Dynamic Cost Model Research Articles

Evolutionary game- based optimization of green certificate- carbon emission right- electricity joint market for thermal-wind-photovoltaic power system

With the increasing proportion of renewable energy in the power market, the demands on government financial subsidies are gradually increasing. Thus, a joint green certificate- carbon emission right- electricity multi-market trading process is proposed to study the market-based strategy for renewable energy. Considering the commodity characteristics of green certificates and carbon emission rights, the dynamic cost models of green certificates and carbon rights are constructed based on the Rubinstein game and ladder pricing models. Furthermore, considering the irrational bidding behavior of energy suppliers in the actual electricity market, an evolutionary game based multi-market bidding optimization model is presented. Subsequently, it is solved using a composite differential evolutionary algorithm. Finally, the case study results reveal that the proposed model can increase profits and the consumption rate of renewable energy and reduce carbon emission.

An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture.

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to cloud computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in mobile edge computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among cloud or mobile edge computing (MEC) server or local IoT devices to achieve better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.

Dynamic Energy Balancing Cost Model for Day Ahead Markets With Uncertain Wind Energy and Generation Contingency Under Demand Response

This paper presents a dynamic energy balancing cost model for day ahead market scheduling under generation uncertainties and demand response penetration. The proposed dynamic models include temporally coupled system conditions to model various types of costs incurred due to uncertain wind energy generation and conventional generation uncertainty. The cost models of wind energy schedule in real-time markets for the deviation from DA estimations is modeled using compensation cost, dynamic adjustment cost, and rescheduling cost. The generation uncertainties and outages considered for conventional generation facility is expressed using geometric distribution to include temporal characteristics of generation outage rate. The problem is formulated as an operational cost minimization problem. The efficacy of the proposed dynamic balancing cost approach is verified using various levels of uncertainty. Further, sensitivity analysis is carried out with respect to various levels of wind penetration and outage rates for all the technoeconomic aspects of resource scheduling. The simulation results for the proposed approach are presented and discussed against static penalty cost methodology. The sensitivity analysis demonstrate the superiority of the proposed dynamic cost models in terms of technoeconomic performance attributes compared to the static cost models.

Nonlinear regression A*OMP for compressive sensing signal reconstruction

A number of tree search based methods have recently been utilized for compressive sensing signal reconstruction. Among these methods, a heuristic algorithm named A* orthogonal matching pursuit (A*OMP) follows best-first search principle and employs dynamic cost model which makes sparse reconstruction exceptionally excellent. Since the algorithm performance of A*OMP relies heavily on preset parameters in the cost model and the estimation of these preset parameters requires a large number of experiments, there is room for improvement in A*OMP. In this paper, an improved algorithm referred to as Nonlinear Regression A*OMP (NR-A*OMP) is proposed which is built on the residue trend to avoid the estimation procedure. This method is inspired by the fact that the residue is correlated closely to the measurement matrix. The residue trend reflects the characteristics of nonlinear regression with the increasing of sparsity K. In addition, restricted isometry property (RIP) based general conditions are introduced to ensure the effectiveness and practicality of the algorithm. Numerical simulations demonstrate the superiority of NR-A*OMP in both reconstruction rate and normalized mean squared error. Results indicate that the performance of NR-A*OMP can become nearly equal to or even better than that of A*OMP with perfect preset parameters.

Techno‐economic assessment of biomass bales storage systems for a large‐scale biorefinery

AbstractStorage of agricultural residues and energy crops is essential for preserving and maintaining its economic value, and year‐round continuous delivery to a biorefinery. In this study, a dynamic cost model was developed for three storage methods (Indoor, Outdoor‐tarped, and Outdoor‐open) with two biomass bale types (rectangular and round) to estimate the storage cost of corn stover and switchgrass. The effects of storage time and multiple‐use of a storage facility on the storage cost were also evaluated to develop effective storage strategies. The cost of bale storage systems comprised of capital expenses for infrastructures, bale handling, dry matter loss (DML) and quality loss costs. Among storage methods, the outdoor‐tarped storage with rectangular bales was the least expensive options for corn stover ($14.6 dry Mg−1) and switchgrass ($16 dry Mg−1). Indoor storage of round bales was costly due to the large storage footprint requirement and high infrastructure investment. However, the indoor storage cost could be reduced, if the storage facility could be used more than once a year. The sensitivity analysis study illustrated that the DML, biomass cost, loading/unloading time and bale density were the most sensitive parameters influencing the storage cost. An example storage strategy developed for a typical biorefinery could reduce the average annual storage cost (up to $10 dry Mg−1) along with an annual cost saving ranging from 10 to 100% compared with any single storage option. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd.

