Novel Price Sensitive Demand Model with Empirical Results

Demand of commoditized products –for example drinking water and generic pharmaceutical products – depends mainly on price. Therefore, pricing strategy is very important especially for competing suppliers with common retailer. However, although many literatures were studied in pricing strategy, but almost all of those studies did not expand to use empirical data which is very important for applying the findings to real business.     To find the point that both manufacturers and a retailer can maximize their own profit in a supply chain, Game theory principle is used. This paper introduces a new parameter concept, the competition intensity of pricing degree, which is an important parameter that we incorporate in our linear price sensitive demand model in order to make the result more applicable. Moreover, we include a case study to show how the finding can be valuable tools for deciding on pricing policy. Keywords: Demand Model, Competition intensity of price degree, Bargaining Power, Game Theory, Pharmaceutica

On Approximating Four Covering and Packing Problems

In this paper, we consider approximability issues of the following four problems: triangle packing, full sibling reconstruction, maximum profit coverage and 2-coverage. All of them are generalized or specialized versions of set-cover and have applications in biology ranging from full-sibling reconstructions in wild populations to biomolecular clusterings; however, as this paper shows, their approximability properties differ considerably. Our inapproximability constant for the triangle packing problem improves upon the previous results; this is done by directly transforming the inapproximability gap of Haastad for the problem of maximizing the number of satisfied equations for a set of equations over GF(2) and is interesting in its own right. Our approximability results on the full siblings reconstruction problems answers questions originally posed by Berger-Wolf et al. and our results on the maximum profit coverage problem provides almost matching upper and lower bounds on the approximation ratio, answering a question posed by Hassin and Or.Comment: 25 page

Bounds of redundant multicast routing problem with SRLG-diverse constraints: edge, path and tree models

Mixed integer program, Multicast, Shared risk link groups,

Pattern-Based Variant-Best-Neighbors Respiratory Motion Prediction Using Orthogonal Polynomials Approximation

Motion-adaptive radiotherapy techniques are promising to deliver truly ablative radiation doses to tumors with minimal normal tissue exposure by accounting for real-time tumor movement. However, a major challenge of successful applications of these techniques is the real-time prediction of breathing-induced tumor motion to accommodate system delivery latencies. Predicting respiratory motion in real-time is challenging. The current respiratory motion prediction approaches are still not satisfactory in terms of accuracy and interpretability due to the complexity of breathing patterns and the high inter-individual variability across patients. In this paper, we propose a novel respiratory motion prediction framework which integrates four key components: a personalized monitoring window generator, an orthogonal polynomial approximation-based pattern library builder, a variant best neighbor pattern searcher, and a statistical prediction decision maker. The four functional components work together into a real-time prediction system and is capable of performing personalized tumor position prediction during radiotherapy. Based on a study of respiratory motion of 27 patients with lung cancer, the proposed prediction approach generated consistently better prediction performances than the current respiratory motion prediction approaches, particularly for long prediction horizons

Supply chain management and logistics : innovative strategies and practical solutions/ Edit.: Zee Liang

xxi, p.264.: ill.; 24 c

Supply chain management and logistics : innovative strategies and practical solutions/ Edit.: Zee Liang

xxi, p.264.: ill.; 24 c