ABSTRACT In hazardous material transport on road networks, two conflicting objectives must be addressed simultaneously: minimizing risk and minimizing cost. Risk mitigation policies may yield as a secondary outcome uneven flow distribution on the network. This study empowers an existing risk mitigation policy based on gateways (GBP) to improve fairness. According to GBP, each vehicle is obliged to traverse a compulsory node (a gateway) on its minimum cost itinerary from origin to destination. Gateways must be located on a few network nodes and assigned to vehicles to minimize total risk, yielding a bi‐level optimization problem. GBP already proved able to reduce total risk by opening just a few gateways and to a limited detriment of total cost. However, gateways may end up acting as flow concentrators, thus hampering equity. This study aims to bridge the gap between risk mitigation and fairness. To this aim, we generalize the multi‐commodity flow formulation of the problem by imposing a capacity constraint on the nodes, discuss its impact on the model structure, and experimentally investigate whether it is possible to achieve a more equitable risk distribution and how total risk and total cost are affected.

Abstract Chronic patients suffering from non-communicable diseases are often enrolled into a diagnostic and therapeutic care program featuring a personalized care plan. Healthcare is mostly provided at the patient’s home, but those examinations and treatments that must be delivered at the hospital have to be explicitly booked. Booking is not trivial due to, on the one hand, the several time constraints that become particularly tight in the case of comorbidity, on the other hand, the limited availability of both staff and equipment at the hospital care units. This suggests that the scheduling of the clinical pathways for enrolled outpatients should be managed in a centralized manner, taking advantage of the fact that demand for services is known well in advance. The aim is to serve as many requests as possible (unattended requests are supplied by contracted private health facilities) in a timely manner, taking patients priority into account. Booking involves setting a date and a time for each selected health service, which is rather complex. In this work, we provide a declarative approach by encoding the problem in Answer Set Programming (ASP). In order to improve the scalability of the ASP approach, we present and compare two heuristic approaches, respectively based on service demand and time decomposition. All approaches are tested on instances of increasing size to assess scalability with respect to time horizon and number of requests.

Abstract In answer set programming (ASP), the user can define declaratively a problem and solve it with efficient solvers; practical applications of ASP are countless and several constraint problems have been successfully solved with ASP. On the other hand, solution time usually grows in a superlinear way (often, exponential) with respect to the size of the instance, which is impractical for large instances. A widely used approach is to split the optimization problem into subproblems (SPs) that are solved in sequence, some committing to the values assigned by others, and reconstructing a valid assignment for the whole problem by juxtaposing the solutions of the single SPs. On the one hand, this approach is much faster due to the superlinear behavior; on the other hand, it does not provide any guarantee of optimality: committing to the assignment of one SP can rule out the optimal solution from the search space. In other research areas, logic-Based Benders decomposition (LBBD) proved effective; in LBBD, the problem is decomposed into a master problem (MP) and one or several SPs. The solution of the MP is passed to the SPs that can possibly fail. In case of failure, a no-good is returned to the MP that is solved again with the addition of the new constraint. The solution process is iterated until a valid solution is obtained for all the SPs or the MP is proven infeasible. The obtained solution is provably optimal under very mild conditions. In this paper, we apply for the first time LBBD to ASP, exploiting an application in health care as case study. Experimental results show the effectiveness of the approach. We believe that the availability of LBBD can further increase the practical applicability of ASP technologies.

Silicon photonics is gaining momentum as a candidate technology platform for future intra- and inter-chip communications. However, its industrial uptake depends not only on technology maturity, but also on the capability to bridge the abstraction gap between technology developers and system designers. This paper presents an early-stage cross-layer refinement methodology of wavelength-routed optical network-on-chip topologies, linking logic topology synthesis to the physical implementation steps.

