A weighting scheme and a model for computing alternative pareto optimal solutions of multi objective linear programming problems

A new weighted order scheme is presented for finding alternative Pareto optimal solution of multi objective linear programming problems. In developing the weighted scheme, a big M model is proposed. We establish some properties of the ordering scheme and the proposed model and show how to determine proper values of the parameter M. Illustrative examples are worked out to show the effectiveness of the proposed approach and the competitiveness of the obtained solutions as compared to the ones obtained by the weighted maxmin and the weighted aggregation methods.6 page(s

A Triangular type-2 multi-objective linear programming model and a solution strategy

We consider a multi-objective linear programming model with type-2 fuzzy objectives. The considered model has the flexibility for the user to specify the more general membership functions for objectives to reflect the inherent fuzziness, while being simple and practical. We develop two solution strategies with reasonable computing costs. The additional cost, as compared to the type-1 fuzzy model, is indeed insignificant. These two algorithms compute Pareto optimal solutions of the type-2 problems, one being based on a maxmin approach and the other on aggregating the objectives. Finally, applying the proposed algorithms, we work out two illustrative examples.11 page(s

A new optimization approach for supply chain management models with quantity discount consideration

Owing to the difficulty of treating nonlinear functions, in many supply chain management (SCM) models, it has been assumed that the average prices of materials, production, transportation, and inventory are constant. This assumption, however, is not practical. Vendors usually offer quantity discounts to encourage the buyers to order more, and the producer intends to discount the unit production cost if the amount of production is large. In this article, we solves a nonlinear SCM model capable of treating various quantity discount functions simultaneously, including linear, single breakpoint, step, and multiple breakpoint functions. Then, we utilize the presented linearization techniques. So, such a nonlinear model is approximated to a linear mixed 0-1 program solvable to obtain a global optimum.8 page(s

Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital

Abstract Background The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney hospital. We developed and compared validated readmission risk scores using routinely collected hospital data to predict 7-day, 30-day and 60-day all-cause unplanned readmission. Methods A combination of gradient boosted tree algorithms for variable selection and logistic regression models was used to build and validate readmission risk scores using medical records from 62,235 live discharges from a metropolitan hospital in Sydney, Australia. Results The scores had good calibration and fair discriminative performance with c-statistic of 0.71 for 7-day and for 30-day readmission, and 0.74 for 60-day. Previous history of healthcare utilization, urgency of the index admission, old age, comorbidities related to cancer, psychosis, and drug-abuse, abnormal pathology results at discharge, and being unmarried and a public patient were found to be important predictors in all models. Unplanned readmissions beyond 7 days were more strongly associated with longer hospital stays and older patients with higher number of comorbidities and higher use of acute care in the past year. Conclusions This study demonstrates similar predictors and performance to previous risk scores of 30-day unplanned readmission. Shorter-term readmissions may have different causal pathways than 30-day readmission, and may, therefore, require different screening tools and interventions. This study also re-iterates the need to include more informative data elements to ensure the appropriateness of these risk scores in clinical practice

Additional file 1: Table S1. of Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital

Parameters of the Gradient Tree Boosting algorithm. In this study, we used the freely available gradient tree boosting algorithm implemented in the R package XGBoost with the following parameters chosen via manual tuning. Table S2. Conversion of continuous variables into categorical variables: cutting points for hospital length of stay (LOS), age (years), cumulative LOS (hours) in the previous year, days from last admission, number of pathology tests, number of pathology panels, hours since last surgery, hours since last panel and admission type. Table S3: Characteristics of patients and their hospital admissions for the study population. Main descriptive statistics. Table S4. Main categories of primary diagnosis (ICD10-AM) in our cohort. Table S5. Comorbidity groups in our cohort (Reference value = no comorbidity). Table S6. Pathology variables identified by the hospital laboratory in our cohort (Reference value = missing). Table S7. Calibration performance; Observed and expected rates for selected scores can be found in this table. Table S8. Sensitivity, specificity and PPV for different cut-off scores. (PDF 155 kb

Concept drift adaption for online anomaly detection in structural health monitoring

© 2019 Association for Computing Machinery. Despite its success for anomaly detection in the scenario where only data representing normal behavior are available, one-class support vector machine (OCSVM) still has challenge in dealing with non-stationary data stream, where the underlying distributions of data are time-varying. Existing OCSVM-based online learning methods incrementally update the model to address the challenge, however, they solely rely on the location relationship between a test sample and error support vectors. To better accommodate normal behavior evolution, online anomaly detection in non-stationary data stream is formulated as a concept drift adaptation problem in this paper. It is proposed that OCSVM-based incremental learning is only performed in the case of a normal drift. For an incoming sample, its relative relationship with three sets of vectors in OCSVM, namely margin support vectors, error support vectors, and reserve vectors is fully utilized to estimate whether a normal drift is emerging. Extensive experiments in the field of structural health monitoring have been conducted and the results have shown that the proposed simple approach outperforms the existing OCSVM-based online learning algorithms for anomaly detection