Source localization using acoustic vector sensors: a music approach

Traditionally, a large array of microphones is used to localize multiple far field sources in acoustics. We present a sound source localization technique that requires far less channels and measurement locations (affecting data channels, setup times and cabling issues). This is achieved by using an acoustic vector sensor (AVS) in air that consists of four collocated sensors: three orthogonally placed acoustic particle velocity sensors and an omnidirectional sound pressure transducer. Experimental evidence is presented demonstrating that a single 4 channel AVS based approach accurately localizes two uncorrelated sources. The method is extended to multiple AVS, increasing the number of sources that can be identified. Theory and measurement results are presented. Attention is paid to the theoretical possibilities and limitations of this approach, as well as the signal processing techniques based on the MUSIC method

Risk Theory with the Gamma Process

The aggregate claims process is modelled by a process with independent, stationary and nonnegative increments. Such a process is either compound Poisson or else a process with an infinite number of claims in each time interval, for example a gamma process. It is shown how classical risk theory, and in particular ruin theory, can be adapted to this model. A detailed analysis is given for the gamma process, for which tabulated values of the probability of ruin are provide

A scenario based approach for flexible resource loading under uncertainty

Order acceptance decisions in manufacture-to-order environments are often made based on incomplete or uncertain information. To promise reliable due dates and to manage resource capacity adequately, resource capacity loading is an indispensable supporting tool. We propose a scenario based approach for resource loading under uncertainty that minimises the expected costs. The approach uses an MILP to find a plan that has minimum expected costs over all relevant scenarios. We propose an exact and a heuristic solution approach to solve this MILP. A disadvantage of this approach is that the MILP may become too large to solve in reasonable time. We therefore propose another approach that uses an MILP with a sample of all scenarios. We use the same exact and heuristic methods to solve this MILP. Computational experiments show that, especially for instances with much slack, solutions obtained with deterministic techniques for a expected scenario can be improved with respect to their expected costs. We also show that for large instances the heuristic outperforms the exact approach given a computation time as a stopping criterion

A dynamic programming heuristic for vehicle routing with time-dependent travel times and required breaks.

For the intensively studied vehicle routing problem (VRP), two real-life restrictions have received only minor attention in the VRP-literature: traffic congestion and driving hours regulations. Traffic congestion causes late arrivals at customers and long travel times resulting in large transport costs. To account for traffic congestion, time-dependent travel times should be considered when constructing vehicle routes. Next, driving hours regulations, which restrict the available driving and working times for truck drivers, must be respected. Since violations are severely fined, also driving hours regulations should be considered when constructing vehicle routes, even more in combination with congestion problems. The objective of this paper is to develop a solution method for the VRP with time windows (VRPTW), time-dependent travel times, and driving hours regulations. The major difficulty of this VRPTW extension is to optimize each vehicle’s departure times to minimize the duty time of each driver. Having compact duty times leads to cost savings. However, obtaining compact duty times is much harder when time-dependent travel times and driving hours regulations are considered. We propose a restricted dynamic programming (DP) heuristic for constructing the vehicle routes, and an efficient heuristic for optimizing the vehicle’s departure times for each (partial) vehicle route, such that the complete solution algorithm runs in polynomial time. Computational experiments demonstrate the trade-off between travel distance minimization and duty time minimization, and illustrate the cost savings of extending the depot opening hours such that traveling before the morning peak and after the evening peak becomes possible

Anticipatory routing of police helicopters

We have developed a decision support application for the Dutch Aviation Police and Air Support unit for routing their helicopters in anticipation of unknown future incidents. These incidents are not known in advance, yet do require a swift response. A response might include the dispatch of a police helicopter to support the police on the ground. If a helicopter takes too long to arrive at the crime scene, it might be too late to assist. Hence, helicopters have to be proximate when an incident happens to increase the likelihood of being able to support the police on the ground in apprehending suspects. We propose the use of a forecasting technique, followed by a routing heuristic to maximize the number of incidents where a helicopter provides a successful assist. We have implemented these techniques in a decision support application in collaboration with the Dutch Aviation Police and Air Support. Using numerical experiments, we show that our application has the potential to improve the success rate with a factor nine. The Dutch Air Support and Aviation Police are now using the application