Abstract One promising trend in digital system integration consists of boosting on-chip communication performance by means of silicon photonics, thus materializing the so-called Optical Networks-on-Chip. Among them, wavelength routing can be used to route a signal to destination by univocally associating a routing path to the wavelength of the optical carrier. Such wavelengths should be chosen so to minimize interferences among optical channels and to avoid routing faults. As a result, physical parameter selection of such networks requires the solution of complex constrained optimization problems. In previous work, published in the proceedings of the International Conference on Computer-Aided Design, we proposed and solved the problem of computing the maximum parallelism obtainable in the communication between any two endpoints while avoiding misrouting of optical signals. The underlying technology, only quickly mentioned in that paper, is Answer Set Programming. In this work, we detail the Answer Set Programming approach we used to solve such problem. Another important design issue is to select the wavelengths of optical carriers such that they are spread across the available spectrum, in order to reduce the likelihood that, due to imperfections in the manufacturing process, unintended routing faults arise. We show how to address such problem in Constraint Logic Programming on Finite Domains.

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.

Positioning of valves is a real-life issue in Water Distribution System (WDS) design and, currently, it is usually addressed by hydraulic engineers either by hand or by means of genetic algorithms, that give no assurance of optimality. Since a given valves placement identifies a sectorization of the WDS in several isolable portions, the valves positioning problem can be seen as a variant of the well known graph partitioning problem, which is a hard combinatorial problem. Cattafi et al . (2011, TPLP , 11, 731–747) showed recently that Computational Logic can provide technologies and techniques that can be exploited to model and achieve the optimal partition of the water network (i.e. the optimal positioning of valves). In particular, the authors tackled the optimization of the valves positioning through a two player game model, giving a Constraint Logic Programming formalization to solve it effectively. The aim of this article, instead, is to investigate the potential of Answer Set Programming in this practical application; evaluation is in terms both of language expressivity and solving efficiency. Results are discussed for different ASP models and a comparison with the CLP(FD) technique shown by Cattafi et al . (2011, TPLP , 11, 731–747) will be given.

Low-density parity-check (LDPC) codes are gaining interest for high data rate applications in both terrestrial and spatial communications. They can be designed and studied through a bipartite graph whose characteristics affect the performance. This paper proposes a low-complexity method to improve the performance of LDPC codes by selectively removing some cycles from the associated bipartite graph. The method is based on a modified version of the breadth first search (BFS) algorithm that we call modified BFS (MBFS), which is applied to find cycles, and a greedy procedure to eliminate them. Throughout the paper we will give a detailed description of the algorithm proposed and analytically study its complexity. Simulation results show that this girth conditioning method applied to some classes of codes, whose structure allows further optimization, can lead to a significative complexity reduction and a performance improvements with respect to other methods.

Abstract This paper presents a new application of logic programming to a real-life problem in hydraulic engineering. The work is developed as a collaboration of computer scientists and hydraulic engineers, and applies Constraint Logic Programming to solve a hard combinatorial problem. This application deals with one aspect of the design of a water distribution network, i.e., the valve isolation system design. We take the formulation of the problem by Giustolisi and Savić (2008 Optimal design of isolation valve system for water distribution networks. InProceedings of the 10th Annual Water Distribution Systems Analysis Conference WDSA2008, J. Van Zyl, A. Ilemobade, and H. Jacobs, Eds.) and show how, thanks to constraint propagation, we can get better solutions than the best solution known in the literature for the Apulian distribution network. We believe that the area of the so-calledhydroinformaticscan benefit from the techniques developed in Constraint Logic Programming and possibly from other areas of logic programming, such as Answer Set Programming.

The demand-adaptive systems studied in this paper attempt to offer demand-responsive services within the framework of traditional scheduled bus transportation: Users call to request service between two given points and, in so doing, induce detours in the vehicle routes; at the same time, though, a given set of compulsory stops is always served according to a predefined schedule, regardless of the current set of active requests. The model developed to select requests and determine the routing of the vehicle yields a difficult formulation but with a special structure that may be used to develop efficient algorithms. In this paper, we develop, test, and compare several solution strategies for the single line-single vehicle problem that belong to two general meta-heuristic classes, memory-enhanced greedy randomized multistart constructive procedures, and tabu search methods. Hybrid meta-heuristics combining the two methods are also analyzed.