Imperfect Model Research Articles

Analyzing an imperfect production inventory model for green products considering learning effect on screening process under advertisement dependent credit period

<p style='text-indent:20px;'>In this research work, we have developed an imperfect production inventory model under the screening process in the manufacturing system to meet the demand of quality items for the customer. Here, the novelty lies in the screening rate depending on the number of cycles and the learning effect of the workers. The motivation behind such consideration is to increase the screening rate in each subsequent cycle on account of the learning of the workers. The virtue of government policy is to keep the environment free from pollution, nowadays the production of green products is very essential although this can lead to higher production costs for the manufacturer. As a result, customers also have to pay higher purchasing costs. So, to motivate the customers extensively to buy a green product, awareness about such product is very essential. In this regard, the manufacturer adopts an innovative advertising policy, which is another novelty of this research work. Ultimately, the demand function depends on the green degree, advertisement frequency and the retailer's selling price. It is also noted that in the present competitive business market the manufacturer offers an incentive to their retailers to carry on their business smoothly. In this regard, a credit period policy of logistic nature has been taken. Finally, an integrated profit function has been developed and its feasibility has been justified taking a numerical example. The result indicates that not only an appropriate advertisement frequency but also a suitable level of the green degree of the product is more important for maintaining the integrated profit of the system and individual profit of the retailer and the manufacturer also. We have analyzed the impacts of the retailer's advertisement frequency on the optimal credit period and the retailer's profit. We have also analyzed how the learning rate of the workers affects the screening cost and the manufacturer's profit.</p>

Does inattentiveness matter for DSGE modeling? An empirical investigation

The purpose of this paper is to investigate the empirical performance of the standard New Keynesian dynamic stochastic general equilibrium (DSGE) model in its usual form with full-information rational expectations and compare it with versions assuming inattentiveness- namely sticky information and imperfect information data revision. Using a Bayesian estimation approach on US quarterly data (both real-time and survey) from 1969 to 2015, we find that the model with sticky information fits best and is the only one that can generate the delayed responses observed in the data. The imperfect information data revision model is improved fits better when survey data is used in place of real-time data, suggesting that it contains extra information. • The paper considers the full-information rational expectations with two other forms of inattentive expectations, namely sticky information and imperfect information data revision. • The paper extends empirical evidence using both real-time and survey data to verify which model with the inattentive feature best replicates actual outcomes. • This is the first empirical analysis to compare models with and without expectations inattentiveness. • The New Keynesian DSGE model with an inattentive feature is a better fit with the real-time data using Bayesian estimation. • The imperfect information data revision model performs better when we substitute the real-time data with the survey data.

Selective Maintenance Strategy for a Finite Planning Horizon Considering Imperfect Maintenance

Most selective maintenance research strategies ignore the comprehensive evaluation of numerous following missions in favor of focusing just on the reliability of the next mission. In the same circumstance, however, assessing simply impending missions differs from considering the overall system’s maintenance planning outcomes. In this study, a decision‐making model for selective maintenance is developed by evaluating the total system reliability over a finite planning horizon. The purpose is to calculate the maintenance activities for each system component during each break and the best number of maintenance interruptions for the planning horizon to achieve maximum system reliability. Consequently, the selective maintenance problem is formulated as a max–min optimization model. Also, a hybrid imperfect maintenance model is used to formulate the component improvement after maintenance. Finally, simple case illustrations of maintaining the production system in coal transportation are given based on the assumed data.

