Cost Model Research Articles

Surgery scheduling in flexible operating rooms by using a convex surrogate model of second-stage costs

We study the elective surgery planning problem in a hospital with operating rooms shared by elective and emergency patients. This problem is split in two distinct phases. First, a subset of patients to be operated in the next planning period is selected and the selected patients are assigned to a block and a tentative starting time. Then, in the online phase of the problem, a policy decides how to insert the emergency patients in the schedule and may cancel planned surgeries. The overall goal is to minimize the expectation of a cost function representing the assignment of patient to blocks, case cancellations, overtime, waiting time and idle time. We model the offline problem by a two-stage stochastic program, and show that the optimal second-stage costs can be approximated by a convex piecewise linear surrogate model that can be computed in a preprocessing step. This results in a mixed integer program which can be solved very fast, even for large instances of the problem. We also describe a greedy policy for the online phase of the problem, and analyze the performance of our approach by comparing it to both heuristic methods or approaches relying on sampling average approximation (SAA) on a large set of benchmarking instances. Our simulations indicate that our approach can reduce the expected costs by as much as 30% compared to heuristic methods and it can solve problems with 1000 patients in about one minute, while SAA-approaches fail to obtain good solutions within 30 min on small instances.

Harmonizing ML and Databases: A Symphony of Data (VLDB 2024 Keynote)

Large language models (LLMs) are rapidly transforming the landscape of computing and daily life, demonstrating immense potential across diverse applications like natural language processing, machine translation, and code generation. This talk delves into the impact of LLMs on database research. Specifically, we'll examine how LLMs are fueling innovation in natural language interfaces for data interaction, highlighting current limitations and advocating for semantic data models and enhanced context to improve the accuracy of these solutions. Drawing inspiration from LLMs, we'll introduce a novel paradigm for database cost modeling, leveraging pre-trained models and fine-tuning techniques. We'll share our early-stage prototype, initial results, and outline a research roadmap highlighting numerous exciting challenges in this evolving field.

Collaborative robust dispatch of electricity and carbon under carbon allowance trading market

The launch of the carbon-allowance trading market has changed the cost structure of the power industry. There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch. In this study, a data-driven model of the uncertainty in the annual carbon price was created. Subsequently, a collaborative, robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation. The model is solved using the column-and-constraint generation algorithm. An operation and cost model of a carbon-capture power plant (CCPP) that couples the carbon market and the economic operation of the power system is also established. The critical, profitable conditions for the economic operation of the CCPP were derived. Case studies demonstrated that the proposed low-carbon, robust dispatch model reduced carbon emissions by 2.67% compared with the traditional, economic, dispatch method. The total fuel cost of generation decreases with decreasing, conservative, carbon-price-uncertainty levels, while total carbon emissions continue to increase. When the carbon-quota coefficient decreases, the system dispatch tends to increase low-carbon unit output. This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.

Uncertainty Cost Functions for Wave Energy

Wave energy is considered as an important energy source for people living near coastal areas to meet their basic energy needs. Innovative and new technologies can be used for this energy utilization to run the loads from sea waves. Due to its uncertain power availability, we can use uncertainty cost functions based on probability distributions for the availability of the operation of the wave energy microgrid. These probability distributions are based on several mapping models designed for the wave energy, wave height, and wave speed mapping to make the energy more stable and reliable. In contrast to other renewable energy sources, wave energy is inherently unpredictable, making it impossible to model with a single, globally applicable probability distribution function (PDF). The prediction of the behavior of primary wave energy for this purpose is completely based on several probability distribution functions (PDFs) which can be considered as the best for all conditions. In this paper, we have used the Weibull-Rayleigh probability distribution model to develop the uncertainty cost function for wave energy. The Monte-Carlo process is carried out to get the results supported by the Rayleigh probability distribution model consisting of wave height, and uncertain cost histograms. Cost is minimized by using the Weibull Rayleigh model for both overestimation and underestimation costs of wave energy. Monte-Carlo simulation results are further compared with analytical calculation and error between them.

Promoting TVET excellence in Palestine: a case study of WBL in secondary vocational schools

ABSTRACT Reporting findings from action research in Secondary Vocational Schools (SVSs) in Palestine contributes to our understanding of the five criteria that underpin excellence in Work-Based Learning (WBL) This paper presents a new model for promoting WBL performance for SVSs in Palestine by focusing on five key indicators derived from previous research analysis and findings for the researcher of this paper, the European Foundation for Quality Management (EFQM) and the Quality, Cost and Delivery (QCD) model. This paper addresses the question of how to promote WBL in SVSs in Palestine? In-depth interviews with 52 school personnel and 85 students from three SVSs in Palestine were conducted. A five-question descriptive questionnaire was administered to all respondents to assess the five key performance indicators to identify performance gaps. Analysis of the responses provides an understanding of the causes of the gaps and helps provide explanations to develop an action plan to improve performance.

