Replacement Decisions Research Articles

Effectiveness Evaluation of Replacement Policies for On-Chip Caches in Multiprocessors

A Cache plays a vital role in improving performance in the multicore environment, especially the Last Level Cache (LLC). The improvements in performance are based on the block size, associativity, and replacement policies. Most of the papers concentrate on traditional Least Recently Used (LRU) based replacement policies for their replacement decisions. Unfortunately, the replacement decisions do not enhance performance of the cache as expected. An enhanced modified Pseudo LRU policy is proposed, which is an approximation of LRU. The proposed methodology uses counters to enhance the confidence of replacement decisions based on the history of the replaceable blocks in cache. It is very clear from the Simulation results that the replacement scheme proposed exhibits better performance improvement in terms of miss ratio of about 3% and energy efficiency of about 2% on an average.

Transitioning to sustainable freight transportation by integrating fleet replacement and charging infrastructure decisions

The transportation sector is the largest contributor to global greenhouse gas emissions. Disruptive technological changes in this sector, such as alternative fuel vehicles, are crucial for emission reduction. We show how a cost-minimizing strategic transition plan to adopt electric trucks over time can be developed for a firm that owns and operates a fleet of diesel trucks. We consider the case in which the firm decides to invest in the charging infrastructure required to support this transition, either because the public charging infrastructure is currently inadequate or for strategic reasons. The congestion effect at the charging stations, the charging times, and the potential loss of productive driving time due to detours to reach charging stations are explicitly considered. By developing an independence property, we are able to model this problem as a linear integer program without specifying origins and destinations. We illustrate the resulting transition plan with realistic parameter configurations. Our results indicate that a firm with high transportation demand density over a given service region significantly benefits from adoption of electric trucks, while also enjoying substantial carbon emissions savings. High demand density also favors smaller battery capacity with shorter ranges under the optimized charging network capacity, even though larger battery capacity would increase productivity with extended ranges. Our analysis also offers insights for governments and regulators regarding the impact of several influential factors such as carbon cost, content of renewable energy in electricity mix, diesel engine efficiency, and subsidizing the charging infrastructure. Additionally, we present an extension to the model that allows for different modalities of partnership in the infrastructure investment; notably public-private and private-private partnerships. While in general our results suggest that such partnerships are beneficial to all involved, the amount and relative distribution of the potential gains depend on the topography and on the density of charging infrastructure.

A Graph Neural Network Approach for Caching Performance Optimization in NDN Networks

Named Data Networking (NDN) is a new architecture with in-network caching ability. NDN nodes can cache data packets in their cache store to satisfy further requests. Accurately caching popular content across the network is essential for NDN to reduce the traffic workload and improve network efficiency. However, traditional caching algorithms are not good at predicting future dynamic content popularity. In our paper, we propose a Graph Neural Network-based (GNN-based) caching strategy to optimize the caching performance in NDN. First, our paper utilizes a Convolutional Neural Network (CNN) to extract time-series features for each NDN node. Secondly, GNN is applied to make content caching probability predictions for each NDN node. Third, at each NDN node, a cache replacement decision is made based on its content caching probability ranking, and content with high caching probability replaces content with low probability. We compare our GNN-based caching strategy with three deep learning-based caching techniques, which are 1D-Convolutional Neural Network (1D-CNN), Long Short-Term Memory Encoder-Decoder (LSTM-ED), and Staked Auto Encoder (SAE), and three classical benchmark caching strategies, which are Least Frequently Used (LFU), Least Recently Used (LRU) and First-in-first-out (FIFO). All caching scenarios are simulated in the Mini-NDN platform and evaluated on the tree and arbitrary network topologies. Empirical results suggest that the GNN-based caching approach can achieve around a 50% higher cache hit ratio and a 30% lower latency in the best case than other deep learning-based caching strategies.

