An efficient decomposition approach for surgical planning

This talk presents an efficient decomposition approach to surgical planning. Given a set of surgical waiting lists (one for each discipline) and an operating theater, the problem is to decide the room-to-discipline assignment for the next planning period (Master Surgical Schedule), and the surgical cases to be performed (Surgical Case Assignment), with the objective of optimizing a score related to priority and current waiting time of the cases. While in general MSS and SCA may be concurrently found by solving a complex integer programming problem, we propose an effective decomposition algorithm which does not require expensive or sophisticated computational resources, and is therefore suitable for implementation in any real-life setting. Our decomposition approach consists in first producing a number of subsets of surgical cases for each discipline (potential OR sessions), and select a subset of them. The surgical cases in the selected potential sessions are then discarded, and only the structure of the MSS is retained. A detailed surgical case assignment is then devised filling the MSS obtained with cases from the waiting lists, via an exact optimization model. The quality of the plan obtained is assessed by comparing it with the plan obtained by solving the exact integrated formulation for MSS and SCA. Nine different scenarios are considered, for various operating theater sizes and management policies. The results on instances concerning a medium-size hospital show that the decomposition method produces comparable solutions with the exact method in much smaller computation time

A bilevel rescheduling framework for optimal inter-area train coordination

Railway dispatchers reschedule trains in real-time in order to limit the propagation of disturbances and to regulate traffic in their respective dispatching areas by minimizing the deviation from the off-line timetable. However, the decisions taken in one area may influence the quality and even the feasibility of train schedules in the other areas. Regional control centers coordinate the dispatchers\u27 work for multiple areas in order to regulate traffic at the global level and to avoid situations of global infeasibility. Differently from the dispatcher problem, the coordination activity of regional control centers is still underinvestigated, even if this activity is a key factor for effective traffic management. This paper studies the problem of coordinating several dispatchers with the objective of driving their behavior towards globally optimal solutions. With our model, a coordinator may impose constraints at the border of each dispatching area. Each dispatcher must then schedule trains in its area by producing a locally feasible solution compliant with the border constraints imposed by the coordinator. The problem faced by the coordinator is therefore a bilevel programming problem in which the variables controlled by the coordinator are the border constraints. We demonstrate that the coordinator problem can be solved to optimality with a branch and bound procedure. The coordination algorithm has been tested on a large real railway network in the Netherlands with busy traffic conditions. Our experimental results show that a proven optimal solution is frequently found for various network divisions within computation times compatible with real-time operations

Evaluation of the quantiles and superquantiles of the makespan in interval valued activity networks

This paper deals with the evaluation of quantile-based risk measures for the makespan in scheduling problems represented as temporal networks with uncer tainties on the activity durations. More specifically, for each activity only the interval for its possible duration values is known in advance to both the sched uler and the risk analyst. Given a feasible schedule, we calculate the quantiles and the superquantiles of the makespan which are of interest as risk indicators in various applications. To this aim we propose and test a set of novel algorithms to determine rapid and accurate numerical estimations based on the calculation of theoretically proven lower and upper bounds. An extensive experimental campaign compu tationally shows the validity of the proposed methods, and allows to highlight their performances through the comparison with respect to the state-of-the-art algorithms

Mapping the avalanche risk: from survey to cartographic production. The avalanche bulletin of the Meteomont Service of the Alpine Troops Command

During the last decades, the process of explaining life-threatening natural hazards to the public has become a major public issue from the point of view of effective prevention policies. The avalanche risk and the communication methods aimed at its forecasting and prevention constitute the focus of this paper. Among the strategies for an effective communication of environmental risks, cartography plays a pivotal role. It has proved to be essential not only for communication purposes, but also for the planning of prompt and efficient preventive interventions; in so doing, it contributes to the reduction of avalanche-caused damages and deaths. The paper investigates prevention and forecasting activities of the Meteomont Service of the Alpine Troops Command (COMTA) of Bolzano (capital city of the province of South Tyrol - North Italy), resulting in the daily publication of avalanche bulletins (Bollettini valanghe), which also include hazard maps. Specifically, the phases that contribute to the production of the avalanche bulletin and the embedded avalanche risk maps will be firstly examined; secondly, such maps will be analysed in order to assess their communicative potential for the purpose of a correct interpretation aimed at the effective prevention of snow-related risks in mountain areas. Possible improvement will be proposed on the basis of the experience of several avalanche warning services worldwide

Bridging research and dissemination in the CoViD-19 era: a WebGIS dashboard for the Autonomous Province of Trento (Italy)

