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.

Pre-admission testing clinics are care units serving outpatients prior to surgical operation and performing procedure-specific tests to prepare them. Patients may need multiple tests, each performed by a specialized operator and delivered in any order. Exam rooms act as renewable resources: rooms are limited, tests are administered to patients inside the rooms, individually, and patients occupy the room until all the required tests are completed. Careful scheduling of patient appointments is essential in clinic management for both the patient and the provider: on the one hand, minimizing patient waiting time improves service quality, on the other hand, minimizing completion time (makespan) improves system efficiency. In this paper, we propose offline policies for the daily scheduling of pre-admission test appointments. As a benchmark, we consider two online scheduling policies widely used in common practice. Each of these offers a different compromise between complexity and resource exploitation. The proposed optimization-based offline booking policy is identified as a new problem in the machine scheduling literature, for which we propose a network-flow model representation. A family of matheuristics based on different variable fixing criteria is provided to circumvent the high computational effort required to solve the mathematical model to optimality on real-size instances. The performance, advantages and disadvantages of each of the online and offline policies are compared in a variety of scenarios based on realistic data. Through this work, decision-makers have a new set of tools they can choose from according to their priorities.

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.

Echo-doppler examination of the jugular vessel is a powerful tool for the early diagnosis of cardiovascular disorders that can be further related to central nervous system diseases. Unfortunately, the ultrasound technique is strongly operator-dependent, so the quality of the scan, the accuracy of the measurement, and therefore the rapidity and robustness of the diagnosis reflect the degree of training. The paper presents the development of a mechatronic simulation system for improving the skill of novice physicians in echo-doppler procedures. The patient is simulated by a silicone manikin whose materials are designed to have a realistic ultrasound response. Two tubes allow blood-mimicking fluid to flow inside the manikin, simulating the hemodynamics of the internal jugular vein. The mechatronic system is designed for controlling the flow waveform, to reproduce several clinical cases of interest for diagnosis. The experiments investigate the accuracy of the echo-doppler measurements performed on the proposed system by novice operators using a real ultrasound scanner.

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.

This paper is a research article for finding the optimal control of smart power substations for improving the network parameters and reliability. The included papers are the most essential and main studies in the field, which propose a different approach to reach the best performance in electrical power systems. The parameters for improvement are the ability for tracking of the reference signal, stabilizing the system, reducing the error in steady state and controlling the behavior in transient state. The research focuses with the reaching a better transient stability considering voltage and frequency dynamic parameters. The optimal model for the control is focused on minimizing energy consumption but maintaining the controllable parameters, exploring some optimization techniques to find the optimal control, with of aim of minimizing the response time, the energy consumption, and maximizing the reliability by means of improving the controller to be more robust.

We address the design of the lines of a Walking Bus service according to a new paradigm, where children are picked up at home. The scarcity of accompanying persons together with the limit on the length of the deviations from the shortest itinerary of each child make the problem different from the traditional school bus and walking bus design. We propose an arc-based model, a path-based model tackled by column generation, and a heuristic procedure. Solution approaches are tested on a set of real and realistic instances. Real instances refer to the case study of a primary school in Italy. (C) 2019 Elsevier B.V. All rights reserved.

When demand for transportation is low or sparse, traditional transit cannot provide efficient and good-quality service, because of its fixed structure. For this reason, mass transit is evolving toward some degree of flexibility. Although the extension of Dial-a-Ride systems to general public meets such need of adaptability, it presents several drawbacks mostly related to the their extreme flexibility. Consequently, new transportation alternatives, such as demand-adaptive systems (DASs), combining characteristics from both the traditional transit and Dial-a-Ride, have been introduced. For their twofold nature, DASs require careful planning. We focus on tactical aspects of the planning process by formalizing the single-line DAS design problem with stationary demand and proposing two alternative hierarchical decomposition approaches for its solution. The main motivation behind this work is to provide a general methodology suitable to be used as a tool to build the tactical DAS plan in real-life conditions. We provide an experimental study where the two proposed decomposition methods are compared and the general behavior of the systems is analyzed when altering some design parameters. Furthermore, we test the versatility of our methods on a variety of situation that may be encountered in real-life conditions.

This paper experimentally investigates the relationships among three major stakeholders that are involved in drug inventory management at Intensive Care Units (ICUs), namely: i) nurses, who in person manage drug orders and carry out storage operations, ii) clinicians, who choose the therapy and shape demand, and iii) the hospital management, who is in charge of the economic sustainability of the hospital. As a case study, we consider the ICU ward of a major Italian public hospital and we focus on antibiotics. We exploit a previously developed Mixed Integer Linear Programming model which decides, for each drug, when and how much to order, and we improve it by adding different sets of constraints to represent each stakeholders’ point of view. By solving three generalized models, each of which ties the satisfaction of a single stakeholder to different thresholds, we explore the mutual effects of taking explicitly into account different perspectives within the inventory policy. We implemented an instance generator, built on the basis of empirical probability distributions extracted from a large set of observed historical data and representing the decision flow ruling drugs prescription. Extensive experiments have been carried out on a set of realistic instances provided by the generator. Results based on our test case not only provide computational evidence to intuitive relations among stakeholders, but also suggest possible levels of compromise. Improved stakeholder satisfaction would also benefit the patient, the passive stakeholder who is the ultimate subject of the caring process.

