Dynamic pricing model and algorithm for perishable products with fuzzy demand (ABS 2* Journal)

This paper studies the dynamic pricing problem of selling fixed stock of perishable items over a finite horizon, where the decision maker does not have the necessary historic data to estimate the distribution of uncertain demand, but has imprecise information about the quantity demand. We model this uncertainty using fuzzy variables. The dynamic pricing problem based on credibility theory is formulated using three fuzzy programming models, viz.: the fuzzy expected revenue maximization model, a-optimistic revenue maximization model, and credibility maximization model. Fuzzy simulations for functions with fuzzy parameters are given and embedded into a genetic algorithm to design a hybrid intelligent algorithm to solve these three models. Finally, a real-world example is presented to highlight the effectiveness of the developed model and algorithm.

Dynamic pricing for used products in remanufacturing with back orders(ABS 3* Journal, Accepted with Revision on 2nd December 2011).

In remanufacturing, the supply of used products and the demand for remanufactured products are usually mismatched because of the great uncertainties on both sides. In this paper, we propose a dynamic pricing policy to balance this uncertain supply and demand. Specifically, we study a remanufacturer’s problem of pricing a single class of cores with random price-dependent returns and random demand for the remanufactured products with backlogs. We model this pricing task as a continuous-time Markov decision process, which addresses both the finite and infinite horizon problems, and provide managerial insights by analyzing the structural properties of the optimal policy. We then use several computational examples to illustrate the impacts of particular system parameters on pricing policy.

Correlation between ΔAbs, ΔRGB (red) and stearic acid destruction rates using commercial self-cleaning glass as the photocatalyst

A resazurin (Rz) based photocatalyst indicator ink is used to test the activity of a commercial self-cleaning glass, using UV–vis spectroscopy and digital photography to monitor the photocatalyst-driven change in colour of the ink. UV–vis spectroscopy allows the change in film absorbance, ΔAbs, to be monitored as a function of irradiation time, whereas digital photography is used to monitor the concomitant change in the red component of the RGB values, i.e. ΔRGB (red). Initial work reveals the variation in ΔAbst and ΔRGB (red)t as a function of irradiation time, t, are linearly correlated. The rates of change of these parameters are also linearly correlated to the rates of oxidative destruction of stearic acid on self-cleaning glass under different irradiances. This work demonstrates that a measure of photocatalyst activity of self-cleaning glass, i.e. the time taken to change the colour of an Rz photocatalyst indicator ink, can be obtained using inexpensive digital photography, as alternative to more expensive lab-based techniques, such as UV–vis spectrophotometry.

DocLite: A Docker-Based Lightweight Cloud Benchmarking Tool

Existing benchmarking methods are time consuming processes as they typically benchmark the entire Virtual Machine (VM) in order to generate accurate performance data, making them less suitable for real-time analytics. The research in this paper is aimed to surmount the above challenge by presenting DocLite - Docker Container-based Lightweight benchmarking tool. DocLite explores lightweight cloud benchmarking methods for rapidly executing benchmarks in near real-time. DocLite is built on the Docker container technology, which allows a user-defined memory size and number of CPU cores of the VM to be benchmarked. The tool incorporates two benchmarking methods - the first referred to as the native method employs containers to benchmark a small portion of the VM and generate performance ranks, and the second uses historic benchmark data along with the native method as a hybrid to generate VM ranks. The proposed methods are evaluated on three use-cases and are observed to be up to 91 times faster than benchmarking the entire VM. In both methods, small containers provide the same quality of rankings as a large container. The native method generates ranks with over 90% and 86% accuracy for sequential and parallel execution of an application compared against benchmarking the whole VM. The hybrid method did not improve the quality of the rankings significantly.