Dynamic Input Output Research Articles

A fuzzy dynamic Inoperability Input–output Model for strategic risk management in Global Production Networks

Strategic decision making in Global Production Networks (GPNs) is quite challenging, especially due to the unavailability of precise quantitative knowledge, variety of relevant risk factors that needs to be considered and the interdependencies that can exist between multiple partners across the globe. In this paper, a risk evaluation method for GPNs based on a novel Fuzzy Dynamic Inoperability Input Output Model (Fuzzy DIIM) is proposed. A fuzzy multi-criteria approach is developed to determine interdependencies between nodes in a GPN using experts’ knowledge. An efficient and accurate method based on fuzzy interval calculus in the Fuzzy DIIM is proposed. The risk evaluation method takes into account various risk scenarios relevant to the GPN and likelihoods of their occurrences. A case of beverage production from the food industry is used to showcase the application of the proposed risk evaluation method. It is demonstrated how it can be used for GPN strategic decision making. The impact of risk on inoperability of alternative GPN configurations considering different risk scenarios is analysed.

Simulating evolution of interindustry linkages in endogenous dynamic IO model with layers of techniques

AbstractIn this article, we propose a new version of dynamic input–output model in which both technological progress and deployment are endogenous, and where sector‐specific outlays on R&D speed up the development of new technologies and the installation of capital stock. In this two‐technology model, the new and old technical processes within a sector exchange their relative weights in production. We use the model to obtain projections of the interindustry linkages of sectors in the Polish economy over the next 50 years. The results of this simulation suggest an ongoing change of the composition of the set of key sectors of the Polish economy. In general, one may expect to see an ongoing drop in the importance of agriculture‐ and heavy‐industry‐related sectors on the one hand, and a rise in the importance of services‐related ones on the other.

Can China realise its energy-savings goal by adjusting its industrial structure?

ABSTRACTTo investigate whether China can realise its energy-savings goal by 2020 through adjustments to its industrial structure, this study proposes a dynamic input–output multi-objective optimisation model. According to this model, the objectives to be achieved include the maximum gross domestic product and employment, and the minimum energy consumption, where the constraints are the sectoral dynamic input–output balance, labour and energy supply, and sectoral production capacity. The four best solutions are screened from the Pareto-optimal front. The study findings show that the energy intensities in 2020 would decrease by 42.8%, 43.5%, 42.9%, and 43.4% in the four scenarios when compared to their 2002 levels. This means that China can fully achieve its planned energy-savings target for 2020. In order to ensure that the industrial structure is optimised for the future, sectoral capital investments should be regulated by China's government and efforts to improve energy efficiency should be maintained.

Dynamic integrated assessment of bioenergy technologies for energy production utilizing agricultural residues: An input–output approach

In order to facilitate regional agricultural residue utilization for energy production through bioenergy technologies, a dynamic input–output model is developed to estimate and assess the energy, economic and environmental performances of industrialization of five bioenergy technologies within a 15-year time horizon. Electricity and solid, gaseous and liquid biofuels are energy products of bioenergy technologies. Bioenergy technologies are complemented into regional input–output framework and combined with socioeconomic activities aided by their bottom-up economic and energy parameters. The simulation results for the target area indicate that the agricultural residues available for bioenergy technologies could amount to 55.16million t, facilitating to 8.38 million t coal-equivalent bioenergy production by 2025. A 3.1% net reduction in accumulative greenhouse gas emission compared with the “business as usual” case could be achieved owing to substitution of fossil energy with electricity and biofuels produced by bioenergy technologies. From energy production, economic benefits and greenhouse gas mitigation three aspects integratedly, direct-combustion power generation and briquette fuel are more advantageous in the target area. The quantified energy, economic and environmental performances of bioenergy technologies are expected to give recommendations for their industrial development.

WorkWays: interacting with scientific workflows

SummaryWorkWays is a science gateway that supports human‐in‐the‐loop scientific workflows. Human–workflow interactions are enabled by a dynamic Input Output (IO) model, which allows users to insert data into, or export data out of, a continuously running workflow. WorkWays has been used to solve a number of scientific problems where the user wishes to examine intermediate results in order to interact with the computation as the workflow progresses. This interactive capability not only provides better insights into the computation but also allows users to focus on different input parameter combinations. We have implemented a variety of data types and modes of interaction to account for a wide range of use cases and application domains. This paper demonstrates the applicability of WorkWays on three use cases from different domains. Copyright © 2015 John Wiley & Sons, Ltd.