This paper proposes a drugs inventory policy at point-of-use level, tailored for the Intensive Care Unit (ICU) case study and aimed at relieving nurses of the time-wasting task of drugs ordering and refilling. The policy aims at jointly reducing order occurrences and imposing service regularity, while keeping stock value as low as possible. An optimization model is proposed and solved on a one-month period real instance and on a set of realistic ones derived from drugs consumption data collection at the ward. The potentially conflicting priorities of three stakeholders (nurses, administration and clinicians) have been successfully incorporated and their impact on order occurrences and stock value has been discussed. Computational results suggest that it is possible to optimize the time-consuming order process currently adopted at the ICU case study. This study is part of a more comprehensive project in which the optimization block will be integrated with a demand forecasting tool and deployed in a rolling horizon framework.

We address a particular pickup and delivery vehicle routing problem arising in the collection and disposal of bulky recyclable waste. Containers of different types, used to collect different waste materials, once full, must be picked up to be emptied at suitable disposal plants and replaced by empty containers alike. All requests must be served, and routes are subject to a maximum duration constraint. Minimizing the number of vehicles is the main objective, while minimizing the total route duration is a secondary objective. The problem belongs to the class of rollon-rolloff vehicle routing problems (RR-VRPs), though some characteristics of the case study, such as the free circulation of containers and the limited availability of spare containers, allow us to exploit them in the solution approach. We formalize the problem as a special vehicle routing problem on a bipartite graph, we analyze its structure, and we compare it to similar problems emphasizing the impact of limited spare containers. Moreover, we propose a neighborhood-based metaheuristic that alternatively switches from one objective to the other along the search path and periodically destroys and rebuilds parts of the solution. The main algorithm components are experimentally evaluated on real and realistic instances, the largest of which fail to be solved by a mixed-integer linear programming solver. We are increasingly competitive with the solver as the instance size increases, especially regarding fleet size. In addition, the algorithm is applied to the benchmark instances for the RR-VRP.

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.

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.

While the information and computing revolution is often credited to Moore's Law scaling, the complexity challenge has been actually addressed by electronic design automation, which is capable of transforming complex system-on-chip designs from high-level functional specifications into detailed geometric descriptions. Similarly, the uptake of emerging interconnect technologies depends not only on technology maturity, but also on the availability of tools and methodologies bridging the gap between system designers and technology developers. This chapter provides an early-phase synthesis methodology for wavelength-routed optical networks-on-chip, capturing all design points in a unified design framework, and refining them into an actual implementation.

Pedibus, also known as the Walking School Bus, is a popular system in Western countries aimed at increasing the percentage of children walking to school, reducing vehicular congestion at school gates, and legitimating walking as a mobility mode. In its simplest version, a Pedibus line is a sequence of stops starting from a child home, visiting a sequence of other children’s home, and ending at the school. The service is usually run by volunteers, according to common sense based rules. This paper aims at providing optimization based methodological support to decision makers. The line design problem can be described as follows: given the school location, the children home addresses, and the distance between each pair of locations, we have to design a minimum number of lines rooted at the school so that each location belongs to one line and the distance from school to each location along the line is below a given threshold. The objective function is due to the need for adults supervising each line, whose limited availability may hamper the service long term viability. A secondary objective encourages line merging before destination. Heuristic solution approaches to the design of Pedibus lines have been proposed in the literature, considering Pedibus as a mere application of the school bus routing problem. We propose a new arc-based model tailored on the Pedibus features, i.e., allowing lines merging, which yields a constrained spanning tree network structure. Tests on real and realistic networks show that small and medium size instances are solved to optimality, while the weak linear relaxation of the proposed arc model prevents fast convergence so that largest instances with longest walking distances are solved heuristically. This work paves the way to further studies on path based models to speed up convergence to optimality and to encompass different Pedibus variants.

We present the symmetric traveling salesman problem with generalized latency (TSP-GL) a new problem arising in the planning of the important class of semiflexible transit systems. The TSP-GL can be seen as a very challenging variant of the symmetric traveling salesman problem (S-TSP), where the objective function combines the usual cost of the circuit with a routing component accounting for the passenger travel times. The main contributions of the paper include the formulation of the problems in terms of multicommodity flows, the study of its mathematical properties, and the introduction of a branch-and-cut approach based on Benders reformulation taking advantage of properties that relate the feasible region of the TSP-GL and the S-TSP polyhedron. An extensive computational experimentation compares a number of variants of the proposed algorithm, as well as a commercial solver. These experiments show that the method we propose significantly outperforms a well-known commercial solver and obtains good-quality solutions to realistically sized instances within short computational times.

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 (ONoCs). 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 (ASP). In this work, we detail the ASP 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 (CLP(FD)). This paper is under consideration for possible publication on Theory and Practice of Logic Programming.