886 research outputs found
Uncertainty Management in Simulation-Optimization of Complex Systems : Algorithms and Applications
No full textConnections Among Optimization Models with Uncertainties, ABC and RBV
No full textOptimization models (in particular, with uncertainties) and Activity Based Costing (ABC) and Resource Based View (RBV) of the firm theory are usually separated in theory and applications. We analyze the connections among them, with a focus on stochastic programming, showing how they can all be used as decision support instruments for strategic planning problems, especially concerning resources and services (or products). Such connections produce a multiplicative positive effect as we can use the potential of mathematical modeling with data from the ABC system and a taxonomy of resources from the RBV: the latter can also help us for the interpretation of results after applying stochastic (or mathematical) models and solution methods
Metamodel-based robust simulation-optimization:An overview
No full textOptimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a "robust" methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world but replaces his statistical techniques by design and analysis of simulation experiments based on Kriging (Gaussian process model); moreover, we use bootstrapping to quantify the variability in the estimated Kriging metamodels. In addition, we combine Kriging with nonlinear programming, and we estimate the Pareto frontier. We illustrate the resulting methodology through economic order quantity (EOQ) inventory models. Our results suggest that robust optimization requires order quantities that differ from the classic EOQ. We also compare our results with results we previously obtained using response surface methodology instead of Kriging
Simulation-optimization under uncertainty through metamodeling and bootstrapping
No full textMost methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi's world view with either regression or Kriging (Gaussian Process) metamodels (response surfaces). These metamodels are combined with Non-Linear Mathematical Programming (NLMP) to find a robust optimal solution. Varying the constraint values in the NLMP model gives an estimated Pareto frontier. To account for the variability of the estimated Pareto frontier, this research uses bootstrapping which gives confidence regions for the robust optimal solution. This methodology is illustrated through the Economic Order Quantity (EOQ) inventory-management model, accounting for the uncertainties in the demand rate and the cost coefficients
Modelli di ottimizzazione combinatoria per problemi di coordinamento nei sistemi di produzione multi-stadio
No full textModelli di ottimizzazione combinatoria per problemi di coordinamento nei sistemi di produzione multi-stadio
No full textAn Evolutionary Algorithm for the Sequence Coordination in Furniture Production
No full textIn the material flow of a plant, parts are grouped in batches, each having as attributes the shape and the color. In both departments, a changeover occurs when the attribute of a new part changes. The problem consists in finding a common sequence of batches optimizing an overall utility index. A metaheuristic approach is presented which allows to solve a set of real-life instances and performs satisfactorily on a large sample of experimental data
Radiofrequency ablation of liver tumors: the role of microbubble ultrasound contrast agents
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Radiofrequency ablation of liver tumors: the role of microbubble ultrasound contrast agents.
Author(s): Meloni, Maria Franca; Livraghi, Tito; Filice, Carlo; Lazzaroni, Sergio; Calliada, Fabrizio; Perretti, Leonardo
Source: Ultrasound quarterly Volume: 22 Issue: 1 Pages: 41-7 Published: 2006-Mar
[ PubMed Related Articles ]
Abstract: Radiofrequency ablation (RFA) is currently indicated for the treatment of primary and metastatic hepatic malignancies. Real-time ultrasound (US) is generally used during the procedure to guide electrode placement, but for evaluating the results of treatment, contrast-enhanced computed tomography and magnetic resonance imaging have traditionally been considered more effective. This view has changed, however, with the recent development of contrast-enhanced ultrasound (CEUS) (eg, using sulfur hexafluoride microbubbles), which can provide valuable information on the effects of RFA more rapidly and economically than computed tomography or magnetic resonance imaging without exposing the patient to ionizing radiation. In our center, CEUS is performed in patients with liver tumors before and immediately after RFA, in selected cases during the procedure as well, and in the follow-up. Between January 2003 and June 2005, we performed CEUS on 350 patients scheduled for RFA of primary or metastatic liver tumors. In 14 (13.4%) of the 96 patients whose disease was metastatic, CEUS revealed lesions that had been missed on the conventional US examination. In most of these cases, the result was a more complete treatment performed under CEUS guidance. In the remaining 2 (14%) of 14, the results of the examination allowed us to avoid subjecting the patient to useless treatment. In our experience, the use of CEUS also improved the management and follow-up of patients undergoing interstitial therapy
Expected Shortfall of the Makespan in Interval-Valued Activity Networks
No full textThis paper deals with the analysis of the Expected Shortfall of the makespan in activity
networks when uncertain activity durations are modeled by intervals. More precisely, for
each activity only the interval for its integer valued duration is known to the decision
maker. In this scenario, we address the evaluation of the Expected Shortfall associated to
a feasible schedule discussing its role and importance in decision support systems for
planning and scheduling applications. We propose heuristics to determine a fast
estimation of the Expected Shortfall. The computational results show that it can be used
as optimization criterion for a wide class of planning and scheduling problems while
modeling the risk-averse behavior of a decision maker
An agent based approach to the real time air traffic control
No full textCongestion in the air traffic network is a problem with an increasing relevance for airlines costs as well as airspace safety. One of the major factors is the limited operative capacity of the air network. in this work an agent based approach to the real time air traffic control is proposed. The air network is considered partitioned in different sectors. Each sector has its own decision agent devoted to the air traffic control involved in. in each of these sectors, in order to guarantee the respect of both delay and capacity constraints, a real time scheduling of the flights is obtained by an iterative procedure based on a specific graph model. Copyright © 2005 IFAC
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