Optimizing electricity distribution using two-stage integer recourse models

We consider two planning problems faced by an electricity distributor. Electricity can be ob-tained both from power plants and small generators such as hospitals and greenhouses, whereas the future demand for electricity is uncertain. The price of electricity obtained from the power plants depends on quota that are to be determined in a yearly contract, whereas the (given) contracts with small generators contain various constraints on switching them on or off.

Identification of black hole power spectral components across all canonical states

From a uniform analysis of a large (8.5 Ms) Rossi X-ray Timing Explorer data set of Low Mass X-ray Binaries, we present a complete identification of all the variability components in the power spectra of black holes in their canonical states. It is based on gradual frequency shifts of the components observed between states, and uses a previous identification in the black hole low hard state as a starting point. It is supported by correlations between the frequencies in agreement with those previously found to hold for black hole and neutron stars. Similar variability components are observed in neutron stars and black holes (only the component observed at the highest frequencies is different) which therefore cannot depend on source-specific characteristics such as the magnetic field or surface of the neutron star or spin of the black hole. As the same variability components are also observed across the jet-line the X-ray variability cannot originate from the outer-jet but is most likely produced in either the disk or the corona. We use the identification to directly compare the difference in strength of the black hole and neutron star variability and find these can be attributed to differences in frequency and strength of high frequency features, and do not require the absence of any components. Black holes attain their highest frequencies (in the hard-intermediate and very-high states) at a level a factor ~6 below the highest frequencies attained by the corresponding neutron star components, which can be related to the mass difference between the compact objects in these systems.Comment: 17 pages, 16 figures, accepted for publication in Ap

Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing

In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and quality. Task assignment is challenging, since a worker's performance (i) may fluctuate, depending on both the worker's current personal context and the task context, (ii) is not known a priori, but has to be learned over time. Moreover, learning context-specific worker performance requires access to context information, which may not be available at a central entity due to communication overhead or privacy concerns. Additionally, evaluating worker performance might require costly quality assessments. In this paper, we propose a context-aware hierarchical online learning algorithm addressing the problem of performance maximization in MCS. In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker's context, her/his decisions to accept or decline tasks and the quality in completing tasks. Based on these observations, the LC regularly estimates the worker's context-specific performance. The mobile crowdsourcing platform (MCSP) then selects workers based on performance estimates received from the LCs. This hierarchical approach enables the LCs to learn context-specific worker performance and it enables the MCSP to select suitable workers. In addition, our algorithm preserves worker context locally, and it keeps the number of required quality assessments low. We prove that our algorithm converges to the optimal task assignment strategy. Moreover, the algorithm outperforms simpler task assignment strategies in experiments based on synthetic and real data.Comment: 18 pages, 10 figure

Fast heuristics for a dynamic paratransit problem

In a previous paper we developed a non-standard two-stage recourse model for the dynamic day-ahead paratransit planning problem. Two heuristics, which are frequently applied in the recourse model, contain many details which leads to large CPU times to solve instances of relatively small size. In this paper we simplify both heuristics to decrease CPU time considerably while maintaining the quality of the obtained solutions as much as possible. Numerical experiments on (semi-)realistic instances, inspired by practice, show that our recourse model with fast heuristics provides acceptable solutions within reasonable time.

A dynamic day-ahead paratransit planning problem

Abstract We consider a dynamic planning problem for the transport of elderly and disabled people. The focus is on a decision to make one day ahead: which requests to serve with own vehicles, and which ones to assign to subcontractors, under uncertainty of late requests which are gradually revealed during the day of operation. We call this problem the Dynamic Day-ahead Paratransit Planning problem. The developed model is a nonstandard two-stage recourse model in which ideas from stochastic programming and online optimization are combined: in the first stage clustered requests are assigned to vehicles, and in the dynamic second-stage problem an event-driven approach is used to cluster the late requests once they are revealed and subsequently assign them to vehicles. A genetic algorithm is used to solve the model. Computational results are presented for randomly generated data sets. Furthermore, a comparison is made to a similar problem we studied earlier in which the simplifying but unrealistic assumption has been made that all late requests are revealed at the beginning of the day of operation.

Implementation of new regulatory rules in a multistage ALM model for Dutch pension funds

This paper discusses the implementation of new regulatory rules in a multistage recourse ALM model for Dutch pension funds. The new regulatory rules, which are called the ?Financieel Toetsingskader?, are effective as of January 2007 and have deep impact on the issues of valuation of liabilities, solvency, contribution rate, and indexation. Multistage recourse models have proved to be valuable for pension fund ALM. The ability to include the new regulatory rules would increase the practical value of these models.

An ALM Model for Pension Funds using Integrated Chance Constraints

We discuss integrated chance constraints in their role of short-term risk constraints in a strategic ALM model for Dutch pension funds. The problem is set up as a multistage recourse model, with special attention for modeling the guidelines proposed by the regulating authority for Dutch pension funds. The paper concludes with a numerical illustration of the importance of such short-term risk constraints.

Integrated chance constraints: reduced forms and an algorithm

We consider integrated chance constraints (ICC), which provide quantitative alternatives for traditional chance constraints.We derive explicit polyhedral descriptions for the convex feasible sets induced by ICCs, for the case that the underlying distribution is discrete. Based on these reduced forms, we propose an efficient algorithm for this problem class. The relation to conditional value-at-risk models and (simple) recourse models is discussed, leading to a special purpose algorithm for simple recourse models with discretely distributed technology matrix. For both algorithms, numerical results are presented.

A model-independent analysis of the variability of GRS 1915+105

We analyzed 163 observations of the microquasar GRS 1915+105 made with the Rossi X-ray Timing Explorer (RXTE) in the period 1996-1997. For each observation, we produced light curves and color-color diagrams. We classified the observations in 12 separate classes, based on their count rate and color characteristics. From the analysis of these classes, we reduced the variability of the source to transitions between three basic states: a hard state corresponding to the non-observability of the innermost parts of the accretion disk, and two softer states with a fully observable disk. These two soft states represent different temperatures of the accretion disk, related to different local values of the accretion rate. The transitions between these states can be extremely fast. The source moves between these three states following certain patterns and avoiding others, giving rise to a relatively large but limited number of variability classes. These results are the first step towards a linking of the properties of this exceptional source with standard black-hole systems and with accretion disk models.Comment: Accepted for publication in Astronomy & Astrophysics, 2000 January 6t