An enhanced concave program relaxation for choice network revenue management

The network choice revenue management problem models customers as choosing from an offer-set, andthe firm decides the best subset to offer at any given moment to maximize expected revenue. The resultingdynamic program for the firm is intractable and approximated by a deterministic linear programcalled the CDLP which has an exponential number of columns. However, under the choice-set paradigmwhen the segment consideration sets overlap, the CDLP is difficult to solve. Column generation has beenproposed but finding an entering column has been shown to be NP-hard. In this paper, starting with aconcave program formulation based on segment-level consideration sets called SDCP, we add a class ofconstraints called product constraints, that project onto subsets of intersections. In addition we proposea natural direct tightening of the SDCP called ?SDCP, and compare the performance of both methodson the benchmark data sets in the literature. Both the product constraints and the ?SDCP method arevery simple and easy to implement and are applicable to the case of overlapping segment considerationsets. In our computational testing on the benchmark data sets in the literature, SDCP with productconstraints achieves the CDLP value at a fraction of the CPU time taken by column generation and webelieve is a very promising approach for quickly approximating CDLP when segment consideration setsoverlap and the consideration sets themselves are relatively small.

Iowa Department of Revenue, Strategic Plan, 2007-2010

This report outlines the strategic plan for Iowa Department of Revenue, goals and mission.

Ganglion sentinelle dans les cancers du col de faible stade. Données actuelles. Assurance qualité. Perspectives [Sentinel lymph node in low stage cervical cancers. Current data. Quality assurance. Prospects].

Le prélèvement des ganglions sentinelles apparaît comme une technique séduisante pour l'évaluation ganglionnaire des cancers du col utérin de faible stade. La sélection d'une population à bas risque de métastase ganglionnaire, un entraînement minimal et le respect de quelques règles simples permettent de limiter le risque de faux négatif au minimum. La technique apporte des informations supplémentaires sur le plan anatomique en identifiant des ganglions situés en dehors des zones habituelles de curage, et sur le plan histologique avec la mise en évidence de cellules tumorales isolées et surtout de micrométastases dont la valeur pronostique est suspectée Sentinel node biopsy appears as a promising technique for the assessment of nodal disease in early cervical cancers. Selection of a population with a low risk of nodal metastasis, a minimal training, and simple rules allow a low false negative rate. Sentinel node biopsy provides supplementary information, such as anatomical information (nodes outside of routine lymphadenectomy areas) and histological information (isolated tumors cells and micrometastases).

A finite-population revenue management model and a risk-ratio procedure for the joint estimation of population size and parameters

Many dynamic revenue management models divide the sale period into a finite number of periods T and assume, invoking a fine-enough grid of time, that each period sees at most one booking request. These Poisson-type assumptions restrict the variability of the demand in the model, but researchers and practitioners were willing to overlook this for the benefit of tractability of the models. In this paper, we criticize this model from another angle. Estimating the discrete finite-period model poses problems of indeterminacy and non-robustness: Arbitrarily fixing T leads to arbitrary control values and on the other hand estimating T from data adds an additional layer of indeterminacy. To counter this, we first propose an alternate finite-population model that avoids this problem of fixing T and allows a wider range of demand distributions, while retaining the useful marginal-value properties of the finite-period model. The finite-population model still requires jointly estimating market size and the parameters of the customer purchase model without observing no-purchases. Estimation of market-size when no-purchases are unobservable has rarely been attempted in the marketing or revenue management literature. Indeed, we point out that it is akin to the classical statistical problem of estimating the parameters of a binomial distribution with unknown population size and success probability, and hence likely to be challenging. However, when the purchase probabilities are given by a functional form such as a multinomial-logit model, we propose an estimation heuristic that exploits the specification of the functional form, the variety of the offer sets in a typical RM setting, and qualitative knowledge of arrival rates. Finally we perform simulations to show that the estimator is very promising in obtaining unbiased estimates of population size and the model parameters.

Annual Report of the Iowa Department of Revenue FY2008

Annual Report of the Iowa Department of Revenue FY2008

Addendum to Annual Report of the Iowa Department of Revenue FY2008

Addendum to Annual Report of the Iowa Department of Revenue FY2008

Iowa Department of Revenue Performance Report, FY06

The Iowa Department of Revenue Performance Report is presented in accordance with the Accountable Government Act to improve decision-making and increase accountability to stakeholders and citizens of Iowa.

Iowa Department of Revenue Performance Report, FY07

The Iowa Department of Revenue Performance Report is presented in accordance with the Accountable Government Act to improve decision-making and increase accountability to stakeholders and citizens of Iowa.

Iowa Department of Revenue Performance Report, FY08

The Iowa Department of Revenue Performance Report is presented in accordance with the Accountable Government Act to improve decision-making and increase accountability to stakeholders and citizens of Iowa.

Report of the Iowa Department of Revenue for the year ended June 30, 2008

Report of the Iowa Department of Revenue for the year ended June 30, 2008