Analysing the Imperfect Production Model for Decaying Products with Advertisement and stock dependent demand

Analysing the Imperfect Production Model for Decaying Products with Advertisement and stock dependent demand

Analysing the imperfect production model for decaying products with advertisement and stock dependent demand

Analysing the imperfect production model for decaying products with advertisement and stock dependent demand

An imperfect production-inventory model for reworked items with advertisement, time and price dependent demand for non-instantaneous deteriorating item using genetic algorithm

This article deals with an imperfect production inventory system with advertisement, price, and time-dependent demand for a non-instantaneous deteriorating item. Every manufacturing company aims to produce only perfect quality items, but this is not possible in reality. Firstly, the manufacturing system starts producing perfect items, but after some time, it starts producing imperfect items also, because of the manufacturing machine's long run. Perfect products are ready to sell, and some percentages of imperfect products are reworked to become perfect, and the remaining defective products are sold at a cheaper price. An efficiency cost is included to maintain the system efficiency. Considering these conditions, we developed a profit function. This profit function is highly nonlinear. Therefore, we employed a real coded genetic algorithm to obtain the optimum values of production rate, selling price of the perfect quality item, selling price of imperfect item, and inverse efficiency factor to maximise the profit. The model is demonstrated by using numerical example followed by sensitivity analysis.

Imperfect Maintenance Model for Optimizing Air Compressor Availability

Imperfect Maintenance Model for Optimizing Air Compressor Availability

Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles

Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs). In contrast, purely data-driven models require a large number of observations and lack operational guarantees for safety-critical systems. Data-driven models leveraging available partially characterized dynamics have potential to provide reliable systems models in a typical data-limited scenario for high value complex systems, thereby avoiding months of expensive expert modeling time. In this work we explore this middle-ground between expert-modeled and pure data-driven modeling. We present control-oriented parametric models with varying levels of domain-awareness that exploit known system structure and prior physics knowledge to create constrained deep neural dynamical system models. We employ universal differential equations to construct data-driven blackbox and graybox representations of the AUV dynamics. In addition, we explore a hybrid formulation that explicitly models the residual error related to imperfect graybox models. We compare the prediction performance of the learned models for different distributions of initial conditions and control inputs to assess their suitability for control.

Statistical constraints on climate model parameters using a scalable cloud-based inference framework

Abstract Atmospheric aerosols influence the Earth’s climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate simulations are highly uncertain. Constraining these processes could help improve model-based climate predictions. We propose a scalable statistical framework for constraining the parameters of expensive climate models by comparing model outputs with observations. Using the C3.AI Suite, a cloud computing platform, we use a perturbed parameter ensemble of the UKESM1 climate model to efficiently train a surrogate model. A method for estimating a data-driven model discrepancy term is described. The strict bounds method is applied to quantify parametric uncertainty in a principled way. We demonstrate the scalability of this framework with 2 weeks’ worth of simulated aerosol optical depth data over the South Atlantic and Central African region, written from the model every 3 hr and matched in time to twice-daily MODIS satellite observations. When constraining the model using real satellite observations, we establish constraints on combinations of two model parameters using much higher time-resolution outputs from the climate model than previous studies. This result suggests that within the limits imposed by an imperfect climate model, potentially very powerful constraints may be achieved when our framework is scaled to the analysis of more observations and for longer time periods.

Distributed Data-Driven Control of Network Systems

Imperfect models lead to imperfect controllers and deriving accurate models from first principles or system identification is especially challenging in networked systems. Instead, data can be used to directly compute controllers, without requiring any system identification or modeling. In this paper we propose a strategy to directly learn control actions when data from past system trajectories is distributed among multiple agents in a network. The approach we develop provably converges to a suboptimal solution in a finite number of steps, bounded by the diameter of the network, and with a sub-optimality gap that can be characterized as a function of data, and that can be made arbitrarily small. We further characterize the robustness properties of our approach and give provable guarantees on its performance when data are affected by noise or by a class of attacks.

Naruszenie prawa przez pracodawcę przy uzgadnianiu zasad kontroli trzeźwości z działającymi u niego związkami zawodowymi

Violation of the law by the employer when agreeing the rules of soberity control with the employer’s trade unions This article critically assesses the regulation, which entered into force on February 21, 2023, with regards to the procedure for establishing preventive sobriety control for employers with trade unions. The new solutions, on one hand, grant the employer the right to control the sobriety of employees if it is necessary to ensure the protection of employees or other persons’life and health or the protection of property, on the other hand it entangles the employer into an imperfect model of coordinating the rules of this control in the labour regulations. In practice, trade unions have the opportunity to block the introduction of intra-company sobriety control regulations, and at the later stage, to change them. Taking into account the purpose of this control the Author points out arguments in support of the thesis that is permissible to amend the working regulations against the will of the trade union party, which obstructionism prevents the introduction or modification of the already existing system of employee sobriety control.