Techno-economic assessments of advanced Combined Cycle Gas Turbine (CCGT) technology for the new electricity market in the United Arab Emirates

In this study, a dynamic cost model was constructed to compare the Levelised Costs of Electricity (LCOE) for advanced Combined Cycle Gas Turbine (CCGT) technology in comparison to traditional CCGT technology. The key technical and economic factors that affected the competitiveness of these CCGT units were evaluated. The results showed that advanced H-class CCGT technology has the lowest LCOE for the base case scenario at 4.93UScents/kWh versus 5.32 and 5.71UScents/kWh for F- and E-class technologies respectively. It is evident that the more advanced CCGT technology matches the major market drivers for the United Arab Emirates (UAE) energy transition, namely; competitive lifecycle costs, high thermal efficiencies which reduce fuel costs and limit CO2 emissions and a high operational flexibility. The LCOE model outputs summarise the overall financial competitiveness of the different CCGT technologies for the UAE up to the year 2030 considering the future power generation demand profile. There are no H-class gas turbines installed in the UAE and this was one of the drivers behind this paper to show the benefits of the latest advanced CCGT technology. The study conveniently facilitates future discussions on the opportunities and challenges of the UAE’s energy transition for developers, electricity suppliers and national policy makers.

Dynamic quality cost model based on complexity theory

Purpose This article presents a dynamic model of decision-making on Quality Costs, using the concepts of the Complexity Theory, consistent with the PAF models (Prevention Appraisal-Failures) and with the “optimum” derivation feature (generally towards Zero Defects). Design/methodology/approach After a literature review and the mathematical model development, this model was simulated in several situations and the results were evaluated, producing new insights. Findings The results show that the larger the delay between action and reaction "t" is, the more complex the system will be, and the effects of the decisions are experienced for “k” later periods, but for some specific “t” a dynamic balance is possible. Research limitations/implications The strategy is immutable. The quantity produced is not a variable in the model. The investment amount “x” is fixed. Originality/value This model is original due to the use of the Complexity Theory, and also to show that the optimum in terms of quality costs can be a positive value or Zero Defects, being in fact a moving target, depending on external conditions. The model value lies in the fact that it is dynamic, so closer to the reality of decision-making enterprises, and for revealing the importance of factors involved with complexity has, such as the time lag "t" for the success of management strategies of Quality Costs.

Phonemic accuracy development in children with cochlear implants up to five years of age by using Levenshtein distance

Phonemic accuracy of children with cochlear implants (CI) is often reported to be lower in comparison with normally hearing (NH) age-matched children. In this study, we compare phonemic accuracy development in the spontaneous speech of Dutch-speaking children with CI and NH age-matched peers. A dynamic cost model of Levenshtein distance is used to compute the accuracy of each word token. We set up a longitudinal design with monthly data for comparisons up to age two and a cross-sectional design with yearly data between three and five years of age. The main finding is that phonemic accuracy steadily increases throughout the period studied. Children with CI's accuracy is lower than that of their NH age mates, but this difference is not statistically significant in the earliest stages of lexical development. But accuracy of children with CI initially improves significantly less steeply than that of NH peers. Furthermore, the number of syllables in the target word and target word's complexity influence children's accuracy, as longer and more complex target words are less accurately produced. Up to age four, children with CI are significantly less accurate than NH children with increasing word length and word complexity. This difference has disappeared at age five. Finally, hearing age is shown to influence accuracy development of children with CI, while age of implant activation is not.Learning outcomes: This article informs the reader about phonemic accuracy development in children. The reader will be able to (a) discuss different metrics to measure phonemic accuracy development, (b) discuss phonemic accuracy of children with CI up to five years of age and compare them with NH children, (c) discuss the influence of target word's complexity and target word's syllable length on phonemic accuracy, (d) discuss the influence of hearing experience and age of implantation on phonemic accuracy of children with CI.