The paper presents an ongoing project devoted to the study, the analysis and the representation of epidemiological data related to CoViD-19 spread in the territory of the Province of Trento (Italy), both for scientific and communication purposes. In this broader context, the construction of a digital cartography tool as a WebGIS to allow local communities understanding of epidemiological spread is presented. Data have been supplied by the local Provincial Health Authority; statistic have been processed in order to develop municipality scale vector polygonal coropleth and point maps in order to show affected, health and death rate distribution. A timeline allows the representation of changes and dynamics from Spring 2020 to the current date. The database provides “on-the-fly” data to the production scripts of maps and time charts. These scripts querying the database produce a geographic file in the geojson standard interchange format. This file is read by the javascript scripts based on the leaflet libraries for the production of the final maps. In a similar process, scripts based on the chart.js library produce the graph of the data temporal variation, automatically reading dates and interval time of analysis. A custom procedure was developed to allow the periodic update of the dataset. New information is added to the database by uploading an external spreadsheet. The study presents the methodology to develop and assess the WebGIS for managing, visualize and analyse Coronavirus diffusion. Future implementation of the WebGIS will expand the used data and allow the comparison with social and environmental factors

Replication and sequencing of unreliable jobs on m parallel machines:New results

This paper gives new results for the problem of sequencing m copies of n unreliable jobs (i.e., jobs that have a certain probability of being successfully carried out) on m parallel machines (one copy per machine). A job is carried out if at least one of its copies is successfully completed, in which case a certain revenue is earned. If the copy of a job fails, the corresponding machine is blocked and cannot perform the subsequently scheduled job copies. The problem is to sequence the n copies of each job on each of the m machines in order to maximize the expected revenue. For the case of m=2, Agnetis et al. (2022) proposed a metaheuristic approach and some upper bounding schemes. Here we address the general m-machine problem, giving a simple (1−1/e)-approximation algorithm, an even simpler algorithm, for which we show a tight logarithmic approximation guarantee, and an additional heuristic as well as an additional metaheuristic. We provide computational results, which, along with a new upper-bounding scheme, establish the effectiveness of our approaches in practice.</p

Susceptibility of optimal train schedules to stochastic disturbances of process times

This work focuses on the stochastic evaluation of train schedules computed by a microscopic scheduler of railway operations based on deterministic information. The research question is to assess the degree of sensitivity of various rescheduling algorithms to variations in process times (running and dwell times). In fact, the objective of railway traffic management is to reduce delay propagation and to increase disturbance robustness of train schedules at a network scale. We present a quantitative study of traffic disturbances and their effects on the schedules computed by simple and advanced rescheduling algorithms. Computational results are based on a complex and densely occupied Dutch railway area; train delays are computed based on accepted statistical distributions, and dwell and running times of trains are subject to additional stochastic variations. From the results obtained on a real case study, an advanced branch and bound algorithm, on average, outperforms a First In First Out scheduling rule both in deterministic and stochastic traffic scenarios. However, the characteristic of the stochastic processes and the way a stochastic instance is handled turn out to have a serious impact on the scheduler performance

Micro-encapsulated and colonic-release sodium butyrate modulates gut microbiota and improves abdominal pain in patients with symptomatic uncomplicated diverticular disease

The role of gut microbiota (GM) in the pathogenesis of Symptomatic Uncomplicated Diverticular Disease (SUDD) remains controversial. Here, we assessed the efficacy of a butyrate formulation in modulating GM and abdominal pain in patients with SUDD. A retrospective study was conducted in patients with SUDD who were treated with a delayed- and colonic-release formulation of butyrate (two capsules bid, for a total dose of 400 mg butyrate) for 3 months. GM was profiled before (T0) and after 90 days of treatment (T2) using 16S rRNA amplicon sequencing. The primary endpoint was change in GM at T2; secondary endpoints were reduction in abdominal pain severity according to VAS (Visual Analog Scale, 0: absence; 10: maximum severity) at T1 (45 days) and T2, stool characteristics according to the Bristol stool form scale at T0, T1 and T2, and safety of treatment. Fifty-nine patients with SUDD (59.3% male; median age 65.5 years, interquartile range 55–71 years) completed treatment. The butyrate formulation increased GM diversity and resulted in several compositional changes that were closely related to baseline abdominal pain severity. Regarding secondary endpoints, abdominal pain decreased significantly over time, while the Bristol stool form scale did not. Mild adverse events were recorded in 3 (5.08%) patients. This study showed that a microencapsulated and colonic-release formulation of butyrate favorably modulates GM and reduces abdominal pain in patients with SUDD