A multi-criteria penalty function approach for evaluating a priori model parameter estimates

• Parameters estimated via both a priori and unconstrained calibration have problems. • Penalty function approach is proposed to examine the trade-off between two approaches. • Compromise solutions provided a balance between performance and physical realism. • Adjustments to a few key a priori parameters were necessary for improved performance. A priori parameterization approaches that improve our ability to provide reliable hydrologic predictions in ungauged and poorly gauged basins, as well as in basins undergoing change are currently receiving considerable attention. However, such methods are typically based on local-scale process understanding and simplifying assumptions and an increasing body of evidence suggests that hydrologic models that utilize parameters estimated via such approaches may not always perform well. This paper proposes a Maximum Likelihood multi-criteria penalty function strategy for evaluating a priori parameter estimation approaches. We demonstrate the method by examining the extent to which a priori parameter estimates specified for the Hydrology Laboratory’s Research Distributed Hydrologic Model (via a set of pedotransfer functions) are consistent with the optimal model parameters required to simulate the dynamic input–output response of the Blue River basin. Our results indicated that whereas simulations using the a priori parameter estimates give consistently positive flow bias, unconstrained optimization to the response data results in parameter values that are very different from the a priori parameter set. Moreover, although unconstrained optimization performed best (as measured by the calibration criteria), poor hydrograph simulation performance was evident when evaluated in terms of multiple performance statistics not used in the calibration. On the other hand, the multi-criteria compromise solutions provided improved input–output performance in terms of measures not used in calibration, with generally more consistent behavior across calibration and evaluation years, while maintaining physically realistic a priori values for most of the model parameter estimates; adjustments were found to be necessary for only a few key model parameters.

Dynamic Input–Output Models in Environmental Problems: A Computational Approach with CAS Software

The study of interactions between the economy and the environment has always been an interesting subject. Apart from static input---output (IO) models, environmentally extended IO analysis has resulted in dynamic IO models as well. Dynamic models are built of differential equations and, in the case of discrete models with the help of difference equations. The dynamic approach requires advanced mathematical skills especially in cases where the stability of the system under study is considered. In this paper we state some applied environmental models in discrete time based on dynamic IO analysis and we propose a computational approach in computer algebra system environments that investigates the extent and the nature of their stability. The computer codes are fully presented and can be reproduced as they are in computational-based research practice and education.

A quality-related fault detection approach based on dynamic least squares for process monitoring

The issue of quality-related fault detection has attracted much attention in recent years. Partial least squares (PLS) is considered as an efficient tool for predicting and monitoring. However, due to the fact that PLS performs an oblique projection to input space, it is not suitable for quality-related fault detection. On the other hand, PLS is a static method which cannot be used in dynamic systems. In this paper, a dynamic least squares approach is developed by using the structure of auto-regressive moving average exogenous (ARMAX) time-series model. Furthermore, augmented input matrix is decomposed into two orthogonal parts according to their correlations with output, such that quality-related fault detection can be utilized by designing appropriate statistics in two subspaces corresponding to the two parts. The proposed approached is simple and effective for systems with dynamic input and static output, which is a common case for most industrial processes. A numerical example and an industrial process simulator are applied to test the performance of the proposed approach.

Do Direct Foreign Investments Increase Efficiency Convergence at Firm Level? The Case of Vietnam, 2000-2011

The objective of this study is to assess the extent to which the effect of FDI on firms’ efficiency and efficiency convergence across industries in Vietnam. Dynamic input output tables are used to construct the linkages between domestic and foreign firms. Stochastic production frontier is used to estimate firms ‘efficiency with a large panel dataset covering manufacturing firms in Vietnam from 2000 to 2011. The analysis shows that, the impact of FDI on domestic firms ‘efficiency score and convergence at firms’ level through the horizontal, backward and supply backward channels are negative and different.

Low-frequency dynamic modelling and control of matrix converter for power system applications

High-power density of a matrix converter (MC) necessarily introduces tight dynamic input–output coupling, which complicates controller design for high bandwidth applications. Strong contemporary interest in MC-based solutions for synchronous, power system applications demands high dynamic performance out of the MC, operated as a closed-loop system. The requisite system models reported either treat the MC – with its input and output filters – as a ‘black-box’ characterised by a set of eigenvalues, derived from linearised analysis around an equilibrium point. Alternatively, large-signal system analysis was reported for a feed-forward control approach with prescription of a throughput power boundary for stable operation. This study provides a designer's insight into MC modelling, based on linearised analysis, clearly establishing the influence of individual physical sub-systems on the overall dynamic performance. Onset of a cluster of right-half zeros, decided solely by the input filter, power factor, source voltage and power throughput, is shown to be the only factor threatening closed-loop stability. Subsequently, a controller with output feedback is designed to ensure stable operation well beyond the throughput power limit prescribed earlier. Comprehensive simulation and experimental results are provided to validate the analytical model and controller design.