Channel Estimation in RIS-Assisted MIMO Systems Operating Under Imperfections

The promising gains of reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems, in terms of extended coverage and enhanced capacity, are critically dependent on the accuracy of the channel state information. However, traditional channel estimation (CE) schemes are not applicable in RIS-assisted MIMO networks, since passive RISs typically lack the signal processing capabilities that are assumed by CE algorithms. This becomes problematic when physical imperfections or electronic impairments affect the RIS due to its exposition to different environmental effects or caused by hardware limitations from the circuitry. While these real-world effects are typically ignored in the literature, in this paper we propose efficient CE schemes for RIS-assisted MIMO systems taking different imperfections into account. Specifically, we propose two sets of tensor-based algorithms, based on the parallel factor analysis decomposition schemes. First, assuming a long-term model-where the RIS imperfections, modeled as unknown phase shifts, are static within the channel coherence time-we formulate an iterative alternating least squares (ALS)- based algorithm for the joint estimation of the unknown phase deviations and the communication channels. Then, we develop the short-term imperfection model, which allows both amplitude and phase RIS imperfections to be non-static with respect to the channel coherence time. We propose two iterative ALS-based and closed-form higher-order singular value decomposition-based algorithms for jointly estimating the channels and the unknown impairments. We also investigate the computational complexity and the identifiability of the proposed algorithms and study the effect of various imperfections on the CE quality. Simulation results show the effectiveness of our proposed tensor-based algorithms in terms of estimation accuracy and computational complexity.

Case study of the Bövik–Benveniste methodology for imperfect interface modeling of two-dimensional elasticity problems with thin layers

Case study of the Bövik–Benveniste methodology for imperfect interface modeling of two-dimensional elasticity problems with thin layers

Simulation-based Reliability Evaluation of Maintenance the Efficiency of A Repairable System

The aim of this paper is to study the asymptotic behavior of the Arithmetic Reduction of Intensity (ARI) and Arithmetic Reduction of Age (ARA) models as two imperfect maintenance models. These models have been proposed by Doyen & Gaudoin (2011), the failure process with bathtub failure intensity. The maintenance effect is characterized by the change induced by the failure intensity before and after a failure during the degradation period. To simplify the study, the asymptotic properties of the failure process are derived. Then, the asymptotic normality of several maintenance efficiency estimators can be proved in the case where the failure process without maintenance is known. Practically, the coverage rate of the asymptotic confidence intervals issued from those estimators is studied.

Knowledge Graph of University Branding Research: Scientometric Analysis Using CiteSpace

Using CiteSpace, this study visually analyzed literature on university branding found on Web of Science from 2012 to 2022 and explored the development of research in this area. The findings indicated that while university branding research has developed steadily, there remain certain problems, such as imperfect research models, a lack of quantitative research, and a limited research scope. Therefore, to further develop research in this area, it is proposed that university branding research should improve its theoretical innovations, effectively combine brand theory and higher education theory, and improve the operability of university brand theory.

Ecological Forecasting and Operational Information Systems Support Sustainable Ocean Management

In times of rapid change and rising human pressures on marine systems, information about the future state of the ocean can provide decision-makers with time to avoid adverse impacts and maximise opportunities. An ecological forecast predicts changes in ecosystems and its components due to environmental forcing such as climate variability and change, extreme weather conditions, pollution, or habitat change. Here, we summarise examples from several sectors and a range of locations. We describe the need, approach, forecast performance, delivery system, and end user uptake. This examination shows that near-term ecological forecasts are needed by end users, decisions are being made based on forecasts, and there is an urgent need to develop operational information systems to support sustainable ocean management. An operational information system is critical for connecting to decision makers and providing an enduring approach to forecasting and proactive decision making. These operational systems require significant investment and ongoing maintenance but are key to delivering ecological forecasts for societal benefits. Iterative forecasting practices could provide continuous improvement by incorporating evaluation and feedback to overcome the limitations of the imperfect model and incomplete observations to achieve better forecast outcomes and accuracy.

