An Inventory Model with Planned Shortages and Price Dependent Demand

Previous research has yielded a procedure for a retailer to determine the optimal lot size and selling price when a supplier offers all-unit quantity discounts and demand is a decreasing function of price. In this paper, we extend that research by allowing for shortages. An algorithm is presented that determines the optimal lot size, order level, and selling price for a class of demand functions, including the constant price-elasticity and linear demand functions.     

Assessing the language of 2 year-olds: From theory to practice

Early screening for language problems is a priority given the importance of language for success in school and interpersonal relationships. The paucity of reliable behavioral instruments for this age group prompted the development of a new touchscreen language screener for 2-year-olds that relies on language comprehension. Developmental literature guided selection of age-appropriate markers of language disorder risk that are culturally and dialectally neutral and could be reliably assessed. Items extend beyond products of linguistic knowledge (vocabulary and syntax) and tap the process by which children learn language, also known as fast mapping. After piloting an extensive set of items (139), two phases of testing with over 500 children aged 2; 0–2; 11 were conducted to choose the final 40-item set. Rasch analysis was used to select the best fitting and least redundant items. Norms were created based on 270 children. Sufficient test-retest reliability, Cronbach's alpha, and convergent validity with the MB-CDI and PPVT are reported. This quick behavioral measure of language capabilities could support research studies and facilitate the early detection of language problems.     

A Comparison of Selected Artificial Neural Networks that Help Auditors Evaluate Client Financial Viability

This study compares the performance of three artificial neural network (ANN) approaches—backpropagalion, categorical learning, and probabilistic neural network—as classification tools to assist and support auditor's judgment about a client's continued financial viability into the future (going concern status). ANN performance is compared on the basis of overall error rates and estimated relative costs of misclassificaticn (incorrectly classifying an insolvent firm as solvent versus classifying a solvent firm as insolvent). When only the overall error rate is considered, the probabilistic neural network is the most reliable in classification, followed by backpropagation and categorical learning network. When the estimated relative costs of misclassification are considered, the categorical learning network is the least costly, followed by backpropagation and probabilistic neural network.