Asynchronous Backtracking variant applied to a rolls replacement process within a wire rod mill

There is a wide number of application problems included in multi-agent systems and distributed artificial intelligence which can be described as the issue of finding a consistent combination satisfying all the existing inter-agent constraints, that is, they are distributed constraint satisfaction problems (DisCSPs). In this paper, a new technique for solving such kind of issues when they can be modelled as a succession of interrelated elements with constraints among an agent and its immediately following, having one of the agents the ability to notify either that the problem has been solved or that there are not any possible solutions, is presented. It has been developed using Asynchronous Backtracking, one of the most known algorithms for solving DisCSPs as a basis. To validate its applicability, it has been tested in a wire rod rolling mill for rolls replacement decision making process. It was implemented using the PADE multi-agent framework. The results demonstrate the method is valid to solve this kind of problems usually solved with recursive algorithms such as backtracking.

Sustainable Renewal of Underground Parking Space in the Scenario of Shared Autonomous Vehicles

Shared autonomous vehicles (SAVs) are a major development direction in international scientific and technological innovation. One of the most popular features of SAVs in the urban space is that they can significantly reduce the need for parking. The urban underground parking space (UPS) is currently the largest static traffic space, especially in high-density urban centers. Under the SAV scenario, the need for the renewal of UPS will increase in the near future. However, renewal of the UPS is difficult due to its special form features, which are greatly restricted by the external environment, thus necessitating targeted methods and strategies. This research first conducted field investigations and data collection on the spatial morphology and service conditions of typical UPSs in different areas of Hangzhou city. Based on the driver status response and the multi-objective attribute models, the time-series evaluation method and function replacement decision model for the sustainable renewal of underground parking were established. The research also discusses appropriate design strategies for the combination of spatial characteristics and functional replacement goals of typical samples. The conclusions will provide scientific guidance for the future design practice of architects and urban designers in SAV.

Integrated approach for pipe failure prediction and condition scoring in water infrastructure systems

Pipe failures in water distribution infrastructure have significant economic, environmental, and public health impacts. To alleviate these impacts, pipe deterioration modeling has been increasingly implemented to characterize and predict pipe failure patterns with the aim of prioritizing repair and replacement decisions. Logistic regression has been recognized in recent literature as a strong candidate for failure prediction modeling. However, previous studies have often been limited to demonstrating the application of logistic regression for estimating failure probabilities. This study builds on previous efforts by proposing an approach for implementing logistic regression into a holistic framework for asset management decision-making. This framework incorporates logistic regression modeling, with a flexible time-interval, into a practical condition scoring methodology that accounts for the attitude of water utilities towards risk. The developed framework is demonstrated and tested on a 20-year pipe failure dataset of a large metropolitan US city. The logistic regression model displayed high accuracy in estimating the probability of failure within different time intervals, and the scoring method showed a reasonable ability to predict the criticality of repair decisions for pipes based on their condition.

State of Health Estimation of Lithium-Ion Battery Using Time Convolution Memory Neural Network

The accurate state of health (SOH) estimation of lithium-ion batteries enables users to make wise replacement decision and reduce economic losses. SOH estimation accuracy is related to many factors, such as usage time, ambient temperature, charge and discharge rate, etc. Thus, proper extraction of features from the above factors becomes a great challenge. In order to extract battery’s features effectively and improve SOH estimation accuracy, this article proposes a time convolution memory neural network (TCMNN), combining convolutional neural networks (CNN) and long short-term memory (LSTM) by dropout regularization-based fully connected layer. In experiment, the terminal voltage and charging current of the battery during charging process are collected, and input and output data sets are sorted out from the experimental battery data. Due to the limited equipment in the laboratory, only one battery can be charged and discharged at a time; the amount of battery data collected is relatively small, which will affect the extraction of features during the training process. Data augmentation algorithms are applied to solve the problem. Furthermore, in order to improve the accuracy of estimation, exponential smoothing algorithm is used to optimize output data. The results show that the proposed method can well extract and learn the feature relationship of battery cycle charge and discharge process in a long time span. In addition, it has higher accuracy than that of CNN, LSTM, Backpropagation (BP) algorithm, and Grey model-based neural network. The maximum error is limited to 3.79%, and the average error is limited to 0.143%, while the input data dimension is 514.

