Matematičko modeliranje u svrhu predviđanja i optimiranja zavarivačke kupke kod TIG zavarivanja

In this work, nonlinear and multi-objective mathematical models were developed to determine the process parameters corresponding to optimum weld pool geometry. The objectives of the developed mathematical models are to maximize tensile load (TL), penetration (P), area of penetration (AP) and/or minimize heat affected zone (HAZ), upper width (UW) and upper height (UH) depending upon the requirements.Razvijeni su nelinearni i multi-objektni matematički modeli da bi odredili parametre s optimalnom geometrijom zavarivačke kupke. Cilj razvijanja matematičkih modela je postići maksimalna vlačna čvrstoća, penetracija, područje pretaljivanja i/ili minimalna zona utjecaja topline, širina i nadvišenje zavara ovisno o postavljenim zahtjevima

Ensemble-based methods for forecasting census in hospital units

BACKGROUND: The ability to accurately forecast census counts in hospital departments has considerable implications for hospital resource allocation. In recent years several different methods have been proposed forecasting census counts, however many of these approaches do not use available patient-specific information. METHODS: In this paper we present an ensemble-based methodology for forecasting the census under a framework that simultaneously incorporates both (i) arrival trends over time and (ii) patient-specific baseline and time-varying information. The proposed model for predicting census has three components, namely: current census count, number of daily arrivals and number of daily departures. To model the number of daily arrivals, we use a seasonality adjusted Poisson Autoregressive (PAR) model where the parameter estimates are obtained via conditional maximum likelihood. The number of daily departures is predicted by modeling the probability of departure from the census using logistic regression models that are adjusted for the amount of time spent in the census and incorporate both patient-specific baseline and time varying patient-specific covariate information. We illustrate our approach using neonatal intensive care unit (NICU) data collected at Women & Infants Hospital, Providence RI, which consists of 1001 consecutive NICU admissions between April 1st 2008 and March 31st 2009. RESULTS: Our results demonstrate statistically significant improved prediction accuracy for 3, 5, and 7 day census forecasts and increased precision of our forecasting model compared to a forecasting approach that ignores patient-specific information. CONCLUSIONS: Forecasting models that utilize patient-specific baseline and time-varying information make the most of data typically available and have the capacity to substantially improve census forecasts

A combination of deterministic and stochastic approaches to optimize bed capacity in a hospital unit

PubMedID: 18280609Random number of arrivals and random length of stays make the number of patients in a hospital unit behave as a stochastic process. This makes the determination of the optimum size of the bed capacity more difficult. The number of admissions per day, service level and occupancy level are key control parameters that affect the optimum size of the required bed capacity. In this study a new stochastic approximation is developed and applied to a unit of a teaching hospital. Data between 2000 and 2004 was used to obtain the necessary probability distribution functions. Mathematical relationships between the control parameters and size of the bed capacity are obtained using generated data from a constructed simulation model. Nonlinear mathematical models are then used to determine the optimum size of the required bed capacity based on target levels of the control parameters, and a profit and loss analysis is performed. © 2008 Elsevier Ireland Ltd. All rights reserved

A new approximation for inventory control system with decision variable lead-time and stochastic demand

Demand for any material in a hospital depends on a random arrival rate and random length of stay in units. Therefore, the demand for any material shows stochastic characteristics that make determining the optimum level of r and Q problem more difficult. Thus, in this study, a single item inventory system for healthcare was developed using a continuous review (r, Q) policy. A simulation meta-model was constructed to obtain equations for the average on-hand inventory and average number of orders per year. Then, the equations were used to determine the optimal levels of r and Q while minimizing the total cost in an integer non-linear model. The same problem investigated in this study was also solved using OptQuest optimization software. © INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING

Optimization of passive optical network planning

Network planning problem typically involves large capital investment and can be formulated as an optimization problem where the objective is minimization of the first installed cost. We consider a passive optical network (PON) planning problem based on a residential area in Adana (Turkey). There are four possible primary node locations, twenty possible secondary node locations, and twenty-eight customers. We use genetic algorithm and mathematical modeling techniques to optimize the position of the primary and secondary nodes, their split levels and assigning customers to secondary nodes and secondary nodes to primary nodes under some constraints such as system's attenuations and technical characteristics of all the equipment. © 2011 Elsevier Inc.The authors are grateful to Turkish Telecom Company that generously provided time and access to their operations for this study and they also thank Professors Necdet Geren, Melih Bayramoglu, and Naki Tutuncu of Cukurova University for their valuable comments on this paper. This study has been sponsored by the University of Cukurova

Integrated analytical hierarch process and mathematical programming to supplier selection problem with quantity discount

In this article an integration of analytical hierarchy process and non-linear integer and multi-objective programming under some constraints such as quantity discounts, capacity, and budget is applied to determine the best suppliers and to place the optimal order quantities among them. This integration-based multi-criteria decision making methodology takes into account both qualitative and quantitative factors in supplier selection. While the analytical hierarchy process matches item characteristics with supplier characteristics, non-linear integer programming model analytically determines the best suppliers and the optimal order quantities among the determined suppliers. The objectives of the mathematical models constructed are maximizing the total value of purchase (TVP), minimizing the total cost of purchase (TCP) or maximizing TVP and minimizing TCP simultaneously. In addition, several "what if" scenarios are facilitated and the quality of the resulting models is evaluated on real-life data. © 2008 Elsevier Inc. All rights reserved

Optimizing nurse capacity in a teaching hospital neonatal intensive care unit

PubMedID: 26729324Patients in intensive care units need special attention. Therefore, nurses are one of the most important resources in a neonatal intensive care unit. These nurses are required to have highly specialized training. The random number of patient arrivals, rejections, or transfers due to lack of capacity (such as nurse, equipment, bed etc.) and the random length of stays, make advanced knowledge of the optimal nurse a requirement, for levels of the unit behave as a stochastic process. This stochastic nature creates difficulties in finding optimal nurse staffing levels. In this paper, a stochastic approximation which is based on the required nurse: patient ratio and the number of patients in a neonatal intensive care unit of a teaching hospital, has been developed. First, a meta-model was built to generate simulation results under various numbers of nurses. Then, those experimented data were used to obtain the mathematical relationship between inputs (number of nurses at each level) and performance measures (admission number, occupation rate, and satisfaction rate) using statistical regression analysis. Finally, several integer nonlinear mathematical models were proposed to find optimal nurse capacity subject to the targeted levels on multiple performance measures. The proposed approximation was applied to a Neonatal Intensive Care Unit of a large hospital and the obtained results were investigated. © 2016, Springer Science+Business Media New York

An asymptotic approach for a semi-Markovian inventory model of type (s, S)

In this study, we constructed a stochastic process (X(t)) that expresses a semi-Markovian inventory model of type (s, S) and it is shown that this process is ergodic under some weak conditions. Moreover, we obtained exact and asymptotic expressions for the nth order moments (n = 1,2,3,.) of ergodic distribution of the process X(t), as S - s › ?. Finally, we tested how close the obtained approximation formulas are to the exact expressions. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd

Optimal control of work-in-process inventory of a two-station production line

Most production lines keep a minimal level of inventory stock to save storage costs and buffer space. However, the random nature of processing, breakdown, and repair times can significantly affect the efficiency of a production line and force the stocking of work-in-process inventory. We are interested in the case when starvation and blockage are preferentially avoided. In this study, a mathematical model has been developed using asymptotic approximation and simulation that provides asymptotic results for the expected value and the variance of the stock level in a buffer as a function of time. In addition, the functional relationship between buffer capacity and the first stopping time caused by starvation or blockage has been determined. Copyright © 2009 John Wiley & Sons, Ltd