Improving A⋆ OMP: Theoretical and empirical analyses with a novel dynamic cost model

Best-first search has been recently utilized for compressed sensing (CS) by the A⋆ orthogonal matching pursuit (A⋆OMP) algorithm. In this work, we concentrate on theoretical and empirical analyses of A⋆OMP. We present a restricted isometry property (RIP) based general condition for exact recovery of sparse signals via A⋆OMP. In addition, we develop online guarantees which promise improved recovery performance with the residue-based termination instead of the sparsity-based one. We demonstrate the recovery capabilities of A⋆OMP with extensive recovery simulations using the adaptive-multiplicative (AMul) cost model, which effectively compensates for the path length differences in the search tree. The presented results, involving phase transitions for different nonzero element distributions as well as recovery rates and average error, reveal not only the superior recovery accuracy of A⋆OMP, but also the improvements with the residue-based termination and the AMul cost model. Comparison of the run times indicates the speed up by the AMul cost model. We also demonstrate a hybrid of OMP and A⋆OMP to accelerate the search further. Finally, we run A⋆OMP on sparse images to illustrate its recovery performance for more realistic coefficient distributions.

Research on Dynamic Cost-Sensitive SVM Classifier based on Chaos Particle Swarm Optimization Algorithm

In order to improve the performance of Support Vector Machine (SVM) classifier for imbalanced data, this paper proposes dynamic cost-sensitive SVM classifier based on chaos particle swarm optimization (CPDC_SVM). Firstly, this paper introduces dynamic cost-sensitive thought to SVM classifier, and gives the method for structuring dynamic cost and cost-sensitive SVM model. Secondly, we propose the evaluation methodology performance for classifier, and adopts decimal base to code the particles. At last, chaos thought is introduced in particle swarm optimization algorithm, and the Algorithm of the dynamic cost-sensitive SVM classifier is given, which improves convergent speed and accuracy of particle swarm optimization, and can optimize dynamic cost-sensitive SVM well, so CPDC_SVM adds effectively the convergence speed and accuracy for the particle swarm optimization algorithm. Experimental results show CPDC_SVM has higher precision than traditional SVM classifier, and dynamic cost and chaos particle swarm optimization can improve the performance for classifier.

Product-Customization and Payment Policy with Postponement Strategy

Manufacturers adopting postponement strategy can lower operational cost and quickly respond customers’ personalized demands. Adopting postponement strategy, however, has often led manufacturers to situations of higher risk of holding exclusive material in the customization stage. For example, customers prefer to order flexibly from manufacturers so as to be able to respond flexibly to their custom changes. Manufacturers, on the other hand, prefer customers to place full orders as early as possible so that they can hedge against the risks of under-production, especially in the customer order decoupling point (CODP) downstream. Motivated by this problem, we model a dynamic cost model in this paper to evaluate the risks and benefits. Using the Logistic curve, we develop a payment policy for manufacturer adopting postponement strategy in the CODP downstream. Our research demonstrates that, compared with the payment policy of product completed, payment in stages according to product-customization can coordinate conflict between manufacturers and customers in the process of adopting postponement strategy manufacturing system. We also discuss variation of CODP positioning, total cost and payment when rate of return changes.

Cost Estimation Model Based on Improved Spreadsheet Algorithm for Assembly Line Manufacturing Systems

Traditionally assembly cost models are established through static spreadsheet algorithms. However, there are some inherent problems in using spreadsheets for the estimation of manufacturing cost. Among these is the lack of accounting for dynamic effects caused by stochastic variation such as inventory fluctuation, downtimes, supply interruptions, and system failures. Therefore, a dynamic cost estimation model is proposed which can be seen as an integration method between spreadsheet modeling and the virtual plant concept, which maintained the accessibility and flexibility of the spreadsheet model, and did not require a significant increase in the effort level to build a simulation. However, it still includes the effects of interaction between machines, along with simulating random failures, maintenance dispatch and repair. A case study is also tested and the results verify that the methodology demonstrates the feasibility of dynamic cost model based on a number of improvements on static spreadsheet algorithms

Habits, Complementarities and Heterogeneity in Alcohol and Tobacco Demand: A Multivariate Dynamic Model*

In this paper we test the existence of forward-looking behaviour in a multivariate model for alcohol and tobacco consumption. The theoretical framework, based on a dynamic adjustment cost model with forward-looking behaviour, is enhanced to include the intertemporal interactions between the two goods. The analysis of the within-period preferences completes the intertemporal model, allowing to evaluate the static substitutability/complementarity relationships. The empirical strategy consists in a two-step estimation procedure. In a first stage, we obtain the parameters of the demand system, while in a second stage Euler equations are estimated. Results, based on a cohort data set constructed from a series of cross-sections of the Italian Household Budget Survey, reveal a significant complementarity relationship between alcohol and tobacco. Estimation of the Euler equations does not lead to rejection of the hypothesis of intertemporal dependence, providing evidence for a forward-looking behaviour in alcohol and tobacco consumption. Moreover, we find significant intertemporal interactions that support the adjustment cost setting in a multivariate model with rational expectations.