Monitoring the health status of water mains using a scorecard modelling approach

Abstract There has been considerable research into prediction of water mains failure, however, those models are very complex and fail to convey the message of the health status of an asset to the relevant stakeholders. The study developed a ‘pipe health scorecard’ based on historical failure data which could be used for operation, maintenance, refurbishment, or replacement decisions by a water utility. This scorecard model was developed by using 160,413 pipe-condition data sets from the South Australian Water Corporation over ten years. Measures such as the Kolmogorov–Smirnov (KS) statistic, Area Under the ROC Curve (AUC), and Population Stability Index (PSI) showed the model is strong enough to predict the health status of water mains. The study found the factors influencing water mains failure to be in the order of importance: length, material, age, location (road vs verge), diameter, and operating parameters. The development of a simple but reliable model for the assessment of the health status of water mains will have major benefits to the water utility with the ability to identify and potentially replace water pipes prior to failure. Additional benefits of flexible scheduling of maintenance and replacement programs would contribute to cost savings.

Wind project performance with age: Policy, technology, markets, and the maturing wind power industry

U.S. wind generation capacity has grown by a factor of nearly 50 times in the past 20 years; now nearly a third of wind capacity is 10 years old or older. This ageing of this fleet affects the productivity of existing investments and raises the stakes for replacing these assets through repowering. Focusing on the Texas wind power fleet to examine declining productivity over time, and how those changes factor into replacement and retirement decisions, we corroborate earlier decline findings but observe a slower rate of repowering than in other settings. We then explore repowering decisions and find evidence that technological change has favored relatively unproductive sites, in contrast to the previous literature on this topic. We explore the policy implications of our findings, in particular the desirability and feasibility of using policy tools to promote more repowering.

Exploring a Hybrid Voting-based Eviction Policy for Caches and Sparse Directories on Manycore Architectures

In manycore systems, eviction decisions related to caches and memory coherence greatly impact system performance, thereby emphasizing their importance. Extensive research has produced numerous standalone eviction policies such as LRU, LFU, FIFO, etc. all aiming to attain the Bélády optimum solution. Standalone eviction policies optimize for a single attribute (recency, frequency, etc.), limiting their impact on applications exhibiting non-uniform memory access patterns. The Hybrid Voting-based Eviction Policy (HyVE) extends multiple standalone eviction policies with a ranking system and evaluates them using concepts from the voting theory domain. The goal of HyVE is to make better replacement decisions by creating a consensus among its constituent eviction policies. With its inherent voting properties, HyVE takes different replacement decisions compared to its standalone counterparts, making it a unique and new eviction policy. We deploy and evaluate HyVE as part of two case-studies: last-level cache replacement decisions in a generic manycore environment, and sparse directory eviction decisions on a tile-based distributed shared memory (DSM) architecture. We explore different variants of HyVE, and evaluate them using workloads from the PARSEC and SPLASH-2 benchmark suites in a simulation environment. We also compare HyVE to state-of-the-art set-dueling and learning-based eviction policies. For last-level cache replacement decisions, HyVE reduces cache misses by 7.4% compared to the LRU policy, whereas DRRIP and Hawkeye reduce cache misses by 5.5% and 9.2% respectively compared to the LRU policy. Though Hawkeye exhibits better performance on average, HyVE offers a unique advantage for certain workloads by using a voting-based approach to solve the replacement problem. For sparse directory eviction decisions, results show that HyVE reduces coherence traffic and execution time by up to 11% compared to the LRU policy. We have synthesized HyVE on an FPGA prototype. Hardware analysis results show that HyVE’s constituent policies contribute the most to its overheads, while HyVE’s ranking and voting extensions do not add significant overheads. Timing analysis results show that HyVE’s logic delay is comparable to that of standalone eviction policies. Lastly, we evaluate HyVE on the FPGA prototype using characteristic micro-benchmarks that further emphasize HyVE’s ability to remain agnostic to varying data access patterns.

