Estimation Of Cost Function Research Articles

Data-driven Estimation of Origin-Destination Demand and User Cost Functions for the Optimization of Transportation Networks

In earlier work (Zhang et al., 2016) we used actual traffic data from the Eastern Massachusetts transportation network in the form of spatial average speeds and road segment flow capacities in order to estimate Origin-Destination (OD) flow demand matrices for the network. Based on a Traffic Assignment Problem (TAP) formulation (termed “forward problem”), in this paper we use a scheme similar to our earlier work to estimate initial OD demand matrices and then propose a new inverse problem formulation in order to estimate user cost functions. This new formulation allows us to efficiently overcome numerical difficulties that limited our prior work to relatively small subnetworks and, assuming the cost functions are available, to adjust the values of the OD demands accordingly so that the flow observations are as close as possible to the solutions of the forward problem. Finally, using the same actual traffic data from the Eastern Massachusetts transportation network, we quantify the Price of Anarchy (PoA) for a much larger network than that in Zhang et al. (2016).

The evolution of scale economies in US banking

SummaryContinued consolidation of the US banking industry and a general increase in the size of banks have prompted some policymakers to consider policies that discourage banks from getting larger, including explicit caps on bank size. However, limits on the size of banks could entail economic costs if they prevent banks from achieving economies of scale. This paper presents new estimates of returns to scale for US banks based on nonparametric, local‐linear estimation of bank cost, revenue, and profit functions. We report estimates for both 2006 and 2015 to compare returns to scale some 7 years after the financial crisis and 5 years after enactment of the Dodd–Frank Act with returns to scale before the crisis. We find that a high percentage of banks faced increasing returns to scale in cost in both years, including most of the 10 largest bank holding companies. Also, while returns to scale in revenue and profit vary more across banks, we find evidence that the largest four banks operate under increasing returns to scale.

A combined photocatalytic‐biological wastewater treatment approach: a steady‐state model

AbstractBACKGROUNDWater purification is challenged by the co‐presence of contaminants, some of which are too toxic for bacteria or too opaque for photocatalytic processes. This challenge may be overcome by utilizing two types of reactors (for example biological and photocatalytic) operating in sequence. Realization of this approach should be based on a steady state model for calculating the optimal design under nominal conditions, a non‐steady state responding to fluctuating conditions and fast measurement of toxicity and turbidity.RESULTSA steady‐state model, examining the effect of coexisting toxicity and turbidity on the optimal sequence, on the relative retention times in the two reactors and on the effect of recycling, under an estimated cost function constraint is presented. It was found that the optimal sequence depends on the relative importance of toxicity and turbidity, that a single branch operation is likely to be more cost effective than dual branch (Bio→photocatalytic in parallel with photocatalytic→Bio) system.CONCLUSIONIt is recommended to add the possibility for altering flow direction to account for operation under non‐nominal conditions. To decrease instability during flow‐direction switching, a recycling option should be added. Recycling may also be beneficial in particular when the activated biological medium has sluggish kinetics. © 2017 Society of Chemical Industry

Economic Advantages of Utilizing the Integrated Quality Software Development Model

New contemporary software development models try to tradeoff among the three major aspects of concern; Cost, time and meeting customer requirements. One of the recently introduced software development models is the Integrated Quality Software Development (IQSD). This model builds on the advantages of both the prototyping and waterfall models and eliminates their limitations. This research presents the development of a cost estimation function that quantifies the economic benefits of implementing the IQSD model. Numerical analysis indicated that the IQSD model outperforms traditional development models from an economic standpoint.

Parameter Identification of a Chaotic Circuit with a Hidden Attractor Using Krill Herd Optimization

Parameter estimation plays an important role in modeling and system identification. However, parameter estimation of chaotic systems has some basic differences with other dynamical systems due to butterfly effect. In this paper, we apply a new cost function for parameter estimation in a very interesting chaotic system, a system with a plane of equilibrium which belongs to a newly introduced category of dynamical systems: systems with hidden attractor. The nonlinear dynamics of this system is described in terms of equilibria and its stability, phase portraits, bifurcation diagram and Lyapunov exponents. In order to minimize the proposed cost function and obtain the correct parameters, we use a new efficient optimization method, Krill Herd algorithm. The results show the success of proposed procedures.

Compile- and run-time approaches for the selection of efficient data structures for dynamic graph analysis.