Dynamic Data-Driven Modeling of Pharmaceutical Processes

In many pharmaceutical process operations, first-principle models describing the behavior of the processed powders are difficult to derive or computationally very expensive and thus can be considered as “black-box” processes. Kriging is an efficient data-driven methodology that has been shown previously to predict steady-state operation of black-box processes while providing error estimates for each prediction. In this work, the ability of Kriging to capture the dynamic behavior of processes is evaluated and compared to the performance of dynamic Neural Network (NN) models. Design of Experiment (DoE) concepts are employed to collect the necessary dynamic input–output information that forms the initial multivariate database of the model. The most important feature of the proposed dynamic Kriging methodology is the ability to improve its predictive efficiency as the database is augmented by additional useful information while the process operates. An error tolerance is used in the adaptive procedure, so that only predictions that satisfy a strict accuracy constraint are added to the database. The performance of the proposed method is compared to that of dynamic NN modeling, through their application to a roller compactor process. Results indicate that dynamic Kriging has a better ability to adapt to continuous transitions in the operating conditions, compared to NN modeling. This on-the-fly approach can have significant applications in real-time optimization and control of systems for which a closed-form model is not available.

A comparative and dynamic approach to the development of determiner use in three children acquiring different languages

The study investigates the development of determiner use in three children acquiring French, Austrian German and Dutch, from the onset of language until age 3;0. Noun constructions (determiner omission, correct bare nouns, filler and determiner uses) in the children and in their inputs are analysed, providing evidence of similarities in developmental shape as well as differences in frequencies and timing. As expected, determiner use was delayed in the Germanic languages as compared to French. Differences between the Austrian and the Dutch child were explained by language properties and by child characteristics. Modelling dynamic input–output relations provided evidence of styles of long-term parental adaptation (accommodation for the French and complementarity for the Dutch and Austrian children).

Obesity-related costs and the economic impact of laparoscopic adjustable gastric banding procedures: benefits in the Texas Employees Retirement System

Objective: To assess the return on investment (ROI) and economic impact of providing insurance coverage for the laparoscopic adjustable gastric banding (LAGB) procedure in classes II and III obese members of the Texas Employees Retirement System (ERS) and their dependents from payer, employer, and societal perspectives.Methods: Classes II and III obese employee members and their adult dependents were identified in a Texas ERS database using self-reported health risk assessment (HRA) data. Direct health costs and related absenteeism and mortality losses were estimated using data from previous research. A dynamic input–output model was then used to calculate overall economic effects by incorporating direct, indirect, and induced impacts. Direct health costs were inflation-adjusted to 2008 US dollars using the Consumer Price Index for Medical Care and other spending categories were similarly adjusted using relevant consumer and industrial indices. The future cost savings and other monetary benefits were discounted to present value using a real rate of 4.00%.Results: From the payer perspective (ERS), the payback period for direct health costs associated with the LAGB procedure was 23–24 months and the annual return (over 5 years) was 28.8%. From the employer perspective (State of Texas), the costs associated with the LAGB procedure were recouped within 17–19 months (in terms of direct, indirect, and induced gains as they translated into State revenue) and the annual return (over 5 years) was 45.5%. From a societal perspective, the impact on total business activity for Texas (over 5 years) included gains of $195.3 million in total expenditures, $93.8 million in gross product, and 1354 person-years of employment.Limitations: The analysis was limited by the following: reliance on other studies for methodology and use of a control sample; restriction of cost savings to 2.5 years which required out-of-sample forecasting; conservative assumptions related to the cost of the procedure; exclusion of presenteeism; and no sensitivity analyses performed.Conclusion: This analysis indicates that providing benefits for the LAGB procedure to eligible members of the Texas ERS and their dependents is worthy of support from payer, employer, and societal perspectives.

