scholarly journals Fusegates selection and operation: simulation–optimization approach

2011 ◽  
Vol 14 (2) ◽  
pp. 464-477 ◽  
Author(s):  
Abbas Afshar ◽  
Zeinab Takbiri
Keyword(s):  
Simulation Optimization ◽  
Cost Effective ◽  
Unit Price ◽  
Sensitivity Analyses ◽  
Optimization Approach ◽  
Hydraulic Simulation ◽  
Highly Nonlinear ◽  
Mixed Genetic Algorithm ◽  
Design Characteristics ◽  
Operational Constraints

Fusegates present a reliable and cost-effective alternative, which increase flood protection and water supply benefits. This article develops a comprehensive simulation–optimization framework for practical selection, installation, and operation of fusegates. The computational model simulates the complicated hydraulic behavior of fusegates systems with varying design characteristics and consequential anomalous routing process in case of flood events. An efficient mixed genetic algorithm (GA) is subsequently developed and coupled with the highly nonlinear hydraulic simulation model to minimize the overall expected annual cost under structural, hydraulic, and operational constraints. Types, heights, and tipping heads of gates are explicitly treated as optimization decision variables. Furthermore, the frequent practice of installing all gates in the same level is practically improved to favorably help minimize water loss in case of moderate discharge floods. The proposed model is demonstrated and discussed for a case study of the Taleghan Dam fusegates installation project in Iran. A series of sensitivity analyses are also conducted to assess routing effect and uncertainty in water unit price and replacement costs and provide more insight and understanding of the design problem.

scholarly journals A delayed differentiation multiproduct model with the outsourcing of common parts, overtime strategy for end products, and quality reassurance

2021 ◽  
pp. 143-158 ◽  
Author(s):  
Singa Wang Chiu ◽  
Victoria Chiu ◽  
Ming-Hon Hwang ◽  
Yuan-Shyi Peter Chiu
Keyword(s):  
Cost Effective ◽  
Sensitivity Analyses ◽  
Optimization Approach ◽  
Limited Capacity ◽  
Numerical Illustration ◽  
Delayed Differentiation ◽  
Modeling Analysis ◽  
End Products ◽  
Effective Decision ◽  
Effective Decision Making

Production planners today must simultaneously face with the time and quality demands of various goods externally and meet limited capacity internally. This study presents a two-stage delayed- differentiation multiproduct model that considers the outsourcing options for common parts, overtime strategy for end products, and quality reassurance to assist in making fabrication runtime decisions that are cost-effective. Stage one produces all necessary common intermediate components for end products. To reduce stage one’s utilization/uptime, this study adopts a partial outsourcing option. Stage two uses an overtime strategy to fabricate end products that further shorten the uptime. The production processes in both phases are assumed to be imperfect. This study employs the reworking/scrapping of random faulty items to reassure product quality. The researchers build a model to depict the proposed problem’s characteristics and used the mathematical modeling, analysis, and optimization approach to determine the best rotation cycle length that minimizes the system’s expenses. Further, in this study, the researchers provide sensitivity analyses and a numerical illustration, which validate the result’s applicability and exhibit its capability. This result contributes to practical multiproduct-fabrication by (1) deriving the optimal manufacturing policy for a delayed-differentiation multiproduct system with dual uptime reduction policies and quality reassurance; and (2) offering a decisional model that allows production planners to explore the collective/separate effect of a quality-ensured and dual uptime reduction strategy on a problem’s operating policy and crucial system performance indicators, which assists in cost-effective decision-making.

Simultaneous Determination of Disassembly Sequence and Disassembly-to-Order Decisions Using Simulation Optimization

2016 ◽  
Vol 138 (10) ◽  
Author(s):  
Mehmet Ali Ilgin ◽  
Gökçeçiçek Tuna Taşoğlu
Keyword(s):  
Simultaneous Determination ◽  
Simulation Optimization ◽  
Public Awareness ◽  
Cost Effective ◽  
Product Recovery ◽  
Optimization Approach ◽  
Order Problem ◽  
Disassembly Sequence ◽  
Disassembly Sequencing

Strict environmental regulations and increasing public awareness toward environmental issues force many companies to establish dedicated facilities for product recovery. All product recovery options require some level of disassembly. That is why, the cost-effective management of product recovery operations highly depends on the effective planning of disassembly operations. There are two crucial issues common to most disassembly systems. The first issue is disassembly sequencing which involves the determination of an optimal or near optimal disassembly sequence. The second issue is disassembly-to-order (DTO) problem which involves the determination of the number of end of life (EOL) products to process to fulfill the demand for specified numbers of components and materials. Although disassembly sequencing decisions directly affects the various costs associated with a disassembly-to-order problem, these two issues are treated separately in the literature. In this study, a genetic algorithm (GA) based simulation optimization approach was proposed for the simultaneous determination of disassembly sequence and disassembly-to-order decisions. The applicability of the proposed approach was illustrated by providing a numerical example and the best values of GA parameters were identified by carrying out a Taguchi experimental design.

scholarly journals Pharmacoeconomics of three therapeutic schemes for anti-tuberculosis therapy induced liver injury in China