Graphs are used to model a wide range of systems from different disciplines including social network analysis, biology, and big data processing. When analyzing these constantly changing dynamic graphs at a high frequency, performance is the main concern. Depending on the graph size and structure, update frequency, and read accesses of the analysis, the use of different data structures can yield great performance variations. Even for expert programmers, it is not always obvious, which data structure is the best choice for a given scenario.In previous work, we presented an approach for handling the selection of the most efficient data structures automatically using a compile-time approach well-suited for constant workloads.We extend this work with a measurement study of seven data structures and use the results to fit actual cost estimation functions. In addition, we evaluate our approach for the computations of seven different graph metrics. In analyses of real-world dynamic graphs with a constant workload, our approach achieves a speedup of up to 5.4× compared to basic data structure configurations.Such a compile-time based approach cannot yield optimal results when the behavior of the system changes later and the workload becomes non-constant. To close this gap we present a run-time approach which provides live profiling and facilitates automatic exchanges of data structures during execution. We analyze the performance of this approach using an artificial, non-constant workload where our approach achieves speedups of up to 7.3× compared to basic configurations.

An Economic Estimation of Cost Functions in the Long Term and Economies of Scale to Date Palm in Siwa Oasis

An Economic Estimation of Cost Functions in the Long Term and Economies of Scale to Date Palm in Siwa Oasis

Moving horizon estimation for Markov jump systems

In this paper, we study the state estimation problem for a class of nonlinear Markov jump systems with the moving horizon estimation algorithm, which is an optimization-based filtering method. The optimal estimate is obtained by minimizing a quadratic estimation cost function defined on fixed sliding horizon measurements. For this purpose, the quadratic estimation cost function is formulated from the negative logarithm of the joint states distribution, whose particular factorization is represented by a Bayesian network. By analyzing the corresponding full information estimation as a basic problem for nonlinear Markov jump systems, the formulation of the moving horizon estimation is developed. An example is presented to compare the proposed new estimation technique with classical interacting multiple model particle filtering, and show the effectiveness and advantages of the new estimation scheme.

Optimal Joint Detection and Estimation Based on Decision-Dependent Bayesian Cost

This paper considers the statistical inference problem where both the hypothesis testing and the parameter estimation are of primary interest. With unknown parameters present in each hypothesis, the goal is to detect the true hypothesis and to estimate the unknown parameters simultaneously. In light of the coupling nature of these two subproblems, we adopt the Bayesian estimation cost function that depends on both the detection result and the estimation scheme. We then obtain the optimal joint detector and estimator by minimizing the Bayesian estimation cost subject to the constraint on detection performance. The proposed joint solution not only yields lower estimation cost compared with the method that treats the detection and estimation separately, but also allows for flexible tradeoff between the performances of these two subproblems. In addition, we also extend our framework to the multi-hypothesis scenario, where $K$ hypotheses and their associated unknown parameters are present. Finally, we apply the proposed joint detection and estimation framework to the spectrum sensing for cognitive radio, which aims to detect the presence of the primary user and to estimate the noise/interference level at the same time.

Online Frequency Response Analysis: A Powerful Plug-in Tool for Compensation Design and Health Assessment of Digitally Controlled Power Converters

This paper presents a cycle efficient software frequency response analyzer (SFRA), which is integrated into the firmware of the digitally controller power converter to measure the frequency response of the plant and the open-loop gain of the closed-loop system. The algorithm uses the readily available measurements taken for the power stage control and does not require additional hardware. This enables periodic measurement of the frequency response which can be used to monitor changes in the power converter. The proposed technique can run on a conventional microcontroller without any host intervention, and adds no cost to the system, which is important for the cost-sensitive power converters. Both floating point and fixed point implementation are compared enabling widespread applicability of the technique. In order to validate the efficacy of the analyzer, a synchronous buck power converter is designed and its control transfer function is compared with the measured online frequency response. An algorithm is outlined to fit the measured SFRA data to a pole–zero format through intelligent cost function estimation. The correlation of zeros and poles identified using this method with the modeled plant transfer function is analyzed. A dual core approach is discussed to run SFRA on a high-switching-frequency converter, thus showing applicability of the technique in high-switching-frequency converters.

Cost estimation using ANFIS

ABSTRACTCost function estimation is vital for decision-making in project management. In this article, a novel cost estimator is investigated based on an adaptive neuro-fuzzy inference system (ANFIS). In the numerical studies, ANFIS is tested to modeling pressure vessel cost as a case study. According to the comparative results, ANFIS shows better accuracy than multiple linear regression (MLR), Taylor Kriging (TK), and artificial neural networks (ANNs). Hence, ANFIS can be applicable to various cost function estimation problems.