Decentralized adaptive stabilization for interconnected systems with dynamic input–output and nonlinear interactions

This paper considers the problem of robust decentralized adaptive output feedback stabilization for a class of interconnected systems with dynamic input and output interactions and nonlinear interactions by using MT-filters and the backstepping design method. It is shown that the closed-loop decentralized system based on MT-filters is globally uniformly bounded, all the signals except for the parameter estimates can be regulated to zero asymptotically, and the L 2 and L ∞ norms of the system outputs are also be bounded by functions of design parameters. The scheme is demonstrated by a simulation example.

Measuring the efficacy of inventory with a dynamic input–output model

This paper extends the recently developed dynamic inoperability input–output model (DIIM) for assessing productivity degradations due to disasters. Inventory policies are formulated and incorporated within the DIIM to evaluate the impact of inventories on the resilience of disrupted interdependent systems. The Inventory DIIM can provide practical insights to preparedness decision making through explicit tradeoff analysis of multiple objectives, including inventory costs and economic loss reductions. The model is demonstrated in several illustrative examples to depict various nuances of inventory policies. The paper then culminates in a case study that utilizes input–output and inventory accounts from the Bureau of Economic Analysis.

Grey dynamic input–output analysis

A system whose information is partially known and partially unknown is named as a grey system and it is general situation in reality. On the basis of grey number/matrix and their covered operation, some definitions of grey functions are enumerated. Combining grey system theory with traditional dynamic input–output analysis, we propose the grey dynamic input–output analysis. It has two types, i.e., the continuous and the disperse. The most important results are the computational formulas of the matrix-covered set of inverse grey investment coefficient matrix. By using them, we can forecast and control the economic system under the uncertain situation. For the calculation of the matrix-covered set of inverse grey matrices, we obtain the desired formulas, which can be applied to general grey matrices. The formulas can greatly promote the development of grey theory and enrich the applied fields of uncertain mathematics. The modified case illustrates our method.

The efficiency of remanufacturing in a dynamic input–output model

For industrialised economy of ourdays, remanufacturing represents perhaps the largest unexploited resource and opportunity for realising a greater growth of the economy in an environmental-conscious manner. The aim of this paper is to investigate of the impact of remanufacturing in the economy from an economic-efficiency point of view. In static context this phenomenon was analysed in the literature. We use the multi-sector input–output framework in a dynamic context to study intra-period relationships of the sectors of economy. We extend the classical dynamic input–output model taking into consideration the activity of remanufacturing .We try to answer the question, whether the remanufacturing/reuse increases the growth possibility of an economy. We expose a sufficient condition concerning the effectivity of an economy with remanufacturing. By this evaluation we analyse a possible sustainable development of the economy on the basis of the product recovery management of industries.

A Multi-sector Nonlinear Dynamic Input–Output Model with Human Capital

In this paper, a two-sector model of endogenous growth is extended to a multi-sector nonlinear dynamic input–output model. If the model is on its balanced growth path (where prices – but not quantities – are assumed constant over time), it can be shown that the linear dynamic Leontief model is a special case. At the same time, on its balanced growth path, our model can be interpreted as a CGE model. The model distinguishes n + 1 sectors: n sectors producing physical goods, and one sector producing new human capital. A method is given for calculating the growth rate of GNP, the profit rate, the prices of goods, and the level of production when the model is on its balanced growth path. We point out the relation between the balanced growth rate and the profit rate.

Investigating new technologies in a scenario context: description and application of an input–output method

The dynamic input–output model DIMITRI will be used for explorations of long-term scenarios on technology and demand. The model describes at a sectoral level the relationships between consumption, production and emissions. Technology per sector is described by the inputs from other sectors (represented by IO coefficients) and capital, labour and emission coefficients. Since these coefficients are not stable over time, they have to be constructed for future years. This paper presents a methodology for projecting these coefficients for different scenarios. The methodology combines trend analysis with detailed information of specific technologies per sector, which differentiates between scenarios. The adjustment of coefficients influences model outcomes such as production and emissions. The paper outlines the methodology and presents the main outcomes for four scenarios for the period 2000–2030.

Comparación de Controladores de Nivel para Canales Abiertos Basados en un Modelo por Colocación

This paper proposed a comparative study between different nonlinear control techniques for level regulation in an open channel. Flow dynamics in a canal can be modeled by Saint-Venant equations. In this paper three control laws derived from a collocation model are presented. These laws are: input output linearization, dynamic input output linearization, and backstepping. A one-dimensional simulator that solves Saint-Venant’s equations was implemented, allowing to study the controlled system. The results showed that the dynamic input output linearization control technique was the best among the studied control techniques, and that these techniques are reliable in the cases studied.