Open Medicine ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Yu Chen ◽  
Peng Ye ◽  
Chongwu Ren ◽  
Pengfei Ren ◽  
Zheng Ma ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Liver Injury ◽  
Controlled Trial ◽  
Cost Effective ◽  
Unit Price ◽  
Sensitivity Analyses ◽  
Expert Advice ◽  
Cost Effectiveness Ratio ◽  
Tree Model ◽  
The Cost

AbstractTo evaluate the pharmacoeconomics of three therapeutic schemes in treating anti-tuberluosis therapy -induced liver injury (anti-TB DILI).MethodsIn the construction of a decision tree model, the efficacy and safety parameters came from the results of the randomized, controlled trial conducted here, the effect parameters were derived from expert advice, and the cost parameters, such as usage specification, number, and unit price, came from literature, expert advice, and so on.ResultsThe cost-effectiveness analysis (CEA) based on the effect degrees showed that bicyclol had the best effect (4.63562). The incremental cost-effectiveness ratio (ICER) (206.03270) of bicyclol was the lowest. The cost-effectiveness ratio of silibinin was the lowest (68.59987). The CEA based on the complete normalization rate showed that bicyclol had the highest complete normalization rate (83.562%), the lowest cost-effectiveness ratio (4.63627), and the smallest ICER (4.63504). Sensitivity analyses proved the robustness of the results.ConclusionsBicyclol is the most cost-effective therapy and the preferred choice for treating anti-TB DILI.

scholarly journals Lost in Optimization of Water Distribution Systems: Better Call Bayes

2022 ◽  
Author(s):  
Antonio Candelieri ◽  
Andrea Ponti ◽  
Ilaria Giordani ◽  
Francesco Archetti
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Probabilistic Models ◽  
Optimization Problems ◽  
Simulation Optimization ◽  
Water Distribution Systems ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Hydraulic Simulation ◽  
Multi Objective

The main goal of this paper is to show that Bayesian optimization could be regarded as a general framework for the data driven modelling and solution of problems arising in water distribution systems. Hydraulic simulation, both scenario based, and Monte Carlo is a key tool in modelling in water distribution systems. The related optimization problems fall in a simulation/optimization framework in which objectives and constraints are often black-box. Bayesian Optimization (BO) is characterized by a surrogate model, usually a Gaussian process, but also a random forest and increasingly neural networks and an acquisition function which drives the search for new evaluation points. These modelling options make BO nonparametric, robust, flexible and sample efficient particularly suitable for simulation/optimization problems. A defining characteristic of BO is its versatility and flexibility, given for instance by different probabilistic models, in particular different kernels, different acquisition functions. These characteristics of the Bayesian optimization approach are exemplified by the two problems: cost/energy optimization in pump scheduling and optimal sensor placement for early detection on contaminant intrusion. Different surrogate models have been used both in explicit and implicit control schemes. Showing that BO can drive the process of learning control rules directly from operational data. BO can also be extended to multi-objective optimization. Two algorithms have been proposed for multi-objective detection problem using two different acquisition functions.

Parallel Computing for Tire Simulations

2011 ◽  
Vol 39 (3) ◽  
pp. 193-209 ◽  
Author(s):  
H. Surendranath ◽  
M. Dunbar
Keyword(s):  
High Performance ◽  
Effective Means ◽  
Cost Effective ◽  
Turnaround Time ◽  
Careful Consideration ◽  
Rolling Contact ◽  
Element Analysis ◽  
Parallel Solvers ◽  
Design Changes ◽  
Highly Nonlinear

Abstract Over the last few decades, finite element analysis has become an integral part of the overall tire design process. Engineers need to perform a number of different simulations to evaluate new designs and study the effect of proposed design changes. However, tires pose formidable simulation challenges due to the presence of highly nonlinear rubber compounds, embedded reinforcements, complex tread geometries, rolling contact, and large deformations. Accurate simulation requires careful consideration of these factors, resulting in the extensive turnaround time, often times prolonging the design cycle. Therefore, it is extremely critical to explore means to reduce the turnaround time while producing reliable results. Compute clusters have recently become a cost effective means to perform high performance computing (HPC). Distributed memory parallel solvers designed to take advantage of compute clusters have become increasingly popular. In this paper, we examine the use of HPC for various tire simulations and demonstrate how it can significantly reduce simulation turnaround time. Abaqus/Standard is used for routine tire simulations like footprint and steady state rolling. Abaqus/Explicit is used for transient rolling and hydroplaning simulations. The run times and scaling data corresponding to models of various sizes and complexity are presented.