Transportation infrastructure asset management modeling using Markov decision process under epistemic uncertainties

Purpose Transportation infrastructure asset management has long been an active but challenging problem for agencies, which urges to maintain a good state of their assets but faces budgetary limitations. Managing a network of transportation infrastructure assets, especially when the number is large, is a multifaceted challenge. This paper aims to develop a life-cycle cost analysis (LCCA) based transportation infrastructure asset management analytical framework to study the impacts of a few key parameters/factors on deterioration and life-cycle cost. Using the bridge as an example infrastructure type, the framework incorporates an optimization model for optimizing maintenance, repair, rehabilitation (MR&R) and replacement decisions in a finite planning horizon. Design/methodology/approach The analytical framework is further developed through a series of model variations, scenario and sensitivity analysis, simulation processes and numerical experiments to show the impacts of various parameters/factors and draw managerial insights. One notable analysis is to explicitly model the epistemic uncertainties of infrastructure deterioration models, which have been overlooked in previous research. The proposed methodology can be adapted to different types of assets for solving general asset management and capital planning problems. Findings The experiments and case studies revealed several findings. First, the authors showed the importance of the deterioration model parameter (i.e. Markov transition probability). Inaccurate information of p will lead to suboptimal solutions and results in excessive total cost. Second, both agency cost and user cost of a single facility will have significant impacts on the system cost and correlation between them also influences the system cost. Third, the optimal budget can be found and the system cost is tolerant to budge variations within a certain range. Four, the model minimizes the total cost by optimizing the allocation of funds to bridges weighing the trade-off between user and agency costs. Originality/value On the path forward to develop the next generation of bridge management systems methodologies, the authors make an exploration of incorporating the epistemic uncertainties of the stochastic deterioration models into bridge MR&R capital planning and decision-making. The authors propose an optimization approach that does not only incorporate the inherent stochasticity of bridge deterioration but also considers the epistemic uncertainties and variances of the model parameters of Markovian transition probabilities due to data errors or modeling processes.

Examination of Electrolyte Replacements in the ICU Utilizing MIMIC-III Dataset Demonstrates Redundant Replacement Patterns.

Electrolyte repletion in the ICU is one of the most ubiquitous tasks in critical care, involving significant resources while having an unclear risk/benefit ratio. Prior data indicate most replacements are administered while electrolytes are within or above reference ranges with little effect on serum post-replacement levels and potential harm. ICU electrolyte replacement patterns were analyzed using the MIMIC-III database to determine the threshold governing replacement decisions and their efficiency. The data of serum values for potassium, magnesium, and phosphate before and after repletion events were evaluated. Thresholds for when repletion was administered and temporal patterns in the repletion behaviors of ICU healthcare providers were identified. Most electrolyte replacements happened when levels were below or within reference ranges. Of the lab orders placed, a minuscule number of them were followed by repletion. Electrolyte repletion resulted in negligible (phosphate), small (potassium), and modest (magnesium) post-replacement changes in electrolyte serum levels. The repletion pattern followed hospital routine work and was anchored around shift changes. A subset of providers conducting over-repletion in the absence of clinical indication was also identified. This pattern of behavior found in this study supports previous studies and may allude to a universal pattern of over-repletion in the ICU setting.

Performance-Based Building System Manufacturer Selection Decision Framework for Integration into Total Cost of Ownership Evaluations

Facility managers are often faced with building system procurement or replacement decisions, requiring them to select a system from among competitive manufacturers. Total cost of ownership (TCO) criteria, informed by built assets in operation in the manager’s portfolio, provides some information to select the right asset manufacturer. However, managers must also consider technical performance to complete a more comprehensive analysis. Performance can be calculated using asset parameters like condition, age, and variation in condition to aid in TCO assessments. Leveraging past research and approaches, performance is calculated using an additive model that scales each parameter using a standard normalization technique and employs weighting factors to account for decision-maker input. Data from 20 Air Force installations across the US and two asset types are analyzed, showing the utility of a performance metric. This analysis shows that as manufacturer diversity in portfolios decreases, performance increases for most of the asset types modeled. This paper presents a new performance metric that can be used as an additional criterion in TCO models to build a more robust decision framework.