A cost model for the investment feasibility of quality inspection technologies in the Zero Defect Manufacturing era

The current global market forces companies to focus on suitable manufacturing based on zero defect and zero waste strategies. Higher energy prices and critical raw material supply disruptions stimulate the implementation of non destructive inspection technologies for real-time quality assurance, which aims to increase high-quality products and lower production costs via better materials and energy uses. By embracing these advancements, businesses address market dynamics and strategically position themselves for sustained success in a competitive and resource-conscious world. Traditionally, in small and medium enterprises, new manufacturing or inspection equipment is acquired based on a management decision that is, in turn, based in quality purposes and does not focus deeply on the impact on the final factory cost reduction. This research focuses on developing a costing procedure to incorporate the impact of Non Destructive Inspection Technologies into the multistage cost structure for investment decisions. A case study is presented to demonstrate the applicability of the cost breakdown and return on investment for justifying the investment of the Non Destructive Inspection Technology in additive manufacturing machinery. The proposed cost model provides a framework to preliminary assess the viability of Non Destructive Inspection Technologies investments.

Assessment of Technoeconomic Opportunities in Automation for Nuclear Microreactors

Achieving full decarbonization of all economic sectors remains a challenge, especially in niche markets. For example, remote communities and industrial or mining activities detached from the main electric grid heavily rely on fossil fuels, similar to urban and industrial microgrids with combined heat and power needs. A combination of renewables and energy storage is often not suitable due to cost, reliability, intermittency, and large storage requirements. Small nuclear reactors with a flexible purpose could serve these applications. Microreactors (MR) are a class of reactors that are compact, factory manufactured, transportable, and self-regulating. Typically, they generate much less power than their large reactor counterparts. The main advantages of microreactors include the versatile nature of the energy produced, the reliability of supply, and freedom from having to transport and store large quantities of fuels on-site, coupled with the absence of dependence on an electrical grid. A strong business case is needed to move from the microreactor prototype to the commercialization phase. In fact, fossil fuels are still relatively inexpensive, and in the near term, carbon credits will be available to virtually compensate for emissions. For microreactors, one of the main costs in operation and maintenance (O&M) is their staffing levels. In this study, we investigate how to optimize the number (and thus the cost) of workers, moving from a traditional, fully manned, on-site personnel approach to an unmanned, remote personnel approach. We examine four different staffing models that can be implemented as the technology matures and evolves. We estimate the staffing needs of each model and build a business case to justify the substitution of on-site personnel with adequate technologies. To do so, we propose a cost model to quantify potential cost reductions from automating O&M activities. The model accounts for both the reduction in cost derived from the reduced number of full-time-equivalent (FTE) employees and the increase in cost derived from the need to buy new control hardware as needed. Applying the cost model that we created to different scenarios, an on-site O&M cost reduction exceeding 80% can be expected. Additionally, we found that it is more impactful to focus on automating routine O&M tasks rather than attempting to automate transient management (shutdowns, restarts, monitoring condition deviations). In fact, transients typically account for less than 1% of the total FTE time spent on the reactors.

Failure Consequence Cost Analysis of Wave Energy Converters—Component Failures, Site Impacts, and Maintenance Interval Scenarios

In the early stages of developing wave energy converter (WEC) projects, a quantitative assessment of component failure consequence costs is essential. The WEC types, deployment site features, and accessibility should all be carefully considered. This study introduces an operation and maintenance failure consequence cost (O&M-FC) model, distinct from conventional O&M models. The model is illustrated with case studies at three energetic Atlantic sites, each of which considers two types of generic floating WECs: a 300 kW point absorber (PA) with a hydraulic power-take-off (PTO) and a 1000 kW oscillating water column (OWC) with an air-wells-turbine PTO. This study compares 39 failure modes for PA and 27 for OWC in terms of direct repair costs and indirect lost production costs, examining the impact of location accessibility, capacity factors, and the mean annual energy production. The discussion revolves around the sensitive parameters. Recommendations for failure mitigations are presented, and the impact of planned maintenance (PM) during the operational phase is examined for 20 MW PA and OWC WEC projects. For a given WEC type, the method thoroughly evaluates how the location affects performance metrics. It offers a decision-making tool for determining optimal PM intervals to meet targets such as O&M costs, operating profit, or availability.

