A Redesigned Benders Decomposition Approach for Large-Scale In-Transit Freight Consolidation Operations

The growth in online shopping and third party logistics has caused a revival of interest in finding optimal solutions to the large scale in-transit freight consolidation problem. Given the shipment date, size, origin, destination, and due dates of multiple shipments distributed over space and time, the problem requires determining when to consolidate some of these shipments into one shipment at an intermediate consolidation point so as to minimize shipping costs while satisfying the due date constraints. In this paper, we develop a mixed-integer programming formulation for a multi-period freight consolidation problem that involves multiple products, suppliers, and potential consolidation points. Benders decomposition is then used to replace a large number of integer freight-consolidation variables by a small number of continuous variables that reduces the size of the problem without impacting optimality. Our results show that Benders decomposition provides a significant scale-up in the performance of the solver. We demonstrate our approach using a large-scale case with more than 27.5 million variables and 9.2 million constraints

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations

Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016

BACKGROUND: Detailed assessments of mortality patterns, particularly age-specific mortality, represent a crucial input that enables health systems to target interventions to specific populations. Understanding how all-cause mortality has changed with respect to development status can identify exemplars for best practice. To accomplish this, the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) estimated age-specific and sex-specific all-cause mortality between 1970 and 2016 for 195 countries and territories and at the subnational level for the five countries with a population greater than 200 million in 2016. METHODS: We have evaluated how well civil registration systems captured deaths using a set of demographic methods called death distribution methods for adults and from consideration of survey and census data for children younger than 5 years. We generated an overall assessment of completeness of registration of deaths by dividing registered deaths in each location-year by our estimate of all-age deaths generated from our overall estimation process. For 163 locations, including subnational units in countries with a population greater than 200 million with complete vital registration (VR) systems, our estimates were largely driven by the observed data, with corrections for small fluctuations in numbers and estimation for recent years where there were lags in data reporting (lags were variable by location, generally between 1 year and 6 years). For other locations, we took advantage of different data sources available to measure under-5 mortality rates (U5MR) using complete birth histories, summary birth histories, and incomplete VR with adjustments; we measured adult mortality rate (the probability of death in individuals aged 15-60 years) using adjusted incomplete VR, sibling histories, and household death recall. We used the U5MR and adult mortality rate, together with crude death rate due to HIV in the GBD model life table system, to estimate age-specific and sex-specific death rates for each location-year. Using various international databases, we identified fatal discontinuities, which we defined as increases in the death rate of more than one death per million, resulting from conflict and terrorism, natural disasters, major transport or technological accidents, and a subset of epidemic infectious diseases; these were added to estimates in the relevant years. In 47 countries with an identified peak adult prevalence for HIV/AIDS of more than 0·5% and where VR systems were less than 65% complete, we informed our estimates of age-sex-specific mortality using the Estimation and Projection Package (EPP)-Spectrum model fitted to national HIV/AIDS prevalence surveys and antenatal clinic serosurveillance systems. We estimated stillbirths, early neonatal, late neonatal, and childhood mortality using both survey and VR data in spatiotemporal Gaussian process regression models. We estimated abridged life tables for all location-years using age-specific death rates. We grouped locations into development quintiles based on the Socio-demographic Index (SDI) and analysed mortality trends by quintile. Using spline regression, we estimated the expected mortality rate for each age-sex group as a function of SDI. We identified countries with higher life expectancy than expected by comparing observed life expectancy to anticipated life expectancy on the basis of development status alone. FINDINGS: Completeness in the registration of deaths increased from 28% in 1970 to a peak of 45% in 2013; completeness was lower after 2013 because of lags in reporting. Total deaths in children younger than 5 years decreased from 1970 to 2016, and slower decreases occurred at ages 5-24 years. By contrast, numbers of adult deaths increased in each 5-year age bracket above the age of 25 years. The distribution of annualised rates of change in age-specific mortality rate differed over the period 2000 to 2016 compared with earlier decades: increasing annualised rates of change were less frequent, although rising annualised rates of change still occurred in some locations, particularly for adolescent and younger adult age groups. Rates of stillbirths and under-5 mortality both decreased globally from 1970. Evidence for global convergence of death rates was mixed; although the absolute difference between age-standardised death rates narrowed between countries at the lowest and highest levels of SDI, the ratio of these death rates-a measure of relative inequality-increased slightly. There was a strong shift between 1970 and 2016 toward higher life expectancy, most noticeably at higher levels of SDI. Among countries with populations greater than 1 million in 2016, life expectancy at birth was highest for women in Japan, at 86·9 years (95% UI 86·7-87·2), and for men in Singapore, at 81·3 years (78·8-83·7) in 2016. Male life expectancy was generally lower than female life expectancy between 1970 and 2016, an

Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017.

BACKGROUND: Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. METHODS: The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries-Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and cause-specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised

Influence Estimation and Opinion-Tracking Over Online Social Networks

This article presents a restricted maximum likelihood-based algorithm to estimate who influences whose opinions and to what degree when agents share their opinions over large online social networks such as Twitter. The proposed algorithm uses multi-core processing and distributed computing to provide a scalable solution as the optimization problems are large in scale; a network with 10,000 agents and average connectivity of 100 requires estimates of about 1 million parameters. A computational study is then used to show that the estimates are efficient and robust when the full rank conditions for the covariance matrix are met. The results also highlight the importance of the quantity of the information being shared over the social network for the inference of the influence structure

A two-stage stochastic programming model for assortment optimisation

This paper presents a two-stage stochastic programming model for retail assortment optimisation. Decisions related to what products to stock and in what quantity are taken in the first stage while decisions related to what products to use to satisfy the primary versus the secondary demand are taken in the second stage. Such a model is very useful for online retailers and catalogue merchants who witness a gap between an order arrival and fulfilment. A case study illustrating the modelling approach and its benefits as compared to other existing assortment optimisation techniques is also presented

A Mathematical Foundation for Stochastic Opinion Dynamics

This article presents a stochastic opinion dynamics model where (a) the opinion of each agent in a network is modeled as a probability distribution as against a point object, (b) consensus is defined as the stability region of the ensuing set of stochastic difference equations, and (c) compromise solutions can be derived between agents who don't have a consensus. The model is well suited for tracking opinion dynamics over large online systems such as Twitter and Yelp where opinions need to be extracted from the user-generated text data. Theoretical conditions for the existence of consensus and the impact that stubborn agents have on opinion dynamics are also presented

Monitoring and prioritising alerts for exception analytics

Statistical quality control based approaches provide tools such as control charts for monitoring process parameters (usually 'physical layer') as well as rules such as the Western Electric Rules for detecting anomalies. The design of these charts is based on the statistical properties of the monitored parameters and the anomaly notification is related to the statistical significance of the observed change, the practical significance of the change, i.e. the sensitivity of system to the deviation in the parameter values, is not captured. Monitoring and alerting in the 'planning layers', however, is driven by the practical significance of the detected change as against the statistical significance. Recent developments in the field of supply chain event management have been focusing on providing tools and solutions for addressing these needs of the planning layers - these studies have, to a large extent been ad hoc. In this paper we propose and present parametric charts - a post-optimality-based approach for the design and implementation of monitoring and prioritising strategies for logistical anomalies. Analogous to the quality control charts, the parametric charts have four automatically generated regions, namely, the insensitive, the perturbation, the re-optimisation, and the infeasible, with each region having specific priorities and alarm levels associated with them. The bounds for the regions are based on the practical significance of the detected change as against the statistical significance of the deviation. The proposed approach offers three benefits: (1) it removes the ad hoc-ness associated with the determination of the practical significance as well as the priority given to the different classes of impacts that the system user observes, (2) the time consuming process of entering and fine-tuning the alarm bands and the priorities associated with the different alarm types when the exception analytics systems are installed can be decreased and automated, and (3) it aligns the exception analytics with the specific plans that are being executed. The step-by-step implementation of the charting approach is elucidated through a case study for the production unit at the FAME lab, at the Pennsylvania State University