Effects of cyclic strain hardening on performance of eccentrically braced frames

Performance based seismic design (PBSD) procedures have been developed in recent years. Studies conducted in the past revealed that strain hardening ratio (SHR) used in numerical modeling can be influential on the performance of steel structural systems. In this paper, the effects of SHR on the performance assessment of eccentrically braced frame (EBF) buildings were studied by using FEMA P-58 methodology. The numerical model was validated using the pseudo-dynamic test results of the DUAREM structure. Seismic performance of six archetype EBFs with different number of stories and link length to bay width (e/L) ratios were evaluated under design basis earthquake (DBE), maximum considered earthquake (MCE), and collapse level earthquake (CLE). Irreparable residual drift, repair cost and unsafe placards were calculated for SHR ranging between 0 and 0.01. The results showed that the residual interstory drift ratio is more influenced by the SHR when compared with the interstory drift ratio, link rotation angle and story acceleration. The replacement decision was found to depend on the SHR, the number of stories, the e/L ratio, and the seismic hazard. Under MCE and CLE level events, almost all of the archetypes with SHR ≤ 0.003 required replacement while replacement can be avoided for some of the archetypes with SHR > 0.003. Under DBE level events, 3-story archetypes and 6-story archetypes with SHR ≥ 0.002 did not require replacement. Nine-story archetype with e/L = 0.08 required replacement for SHR = 0 only, while archetypes with e/L = 0.12 required replacement for SHR ≤ 0.003. The study demonstrated the strong influence and sensitivity of the repair cost to the SHR.

A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion

Tool wear monitoring during the cutting process is crucial for ensuring part quality and productivity. A data-driven monitoring approach based on radar map feature fusion is proposed for tool wear recognition and quantitative prediction, aiming at tracking the evolution of tool wear comprehensively. Specifically, the sensitive features from multi-source signals are fused by a radar map, and health indicators capable of characterizing the tool wear evolution are obtained. For the recognition of tool wear state and the quantitative prediction of tool wear values, the Adaboost Decision Tree (Adaboost-DT) ensemble learning model and stacked bi-directional long short-term memory (SBiLSTM) deep learning network are established, respectively. Experimental results demonstrated that the proposed approach could recognize the current wear state quickly and accurately whilst predicting wear values based on limited historical data available. Combining tool wear recognition and prediction results contributes to making a more flexible tool replacement decision in intelligent manufacturing processes.

Optimal Subsidy Schemes and Budget Allocations for Government‐Subsidized Trade‐in Programs

Applications of government subsidies to speed up consumer trade‐ins of used products can be commonly observed in practice. This study investigates the design of such trade‐in subsidy programs and aims to provide implementable insights for practice. In particular, we focus on two open problems in the literature, (i) how to optimally allocate the subsidy budget among the multiple products covered by the trade‐in program, and (ii) how to most effectively utilize the assigned budget to incentivize consumer trade‐ins for each product. We develop a three‐stage Stackelberg game model that captures the essence of the interaction between the government's subsidy decision, the manufacturer's trade‐in rebate decision, and the consumer's product replacement decision. We show that a sharing subsidy scheme under which the government subsidy is proportional to the manufacturer's rebate is more effective in encouraging consumer trade‐ins than fixed‐amount subsidies. Moreover, a product with a higher environmental impact, a larger market size, a longer lifespan, or a lower value to consumers typically demands a larger subsidy budget allocation. We further use our results to derive a simple proportional budget allocation rule that can provide robust and near‐optimal performance. We illustrate our results by a case study based on the “old‐for‐new" program in China that subsidizes home appliance trade‐ins. Our results indicate that policy makers should pay attention to the correlation between government subsidies and manufacturer's rebate as well as key product and market characteristics when designing a subsidy scheme for trade‐in programs.

