event, that is, optimizing the scheduling of a set of tasks so that the consumed volume of contaminated water is minimized. Each task consists of manually activating a given device, located on the hydraulic network of the water distribution system. In practice, once contamination has been detected, a given number of response teams move along the network to operate each device on site. The consumed volume of contaminated water depends on the time at which each device is operated, according to complex hydraulic laws, so that the value associated to each schedule must be evaluated by a hydraulic simulation.We explore the potentials of Genetic Algorithms as a viable tool for tackling this optimization-simulation problem. We compare different encodings and propose ad hoc crossover operators that exploit the combinatorial structure of the feasible region, featuring hybridization with Mixed Integer Linear Programming.Extensive computational results are provided for a real life hydraulic network of average size, showing the effectiveness of the approach. Indeed, we greatly improve upon common sense inspired solutions which are commonly adopted in practice.

This paper heuristically tackles a challenging scheduling problem arising in the field of hydraulic distribution systems in case of a contamination event, that is, optimizing the scheduling of a set of tasks so that the consumed volume of contaminated water is minimized. Each task consists of manually activating a given device, located on the hydraulic network of the water distribution system. In practice, once contamination has been detected, a given number of response teams move along the network to operate each device on site. The consumed volume of contaminated water depends on the time at which each device is operated, according to complex hydraulic laws, so that the value associated to each schedule must be evaluated by a hydraulic simulation. We explore the potentials of Genetic Algorithms as a viable tool for tackling this optimization-simulation problem. In particular, we compare different encodings and propose ad hoc cross over operators that exploit the combinatorial structure of the feasible region, featuring hybridization with Mixed Integer Linear Programming. Computational results are provided for a real life hydraulic network of average size, showing the effectiveness of the approach. Indeed, we greatly improve upon common sense inspired solutions which are adopted in practice

Emerging technologies in on-chip communication domain bring about new combinatorial optimization problems at design automation. We address the Wavelength Selection Problem in Wavelength-Routed Optical Networks-on-Chip (WRONoCs), where wavelengths act as signal carriers for initiator-to-target communication, so that signals are the least interfering and routing faults are prevented. We present this novel engineering problem and model it as a constrained shortest path on acyclic networks, propose a graph-based mathematical formulation and an iterative procedure on incremental graphs to solve the model on realistic data.

The recent interest in emerging interconnect technologies is bringing the issue of a proper EDA support for them to the forefront, so to tackle the design complexity. A relevant case study is provided by wavelength-routed optical NoCs (WRONoCs), which add communication performance guarantees to the typical latency, throughput and power benefits of an optical link, thus providing an appealing technology for the photonic integration of high-end embedded systems. Typically, only abstract WRONoC models are considered to figure out architecture-level performance, and logic connectivity patterns for the quantification of the required signal strength (i.e., static power). However, this design practice overlooks the needed refinement step, where key physical parameters are assigned such as wavelengths of the optical channels, and size of the optical filters. This step is unfortunately not decoupled from the architectural evaluation, since its main constraint (i.e., avoiding routing faults) turns out to be a key limiter for both the network scale and the achievable communication parallelism. By proposing a formal methodology to select WRONoC parameters while avoding the routing fault concern, this paper aims at maximizing the levels of connectivity and/or of bit parallelism that WRONoCs can achieve, while relating their upper bounds to the uncertainty of the manufacturing process