Splitting hybrid Make-To-Order and Make-To-Stock demand profiles

In this paper a demand time series is analysed to support Make-To-Stock (MTS) and Make-To-Order (MTO) production decisions. Using a purely MTS production strategy based on the given demand can lead to unnecessarily high inventory levels thus it is necessary to identify likely MTO episodes. This research proposes a novel outlier detection algorithm based on special density measures. We divide the time series' histogram into three clusters. One with frequent-low volume covers MTS items whilst a second accounts for high volumes which is dedicated to MTO items. The third cluster resides between the previous two with its elements being assigned to either the MTO or MTS class. The algorithm can be applied to a variety of time series such as stationary and non-stationary ones. We use empirical data from manufacturing to study the extent of inventory savings. The percentage of MTO items is reflected in the inventory savings which were shown to be an average of 18.1%.Comment: demand analysis; time series; outlier detection; production strategy; Make-To-Order(MTO); Make-To-Stock(MTS); 15 pages, 9 figure

Balanced dynamic multiple travelling salesmen: algorithms and continuous approximations

Dynamic routing occurs when customers are not known in advance, e.g. for real-time routing. Two heuristics are proposed that solve the balanced dynamic multiple travelling salesmen problem (BD-mTSP). These heuristics represent operational (tactical) tools for dynamic (online, real-time) routing. Several types and scopes of dynamics are proposed. Particular attention is given to sequential dynamics. The balanced dynamic closest vehicle heuristic (BD-CVH) and the balanced dynamic assignment vehicle heuristic (BD-AVH) are applied to this type of dynamics. The algorithms are tested for instances in the Euclidean plane. Continuous approximation models for the BD-mTSP's are derived and serve as strategic tools for dynamic routing. The models express route lengths using vehicles, customers and dynamic scopes without the need of running an algorithm. A machine learning approach was used to obtain regression models. The mean-average-percentage error of two of these models is below 3%.Comment: 15 pages, 10 figures, 7 tables, 2 heuristics, 3 CAM model

Dynamic lot size MIPs for multiple products and ELSPs with shortages, capacity and changeover limits

Scheduling multiple products with limited resources and varying demands remain a critical challenge for many industries. This work presents mixed integer programs (MIPs) that solve the Economic Lot Sizing Problem (ELSP) and other Dynamic Lot-Sizing (DLS) models with multiple items. DLS systems are classified, extended and formulated as MIPs. Especially, logical constraints are a key ingredient in succeeding in this endeavour. They were used to formulate the setup/changeover of items in the production line. Minimising the holding, shortage and setup costs is the primary objective for ELSPs. This is achieved by finding an optimal production schedule taking into account the limited manufacturing capacity. Case studies for a production plants are used to demonstrate the functionality of the MIPs. Optimal DLS and ELSP solutions are given for a set of test-instances. Insights into the runtime and solution quality are given.Comment: 14 pages, 6 figure

Fast Decliner Phenotype of Chronic Obstructive Pulmonary Disease (COPD) : Applying Machine Learning for Predicting Lung Function Loss

Acknowledgements We acknowledge patients for allowing their data to be used for surveillance and research. Practices who have agreed to be part of the RCGP RSC and allow us to extract and used health data for surveillance and research. Ms. Filipa Ferreira from RCGP and Mr. Julian Sherlock from the University of Surrey. Apollo Medical Systems for data extraction. Collaboration with EMIS, TPP, In-Practice and Micro-test CMR supplier for facilitating data extraction. Colleagues at Public Health England. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectorsPeer reviewedPublisher PD

Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease

The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to facilitate the objectivization of AD assessment. Here, we present a novel framework combining Riemannian tangent space mapping and elastic net regression for the development of brain atrophy markers. While most AD QEEG studies are based on small sample sizes and psychological test scores as outcome measures, here we train and test our models using data of one of the largest prospective EEG AD trials ever conducted, including MRI biomarkers of brain atrophy.Comment: Presented at NIPS 2017 Workshop on Machine Learning for Healt

Allergen-induced asthmatic responses modified by a GATA3-specific DNAzyme

BACKGROUND : The most prevalent phenotype of asthma is characterized by eosinophil-dominated inflammation that is driven by a type 2 helper T cell (Th2). Therapeutic targeting of GATA3, an important transcription factor of the Th2 pathway, may be beneficial. We evaluated the safety and efficacy of SB010, a novel DNA enzyme (DNAzyme) that is able to cleave and inactivate GATA3 messenger RNA (mRNA). METHODS : We conducted a randomized, double-blind, placebo-controlled, multicenter clinical trial of SB010 involving patients who had allergic asthma with sputum eosinophilia and who also had biphasic early and late asthmatic responses after laboratory-based allergen provocation. A total of 40 patients could be evaluated; 21 were assigned to receive 10 mg of SB010, and 19 were assigned to receive placebo, with each study drug administered by means of inhalation once daily for 28 days. An allergen challenge was performed before and after the 28-day period. The primary end point was the late asthmatic response as quantified by the change in the area under the curve (AUC) for forced expiratory volume in 1 second (FEV1). RESULTS : After 28 days, SB010 attenuated the mean late asthmatic response by 34%, as compared with the baseline response, according to the AUC for FEV1, whereas placebo was associated with a 1% increase in the AUC for FEV1 (P = 0.02). The early asthmatic response with SB010 was attenuated by 11% as measured by the AUC for FEV1, whereas the early response with placebo was increased by 10% (P = 0.03). Inhibition of the late asthmatic response by SB010 was associated with attenuation of allergen-induced sputum eosinophilia and with lower levels of tryptase in sputum and lower plasma levels of interleukin-5. Allergen-induced levels of fractional exhaled nitric oxide and airway hyperresponsiveness to methacholine were not affected by either SB010 or placebo. CONCLUSIONS : Treatment with SB010 significantly attenuated both late and early asthmatic responses after allergen provocation in patients with allergic asthma. Biomarker analysis showed an attenuation of Th2-regulated inflammatory responses

Age at Menarche and Its Association with the Metabolic Syndrome and Its Components: Results from the KORA F4 Study

OBJECTIVE: The metabolic syndrome is a major public health challenge and identifies persons at risk for diabetes and cardiovascular disease. The aim of this study was to examine the association between age at menarche and the metabolic syndrome (IDF and NCEP ATP III classification) and its components. DESIGN: 1536 women aged 32 to 81 years of the German population based KORA F4 study were investigated. Data was collected by standardized interviews, physical examinations, and whole blood and serum measurements. RESULTS: Young age at menarche was significantly associated with elevated body mass index (BMI), greater waist circumference, higher fasting glucose levels, and 2 hour glucose (oral glucose tolerance test), even after adjusting for the difference between current BMI and BMI at age 25. The significant effect on elevated triglycerides and systolic blood pressure was attenuated after adjustment for the BMI change. Age at menarche was inversely associated with the metabolic syndrome adjusting for age (p-values: <0.001 IDF, 0.003 NCEP classification) and additional potential confounders including lifestyle and reproductive history factors (p-values: 0.001, 0.005). Associations remain significant when additionally controlling for recollected BMI at age 25 (p-values: 0.008, 0.033) or the BMI change since age 25 (p-values: 0.005, 0.022). CONCLUSION: Young age at menarche might play a role in the development of the metabolic syndrome. This association is only partially mediated by weight gain and increased BMI. A history of early menarche may help to identify women at risk for the metabolic syndrome