Determination of the extended Drucker–Prager parameters using the surrogate-based optimization method for polypropylene nanocomposites

In this article, a new method is proposed to identify the constants of the extended Drucker–Prager yield surface for polypropylene nanocomposites. The method is based on optimizing the difference between the numerical and the experimental results of a three-point bending test. The test specimens are made of polypropylene/nanoclay and polypropylene/nano-calcium carbonate nanocomposites with different nanoparticles content. Moreover, the effect of composite filler content on the extended Drucker–Prager constants of polypropylene, as the composite matrix, is investigated. Inasmuch as numerical simulation is usually very time-consuming and highly nonlinear, a surrogate-based model with radial basis function is used to approximate cost function. For calibration of the surrogate model, sample points are selected using the Latin hypercube sampling technique which enjoys good filling-space properties. The results indicate that the cohesion strength and elastic modulus of the neat polymer are improved by adding nanoparticles. The improvement, however, is more significant for the polypropylene/nanoclay with respect to that for the polypropylene/nano-calcium carbonate. The nanoparticles were found not to be considerably effective on the dilation angle of the neat polymer. In addition, it was found that the non-associative plastic flow assumption can describe plastic deformation of polypropylene composites more properly. The surrogate-based optimization method was validated by identifying some of the extended Drucker–Prager constants directly from tension and compression tests.

Optimal control of nonlinear continuous-time systems in strict-feedback form.

This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.

Understanding technological progress and input price as drivers of energy demand in manufacturing industries in India

This paper presents a comprehensive analysis of energy demand behaviour of seven energy intensive manufacturing industries and the aggregate manufacturing sector in India during 1973–74 to 2011–12. The policy Perform, Achieve and Trade (PAT) has mandated energy efficiency targets for these manufacturing industries in India. We focus on two major drivers of energy demand: technological progress and energy price. Productivity growth accounting and estimation of parametric cost function using Annual Survey of Industry data bring out important implications regarding the role of these two drivers. Results suggest that these industries experienced technological progress over the study period (1973–74 to 2011–12) with significant energy-saving bias during 1998–99 to 2011–12. Increase in energy price has led to reduction in energy demand and augmented technological progress in most of the industries. Energy and material inputs are mostly substitutes. During 1998–99 to 2011–12, productivity growth of energy input was induced by both technological progress and increase in energy price. Estimates of inter-factor substitution suggest that price induced reduction in energy demand can be a capital-intensive process in case of some industries. Rebound effect has never taken back full gains of energy efficiency policies in the context of these industries.

A method for analyzing pollution control policies: Application to SO2 emissions in China

Market-based pollution control mechanisms such as pollution levy and cap and trade have received increasing attention from both academics and practitioners. A good understanding of the optimal pollution price under these mechanisms is a premise for regulators to make sound pollution control policies. In this paper, we propose a method for deriving the optimal pollution price for a given pollution target. This method consists of two steps that integrate cost function estimation and market equilibrium analysis: First, historical data is used to estimate the pollution abatement cost functions of the polluters; second, market models are used to solve the equilibrium pollution price under each control mechanism. For illustration, we apply the method to investigate SO2 emission control policies in China, using a dataset of SO2 emissions and abatement costs from three major industry sectors (Electricity, Gas, and Water Supply; Manufacturing; and Mining). Our analysis shows that the optimal levy rate is significantly higher than the actual rate adopted by the Chinese government. For example, the optimal levy rate should be 4.92RMB/kg, while the actual rate is 1.26RMB/kg in 2010. As a result, the actual emission structure is much less efficient: The overall cost savings would be 49.7% for all three sectors during 2006–2010 if the optimal emission structure is achieved. These findings have useful policy implications for the Chinese government. In addition, the method may be applied to analyze control policies at different aggregate levels (for example, provincial economies) or for other pollutants (for example, CO2 and chemical oxygen demand).

Classifying Ports for Efficiency Benchmarking: A Review and a Frontier-based Clustering Approach

Port efficiency and port clustering are two aspects that have received different degrees of attention in the existing literature. While the actual estimation of port efficiency has been extensively studied, the existing literature has paid little attention to developing robust methodologies for port classification. In this paper, we review the literature on classification methods for port efficiency, and present an approach that combines stochastic frontier analysis, clustering and self-organized maps (SOM). Cluster methodologies that build on the estimated cost function parameters could group ports into performance metrics’ categories. This helps when setting improvement targets for ports as a function of their specific cluster. The methodology is applied to a database of Spanish port authorities. The dendrogram features three clusters and five outlier Spanish Port Authorities. SOM are employed to track the temporal evolution of Spanish Port Authorities that are of special interest for some reasons (i.e. outliers). Results show that use of a combination of cost frontier and cluster methods to define robust port typology and SOMs, jointly or in isolation, offers useful information to the decision-makers.