Minimizing the waiting time for emergency surgery

Hospitals aim to deliver the highest quality of care. One key priority is to schedule emergency surgeries as quickly as possible, because postponing them generally increases a patient’s risk of complications and even death. In this paper, we consider the case that emergency surgeries are scheduled in one of the elective Operating Rooms (ORs). In this situation, emergency patients are operated once an going elective surgery has finished. We denote these completion times of the elective surgeries by ‘break-in-moments’ (BIMs). The waiting time for emergency surgeries can be reduced by spreading these BIMs as evenly as possible over the day. This can be achieved by sequencing the surgeries in their assigned OR, such that the maximum interval between two consecutive BIMs is minimized. In this paper, we discuss several exact and heuristic solution methods for this new type of scheduling problem. However, in practice, emergency surgeries arising throughout the day and the uncertainty of the durations of elective surgeries, may disrupt the initial schedule. As a result, the completion times of the elective surgeries, and therefore, the BIMs change, leading also to a change of the maximum distance between two BIMs. To estimate this effect and investigate the robustness of the created schedules, we conduct a simulation study. Computational results show that the best approaches reduce the waiting time of emergency surgeries by approximately 10%

Designing cyclic appointment schedules for outpatient clinics with scheduled and unscheduled patient arrivals

We present a methodology to design appointment systems for outpatient clinics and diagnostic facilities that offer both walk-in and scheduled service. The developed blueprint for the appointment schedule prescribes the number of appointments to plan per day and the moment on the day to schedule the appointments. The method consists of two models that are linked by an algorithm; one for the day process that governs scheduled and unscheduled arrivals on the day and one for the access process of scheduled arrivals. Appointment schedules that balance the waiting time at the facility for unscheduled patients and the access time for scheduled patients, are calculated iteratively using the outcomes of the two models. The method is of general nature and can therefore also be applied to scheduling problems in other sectors than health care

PCN94 - Cost-effectiveness and preference for follow-up scenarios following breast cancer

OBJECTIVES: About one in every eight women in The Netherlands develops breast cancer. Every year, 11,000 new cases are registered and about 3500 women die of breast cancer. Prognosis after primary treatment for patients with breast cancer is improving. This leads to an increased number of patients in follow-up, which leads to increased workload. One of the main goals of follow-up is to improve the survival of patients. This study aims to determine a more individualized follow-up by modelling costeffectiveness of various follow-up scenarios and by determining individual preferences by using a discrete choice experiment (DCE). METHODS: A discrete-event state-transition model was developed to estimate the cost-effectiveness of all scenarios for all patient groups. In addition, a discrete choice experiment (DCE) was designed to establish patient preferences. The DCE incorporated three process attributes (duration of follow-up, frequency and type of consult) and data were collected in a sample of 125 breast cancer patients. Patients had to complete all 18 choice sets that were generated from the three attributes. RESULTS: The modelling study revealed recommendations for follow-up in different age categories. Patients younger than 40 and patients with unfavorable tumor characteristics (>3 lymph nodes, tumor size >2 cm) can benefit from a more intensive follow-up of five or possibly ten years. Patients older than 40 but younger than 70 years old sometimes benefit from a more intensive follow-up; e.g. when younger than 50 and tumor size >2 cm. The DCE, however, showed that patients chose maximum levels of follow-up independent from age and their individual clinical risk profile. Duration of follow-up and type of consult (either hospital visit or telephone) weighted approximately 0.43 and 0.50 respectively. The frequency of follow-up (either once or twice a year) was least important (0.07). CONCLUSIONS: The model showed that follow-up may be individualized according to risk profile and age. However, patients preferred long and intensive follow-up strategies after breast cancer treatment. Taking into account individual patient preferences it may be recommended to reduce the frequency of follow-up to once a year. The service delivery by nurse practioners is well appreciated and another means for improving cost-effective follow-up

Local Thermoelectric Response from a Single Néel Domain Wall

Spatially resolved thermoelectric detection of magnetic systems provides a unique platform for the investigation of spintronic and spin caloritronic effects. Hitherto, these investigations have been resolution limited, confining analysis of the thermoelectric response to regions where the magnetisation is uniform or collinear at length scales comparable to the domain size. Here, we investigate the thermoelectric response from a single trapped domain wall using a heated scanning probe. Following this approach, we unambiguously resolve the domain wall due to its local thermoelectric response. Combining analytical and thermal micromagnetic modelling, we conclude that the measured thermoelectric signature is unique to that of a domain wall with Néel like character. Our approach is highly sensitive to the plane of domain wall rotation, which permits the distinct identification of Bloch or Néel walls at the nanoscale and could pave the way for the identification and characterisation of a range of non-collinear spin textures through their thermoelectric signatures