Dynamic adjustment cost models with forward‐looking behaviour

Summary In this paper we propose a new approach for dynamic decision problems where forward-looking agents choose a set of non-stationary variables subject to quadratic adjustment costs. It is assumed that expectations are computed by a cointegrated Vector Equilibrium Correction Model (VEqCM). The role of feedbacks from the decision to the explanatory variables on solution properties and modelling approach is discussed. We show that once the system of interrelated Euler equations stemming from the agent's optimization problem is embedded within the VEqCM, a switching algorithm based on Generalized Least Squares can be used to estimate and test the model. A labour demand model for two Danish manufacturing industries is investigated empirically.

A dynamic service quality cost model with word-of-mouth advertising

A dynamic cost model is developed which describes the evolution of demand for a service from its beginning transient phase to its long-term equilibrium. The model includes parameters which relate to word-of-mouth advertising; this advertising causes transitions between unaware, positively aware, and negatively aware states of consumers and thus affects service demand. Model simulations reveal some generally useful guidelines for service providers: 1) it is unwise to expand service operations excessively during the early phases of the service offering in order to handle strong demand. The model shows that the demand typically peaks at this early stage before settling down to an equilibrium rate, 2) because of customer sensitivity to service quality, cost reductions that deteriorate service quality are likely to be a poor policy, and 3) it is a good strategy to adopt a high service quality from the beginning, rather than to improve it later; this is because the impact of good service on the customer base is more rapid in the startup phase than in the long-term equilibrium phase.

The productivity effects of the liberalization of Japanese telecommunication policy

In the early 1980s, the Japanese government decided to pursue policies of liberalization that opened Japan's telecommunication market to competition and moved toward the privatization of Nippon Telegraph and Telephone (NTT), the dominant domestic telecommunication service provider. The purpose of this paper is to assess the effects of liberalization on the productive performance of NTT. To assess the productive consequences of liberalization, we first provide basic productivity measures for NTT. The Total Factor Productivity (TFP) measures are then decomposed to separate out the effects of liberalization from other factors such as scale, technology, and capacity utilization. The TFP decomposition is based on the parameter estimates of a dynamic cost model.

The Structure of State and Local Government Production

Static and dynamic translog cost models for the aggregate U.S. state and local government sector are estimated for 1952 to 1985. Inputs are capital, labor, and a combined nondurable goods and other services input, which includes "privatized" services. Estimates show that capital and labor are complements and own-price inelastic. The nondurable-service input is a substitute for both labor and capital and has an own-price elasticity closer to unity. It is used relatively more as the sector expands. The results offer an explanation for the characterization of the government as a low productivity industry and show an economic rather than political motivation for the privatization of some public services.

Learning curves and production functions: An integration

This paper explores a class of dynamic cost models that are designed for made-to-order production and are particularly applicable to the airframe industry. In these models we use optimal control theory to describe the time paths of resource use and production rate. The analytic solution for the most general specification of the model is unknown, however the imposition of a restriction on the relation between production rate and experience rate leads to a straightforward solution. The model implies that there exists an optimal trade-off between learning and production rate during the life of a program and that production rate should be changing throughout the life of the program in order to minimize cost. The model is compared with the learning curve, and the applicability of the model is explored by performing various parametric sensitivity analyses. The model's behavior is consistent with the dynamics of actual made-to-order production programs.

A Based-Bottleneck Multiple Vehicle Type Dynamic Marginal Cost Model and Algorithm

Single vehicle type dynamic marginal cost model is extended to multiple vehicle type dynamic one based on time-dependent multiple vehicle type queue analysis at a bottleneck. A dynamic link model is presented to model interactions between cars and trucks, given the link consists of two distinct segments. The first segment is the running segment on which cars (trucks) run at their free-flow speeds and the second segment is the exit queue segment. A car or a truck is assumed to be a point without length. The class-specific pi parameter is used to transform the effect of truck into passenger car equivalents, so the exit flow of cars and trucks can be calculated according to the exit capacity of a bottleneck. The analytic expression of multiple vehicle type dynamic marginal cost function is deduced under congested and uncongested conditions. Then a heuristic algorithm is presented in solving multiple vehicle type dynamic queues, tolls under system optimum and user equilibrium conditions. The numerical example illustrates the simplicity and applicability of the proposed approach.