The classical risk-neutral newsvendor problem is to decide the order quantity that maximizes the one-period expected profit. In this note, we consider a risk-averse newsvendor with stochastic price-dependent demand. We adopt Conditional Value-at-Risk (CVaR), a risk measure commonly used in finance, as the decision criterion. The aim of our study is to investigate the optimal pricing and ordering decisions in such a setting. For both additive and multiplicative demand models, we provide sufficient conditions for the uniqueness and existence of the optimal policy. Comparative statics show the monotonicity properties and other characteristics of the optimal pricing and ordering decisions. We also compare our results with those of the newsvendor with a risk-neutral attitude and a general utility function.

In response to the recognized need for high throughput biodosimetry methods for use after large scale radiological events, a logical approach is complete automation of standard biodosimetric assays that are currently performed manually. We describe progress to date on the RABIT (Rapid Automated BIodosimetry Tool), designed to score micronuclei or γ-H2AX fluorescence in lymphocytes derived from a single drop of blood from a fingerstick. The RABIT system is designed to be completely automated, from the input of the capillary blood sample into the machine, to the output of a dose estimate. Improvements in throughput are achieved through use of a single drop of blood, optimization of the biological protocols for in-situ analysis in multi-well plates, implementation of robotic plate and liquid handling, and new developments in high-speed imaging. Automating well-established bioassays represents a promising approach to highthroughput radiation biodosimetry, both because high throughputs can be achieved, but also because the time to deployment is potentially much shorter than for a new biological assay. Here we describe the development of each of the individual modules of the RABIT system, and show preliminary data from key modules. Ongoing is system integration, followed by calibration and validation.

The immunofluorescence-based detection of γ-H2AX is a reliable and sensitive method for quantitatively measuring DNA double-strand breaks (DSBs) in irradiated samples. Since H2AX phosphorylation is highly linear with radiation dose, this well-established biomarker is in current use in radiation biodosimetry. At the Center for High-Throughput Minimally Invasive Radiation Biodosimetry, we have developed a fully automated high-throughput system, the RABIT (Rapid Automated Biodosimetry Tool), that can be used to measure γ-H2AX yields from fingerstickderived samples of blood. The RABIT workstation has been designed to fully automate the γ-H2AX immunocytochemical protocol, from the isolation of human blood lymphocytes in heparincoated PVC capillaries to the immunolabeling of γ-H2AX protein and image acquisition to determine fluorescence yield. High throughput is achieved through the use of purpose-built robotics, lymphocyte handling in 96-well filter-bottomed plates, and high-speed imaging. The goal of the present study was to optimize and validate the performance of the RABIT system for the reproducible and quantitative detection of γ-H2AX total fluorescence in lymphocytes in a multiwell format. Validation of our biodosimetry platform was achieved by the linear detection of a dose-dependent increase in γ-H2AX fluorescence in peripheral blood samples irradiated ex vivo with γ rays over the range 0 to 8 Gy. This study demonstrates for the first time the optimization and use of our robotically based biodosimetry workstation to successfully quantify γ-H2AX total fluorescence in irradiated peripheral lymphocytes.

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The newsvendor problem with pricing decision provides an important vehicle for examining how operational problems interacts with marketing issues to influence decision-making at the firm level. This paper considers the newsvendor problem with pricing and its extensions. We try to answer two questions: (1) how to model the price-dependent stochastic demand and (2) how to derive the combined pricing and inventory solutions. We identify a class of demand functions which can result in well behaved profit functions. A demand function can be divided into two parts: the mean demand and random demand. If the part of mean demand has increasing price elasticity (IPE) and the part of random demand has generalized strict increasing failure rate (GSIFR), then the expected profit of the newsvendor is unimodal or quasi-concave. This appears generalizing the existent models in the literature.

We consider a joint inventory-pricing control problem for a periodic-review, single-stage inventory system with a positive order leadtime and a linear order cost. Demands in consecutive periods are independent, but their distributions depend on the price in accordance with a stochastic demand function of additive form. Pricing and ordering decisions are made simultaneously at the beginning of each period. The objective is to maximize the total expected discounted profit over a finite horizon. We partially characterize the structure of the optimal joint ordering and pricing policies. We also show that our structural analysis can be extended to a multistage (or serial) inventory system with constant or stochastic leadtimes and an assemble-to-order system with price-sensitive demand.