A Method for Analyzing Pollution Control Policies: Application To SO<sub>2</sub> Emissions in China

Market-based pollution control mechanisms such as pollution levy and cap and trade have received increasing attention from both academics and practitioners. A good understanding of the optimal pollution price under these mechanisms is a premise for regulators to make sound pollution control policies. In this paper, we propose a method for deriving the optimal pollution price for a given pollution target. This method consists of two steps that integrate cost function estimation and market equilibrium analysis: First, historical data is used to estimate the pollution abatement cost functions of the polluters; second, market models are used to solve the equilibrium pollution price under each control mechanism. For illustration, we apply the method to investigate SO2 emission control policies in China, using a dataset of SO2 emissions and abatement costs from three major industry sectors (Electricity, Gas, and Water Supply; Manufacturing; and Mining). Our analysis shows that the optimal levy rate is significantly higher than the actual rate adopted by the Chinese government. For example, the optimal levy rate should be 4.92 RMB/kg, while the actual rate is 1.26 RMB/kg in 2010. As a result, the actual emission structure is much less efficient: The overall cost savings would be 49.7% for all three sectors during 2006-2010 if the optimal emission structure is achieved. These findings have useful policy implications for the Chinese government. In addition, the method may be applied to analyze control policies at different aggregate levels (for example, provincial economies) or for other pollutants (for example, CO2 and chemical oxygen demand).

Estimation of a Long-Run Cost Function for Bananas Cultivation in Jordan

This study aimed at investigating bananas production in Jordan in terms of estimating the long run cost function and deriving the associated parameters. A field survey was carried out to collect the primary data in Ghore Area of Jordan (Jordan Valley). Simple random and purposive sampling procedure was followed in this study. Interviews were carried out and a questionnaire was constructed to collect the needed information from 66 sampled farmers. The findings of the study showed that both the long run marginal costs and long run average costs were estimated to be 0.05 and 0.13 JDs/kg of bananas respectively. The optimum size of production and the production level with maximum profit for the sampled farms were 8695.65 kgs 13043 kgs respectively. Both the optimum size of production and the production level with maximum profit of the sampled farms were much higher of the average production of these farms by 2260 and 3997 JDs respectively.

Optimal Spatial Subdivision method for improving geometry navigation performance in Monte Carlo particle transport simulation

Geometry navigation is one of the key aspects of dominating Monte Carlo particle transport simulation performance for large-scale whole reactor models. In such cases, spatial subdivision is an easily-established and high-potential method to improve the run-time performance. In this study, a dedicated method, named Optimal Spatial Subdivision, is proposed for generating numerically optimal spatial grid models, which are demonstrated to be more efficient for geometry navigation than traditional Constructive Solid Geometry (CSG) models. The method uses a recursive subdivision algorithm to subdivide a CSG model into non-overlapping grids, which are labeled as totally or partially occupied, or not occupied at all, by CSG objects. The most important point is that, at each stage of subdivision, a conception of quality factor based on a cost estimation function is derived to evaluate the qualities of the subdivision schemes. Only the scheme with optimal quality factor will be chosen as the final subdivision strategy for generating the grid model. Eventually, the model built with the optimal quality factor will be efficient for Monte Carlo particle transport simulation. The method has been implemented and integrated into the Super Monte Carlo program SuperMC developed by FDS Team. Testing cases were used to highlight the performance gains that could be achieved. Results showed that Monte Carlo simulation runtime could be reduced significantly when using the new method, even as cases reached whole reactor core model sizes.

KONSTRUKCE NÁKLADOVÝCH FUNKCÍ V PODMÍNKÁCH RIZIKA A NEURČITOSTI

In economics, a cost curve is a graph of the costs of production as a function of total quantity produced. In a free market economy, these curves are used by the entities to find the optimal point of production, where they make the highest profits. There exist several different types of cost curves, while each of them is relevant to a different area of economics. In the article, authors are focused on solution of cost-functions modelling, both short-run cost function and long-run cost function under circumstances of risk and uncertainty. Considerations about factors of risk and uncertainty are based on an irrefutable fact that companies are not separate entities taken out of surrounding environment; entities operate in global world where many random factors are influencing the processes in companies while the number of these random factors is ad infinitum. The fact that estimation of cost functions' parameters are realized from past data is the basis of the considerations about planning of future scope of production based on these functions. Especially for the long-run cost functions it is impossible to leave out all the random influences. Their quantification is derived from aposteriori probabilistic approach according to Bayesian Theorem.