Improving Bridge Expansion and Contraction Installation Replacement Decision System Using Hybrid Chaotic Whale Optimization Algorithm

Bridge expansion and contraction installation (BECI) has proved to be an essential component of the bridge structure due to its stability, comfort, and durability benefits. At present, traditional replacement technologies for modular type, comb plate type, and seamless type BECIs are widely applied worldwide. Nevertheless, it is unfortunate that the research conducted on decision-making (DM) approaches for the technical condition assessment and the optimal replacement plan selection of existing BECIs remain scarce, which results in the waste of resources and the increase in cost. Therefore, a BECI technical condition assessment approach, which contains specific on-site inspection regulations with both qualitative and quantitative descriptions, is proposed in this research, and a corresponding calculation program has been developed based on the MATLAB platform, which provides the basis for the necessity of replacement. Simultaneously, the hybrid chaotic whale optimization algorithm is designed and performed to improve and automate the process of optimal replacement plan selection under the assistance of the analytic hierarchy process (AHP), where both the achievement in consistency modification and the reservation of initial information are perused, and its superiority and effectiveness are verified via the comparative experimental analysis. The improved BECI replacement decision system is established, and the corresponding case study demonstrates that the proposed system in this research proves reasonable and feasible. The improved system can effectively assist bridge managers in making more informed operation and maintenance (O and M) decisions in actual engineering projects.

Revenue‐Sharing Contracts Under Quality Uncertainty in Remanufacturing

Business users (suppliers) of information technology (IT) equipment often contract with third party remanufacturers (3PRs) for the end‐of‐use disposition of their used electronic equipment. Our goal in this research is to develop a model to analyze contracts consisting of an upfront payment ( t) and a revenue share ( r) between a supplier and 3PR, study their impact on disposition decisions, and explore the impact of 3PR risk‐aversion and quality uncertainty on optimum contracts. We model a Stackelberg game where the supplier offers risk protection from quality uncertainty to a 3PR. The 3PR faces a convex increasing remanufacturing cost and a sales revenue that is increasing and concave in the volume resold. We show general conditions under which a unique contract exists and can be expressed in closed form. We calibrate our model using a dataset on supplier contracts and equipment conditions provided by a 3PR. Our managerial insights are derived from the data and summarized as follows. First, there is a benefit to suppliers from characterizing the quality of used assets. Therefore, they should consider adjusting contracts to reflect asset quality. Second, suppliers can increase profit by contracting based on quality grade and using a portfolio of contracts. Third, we show that the asset age impacts contract terms, with suppliers with assets at a lower age keeping a higher revenue share and observing a greater fraction of equipment remanufactured. This suggests that suppliers should consider the benefits of disposition contracts when making equipment replacement decisions.

An analytical model of the replacement costs of aircraft engine life-limited parts

PurposeThe purpose of this paper is to present novel recursive expressions for modelling the replacement costs of aircraft engine life-limited parts during shop visits to assist engine operators in both evaluating their decisions regarding the applied life-limited parts management strategies and tracking the replacement costs consistently throughout the life of the engine.Design/methodology/approachThe replacement costs of aircraft engine life-limited parts are modelled analytically in this research, which strives to quantify the costs of used and unused lives of the replaced parts, incurred during engine shop visit events. Inputs for this model include the list price of life-limited parts, the replacement decisions made on all previous shop visits and the number of cycles the engine has operated at different thrust ratings on all previous operating intervals.FindingsThe average annual escalation rate of life-limited parts list prices was shown to range from 5% to 7%. The presented model is not only suitable for calculating the costs of used and unused lives of life-limited parts during past engine shop visit events but also for application in the life-limited parts replacement cost forecasting and optimisation models.Originality/valueUniquely derived recursive expressions represent the final result of the developed model which, to the authors’ knowledge, had not been studied elsewhere in the academic literature. The analysis of aircraft engine life-limited part list prices carried out to account for the average annual escalation rate enables the prediction of replacement costs during subsequent shop visits.

Financial Reporting Material Misstatements, Earnings Conservatism and Managerial Replacement Decisions

Purpose: Based on signal theory and legitimacy theory, this paper examines whether firms with financial reporting misstatements (restatements) would prefer conservative financial reporting to send signals regarding their determinants of improving financial reporting credibility and legitimate organizational image in Taiwan. This paper further examines whether these firms reduce the demand for conservative financial reporting after replacing managers in the reveal of restatements.