The Role of Computer Modeling in Combined Sewer Overflow Abatement Planning

1992 ◽  
Vol 26 (7-8) ◽  
pp. 1831-1840 ◽  
Author(s):  
L. A. Roesner ◽  
E. H. Burgess
Keyword(s):  
System Modeling ◽  
Cost Effective ◽  
Combined Sewer Overflows ◽  
Sewer System ◽  
New Techniques ◽  
Hydraulic Simulation ◽  
Continuous Simulation ◽  
Combined Sewer ◽  
The U.S

Increased concern regarding water quality impacts from combined sewer overflows (CSOs) in the U.S. and elsewhere has emphasized the role of computermodeling in analyzing CSO impacts and in planning abatement measures. These measures often involve the construction of very large and costly facilities, and computer simulation during plan development is essential to cost-effective facility sizing. An effective approach to CSO system modeling focuses on detailed hydraulic simulation of the interceptor sewers in conjunction with continuous simulation of the combined sewer system to characterize CSOs and explore storage-treatment tradeoffs in planning abatement facilities. Recent advances in microcomputer hardware and software have made possible a number of new techniques which facilitate the use of computer models in CSO abatement planning.

A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them

2016 ◽  
Author(s):  
Po Ting Lin ◽  
Wei-Hao Lu ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Design Space ◽  
Optimization Problems ◽  
Nonlinear Problems ◽  
Kernel Functions ◽  
Sensitivity Analyses ◽  
Normal Space ◽  
Highly Nonlinear ◽  
Standard Normal ◽  
Gradient Based

In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.

Aircraft Assembly Ramp-Up Planning Using a Hybrid Simulation-Optimization Approach

2020 ◽  
Author(s):  
Amos H.C. Ng ◽  
Jacob Bernedixen ◽  
Martin Andersson ◽  
Sunith Bandaru ◽  
Thomas Lezama
Keyword(s):  
Simulation Optimization ◽  
Hybrid Simulation ◽  
Optimization Approach ◽  
Aircraft Assembly

scholarly journals Cost-effectiveness analysis of ranibizumab for retinal vein occlusion patients in China from the societal perspective

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weiyi Ni ◽  
Jia Liu ◽  
Yawen Jiang ◽  
Jing Wu
Keyword(s):  
Cost Effectiveness ◽  
Retinal Vein Occlusion ◽  
Societal Perspective ◽  
Laser Photocoagulation ◽  
Cost Effective ◽  
Sensitivity Analyses ◽  
Gdp Per Capita ◽  
Base Case ◽  
Vein Occlusion ◽  
Per Capita

Abstract Background Clinical trials in China have demonstrated that ranibizumab can improve the clinical outcomes of branch retinal vein occlusion (BRVO) and central retinal vein occlusion (CRVO). However, no economic evaluation of ranibizumab has been conducted among Chinese patient population. Methods To provide insights into the economic profile of ranibizumab among Chinese RVO population, a Markov state-transition model was used to predict the outcomes of ranibizumab comparing to laser photocoagulation and observational-only care from the societal perspective. This model simulated changes in patient visuality, quality-adjusted of life years (QALY), medical costs, and direct non-medical costs of individuals with visual impairment due to BRVO or CRVO in lifetime. The base-case analysis used an annual discount rate of 5% for costs and benefits following the China Guidelines for Pharmacoeconomic Evaluations. Deterministic and probabilistic sensitivity analyses were performed to test the robustness of the model. Results The base-case incremental cost-effectiveness ratio (ICER) comparing ranibizumab to laser photocoagulation was ¥65,008/QALY among BRVO patients and was ¥65,815/QALY among CRVO patients, respectively. Comparing to the 2019 gross domestic product (GDP) per capita of ¥71,000, both two ICERs were far below the cost-effective threshold at three times of GDP per capita (¥213,000). The deterministic and probabilistic sensitivity analyses demonstrated the base-case results were robust in most of the simulation scenarios. Conclusion The current Markov model demonstrated that ranibizumab may be cost-effective compared with laser photocoagulation to treat BRVO and cost-effective compared to observation-only care to treat CRVO in China from the societal perspective.

scholarly journals Toward Decentralised Sanitary Sewage Collection Systems: A Multiobjective Approach for Cost-Effective and Resilient Designs

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1886
Author(s):  
Arezoo Zahediasl ◽  
Amin E. Bakhshipour ◽  
Ulrich Dittmer ◽  
Ali Haghighi
Keyword(s):  
Treatment Plant ◽  
Drainage System ◽  
Cost Effective ◽  
Optimization Approach ◽  
Hydraulic Design ◽  
Multiobjective Approach ◽  
Layout Generation ◽  
Plant Allocation ◽  
Problem Solving Process

In recent years, the concept of a centralized drainage system that connect an entire city to one single treatment plant is increasingly being questioned in terms of the costs, reliability, and environmental impacts. This study introduces an optimization approach based on decentralization in order to develop a cost-effective and sustainable sewage collection system. For this purpose, a new algorithm based on the growing spanning tree algorithm is developed for decentralized layout generation and treatment plant allocation. The trade-off between construction and operation costs, resilience, and the degree of centralization is a multiobjective problem that consists of two subproblems: the layout of the networks and the hydraulic design. The innovative characteristics of the proposed framework are that layout and hydraulic designs are solved simultaneously, three objectives are optimized together, and the entire problem solving process is self-adaptive. The model is then applied to a real case study. The results show that finding an optimum degree of centralization could reduce not only the network’s costs by 17.3%, but could also increase its structural resilience significantly compared to fully centralized networks.

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