Hybrid Discrete Differential Evolution and Deep Q-Network for Multimission Selective Maintenance

The multimission selective maintenance problem (MSMP) for repairable systems has received increasing attention in recent years. The problem amounts to selecting a subset of feasible maintenance actions, in view of the resource limitations. For considering the realistic case of the imperfect maintenance, this article introduces a hybrid imperfect maintenance model, which is more realistic to evaluate the system reliability. The challenge of solving such kind of problems lies not only in the reliability estimation, but also in the solution method of the maintenance selection. Such decision-making problem can be effectively formulated using the Markov decision process, but it is difficult to apply current methods for solving the engineering systems with large action decision spaces. In order to solve this issue, this work puts forth a novel hybrid algorithm for the MSMP in a large multicomponent system. In the proposed method, a discrete differential evolution algorithm is developed for searching the optimal maintenance action in large-scale discrete action spaces and the deep Q-network method is utilized to approximate the effectiveness of maintenance actions and facilitate the agent training. The experiments, based on a large-scale coal transportation system, verify the effectiveness of the proposed method compared with LSDQN and differential evolution.

Implicit dynamic buckling analysis of thin‐shell isogeometric structures considering geometric imperfections

AbstractIn continuation of our previous work on nonlinear stability analysis of trimmed isogeometric thin shells, this contribution is an extension to dynamic buckling analyses for predicting reliably complex snap‐through and mode‐jumping behavior. Specifically, a modified generalized‐ time integration scheme is used and combined with a nonlinear isogeometric Kirchhoff–Love shell element to provide second‐order accuracy while introducing controllable high‐frequency dissipation. In addition, a weak enforcement of essential boundary conditions based on a penalty approach is considered with a particular focus on the inhomogeneous case of imposed prescribed displacements. Moreover, we propose a least‐squares B‐spline surface fitting approach and corresponding error measures to model both eigenmode based and measured geometric imperfections. The imperfect geometries thus obtained can be naturally integrated into the framework of isogeometric nonlinear dynamic shell analysis. Based on this idea, the different modeling methods and the influence of the appropriately considered geometric imperfections on the dynamic buckling behavior can be investigated systematically. Both perfect and geometrically imperfect shell models are considered to assess the performance of the proposed method. We compare our method with established developments in this field and demonstrate superior achievements with regard to solution quality and robustness.

Application of Big Data Clustering Algorithm in Electrical Engineering Automation

The existing control methods have the problem of imperfect automatic distribution linkage model, which leads to excessive noise in the process of practical application. This paper designs an electrical engineering automation control method based on big data clustering algorithm, obtains the load parameters of power cable laying mode, arranges the cable channels hierarchically, extracts the technical characteristics of electrical engineering automation control, integrates the equipment operation information, builds the automatic distribution linkage model, mines the data rules of power index, sets the distribution structure of electrical equipment by big data clustering algorithm, and centrally configures the functional units.Experimental Results. Compared with the other two control methods, the average noise of this control method is 19.774 dB, 35.462 dB, and 36.323 dB, which proves that the control method combined with big data clustering algorithm has better practical application effect.

Reliability-based optimization of imperfect preventive maintenance with Bayesian estimation

In this study, we present a sequential imperfect preventive maintenance model for a component subject to degradation. We use an age reduction reliability model for scheduling preventive maintenance, which is performed whenever the component’s reliability falls below the reliability threshold level, R. The problem is considered under lack of data, in which the Bayesian methodology is more appropriate than the frequentist view for estimating the unknown parameters of the model. We assume a fixed duration for preventive maintenance, whereas the duration of corrective maintenance is an exponential random variable. We model the total expected cost over all cycles and find the optimal preventive maintenance plan until a replacement based on the reliability threshold value. We also conducted a numerical study for sensitivity analysis of the developed model.