Investment management approaches: A comprehensive review of equity trading simulators, methodological challenges, and future directions

This paper provides a comprehensive review of equity trading simulators, focusing on their performance in assuring pre-trade compliance and portfolio investment management. A systematic search was conducted that covered the period of January 2000 to May 2023 and used keywords related to equity trade simulators, portfolio management, pre-trade compliance, online trading, and artificial intelligence. Studies demonstrating the use of simulators and online platforms specific to portfolio investment management, written in English, and matching the specified query were included. Abstracts, commentaries, editorials, and studies unrelated to finance and investments were excluded. The data extraction process included data related to challenges in modern portfolio trading, online stock trading strategies, the utilization of deep learning, the features of equity trade simulators, and examples of equity trade simulators. A total of 32 studies were included in the systematic review and were approved for qualitative analysis. The challenges identified for portfolio trading included the subjective nature of the inputs, variations in the return distributions, the complexity of blending different investments, considerations of liquidity, trading illiquid securities, optimal portfolio execution, clustering and classification, the handling of special trading days, the real-time pricing of derivatives, and transaction cost models (TCMs). Portfolio optimization techniques have evolved to maximize portfolio returns and minimize risk through optimal asset allocation. Equity trade simulators have become vital tools for portfolio managers, enabling them to assess investment strategies, ensure pre-trade compliance, and mitigate risks. Through simulations, portfolio managers can test investment scenarios, identify potential hazards, and improve their decision-making process.

Experimental and numerical investigation on seismic performance of novel cable-restraining composite rubber bearings for highway bridges

Introducing cable restrainers into isolation bearings is promising to enhance the seismic performance of highway bridges. However, limitations exist in cost, performance, and analysis models of current products typically composed of flat sliding bearings (FBs) and separated cables. Therefore, this study proposes a novel cable-restraining composite rubber bearing (CRCRB) comprising a composite rubber bearing (CRB) as the bearing body and continuous cables. Cyclic tests were conducted on 12 specimens, including cable-restraining FBs and CRCRBs. Then, restoring force models of the bearing bodies were developed via theoretical analysis. Those of the cables were obtained from numerical models based on derived cable profiles. Finally, incremental dynamic analysis (IDA) was performed to evaluate the effects of the CRCRBs and cable models on structural responses. The test results verify the feasibility of the new cable layout and the advantages of the CRBs as the bearing body in restoring force, damping capacity, and residual displacement. As both the radii of the curved segments and the inclination angle of the linear one in the cable profiles decreased, the cable stiffness kept rising during loading. Cable sliding at clamps enlarged the cable-free displacement and residual deformation, and was relieved by adding clamps. The proposed models fit the test data well. The IDA results illustrate the superiority of the CRCRBs in mitigating bridge damage and highlight the importance of using the precise multi-linear cable model instead of the simplified linear one to avoid overestimating responses.

Wire arc additive manufacturing of a high-strength low-alloy steel part: environmental impacts, costs, and mechanical properties

Additive manufacturing (AM) technologies have demonstrated a promising material efficiency potential in comparison to traditional material removal processes. A new directed energy deposition (DED) category AM process called wire arc additive manufacturing (WAAM) is evolving due to its benefits which include faster build rates, capacity to build large volumes, and inexpensive feedstock materials and machine tools compared to more technologically mature powder-based AM technologies. However, WAAM products present challenges like poor surface finish and lower dimensional accuracy compared to powder-based processes or machined parts, prevalence of thermal distortions, residual stresses, and defects like porosity, cracks, and humping, often requiring post-processing operations like finish machining and heat treatment. These post-processing operations add to the production cost and environmental footprint of WAAM-built parts. Therefore, considering the opportunities and challenges presented by WAAM, this paper analyses the environmental impact, production costs, and mechanical properties of WAAM parts and compares them with those achieved by laser powder bed fusion (LPBF) and traditional computer numerical control (CNC) milling. A high-strength low-alloy steel (ER70S) mechanical part with medium complexity was fabricated using WAAM. Based on the data collected during this experiment, environmental impact and cost models were built using life cycle assessment and life cycle costing methodologies. WAAM was observed to be the most environmentally friendly option due to its superior material efficacy than CNC milling and has a better energy efficiency than LPBF. Also, WAAM was the most cost-friendly option when adopted in batch production for batch sizes above 3. The environmental and cost potential of WAAM is amplified when used for manufacturing large products, resulting in significant material, emission, and cost savings. The fabricated WAAM part demonstrated good mechanical properties comparable to that of cast/forged material. The methodology and experimental data presented in this study can be used to calculate environmental impacts and costs for other products and can be helpful to manufacturers in selecting the most ecofriendly and cost-efficient manufacturing process.

Research and economic evaluation on novel pulse superconducting magnet power supply topology with energy storage for fusion devices

Large capacity fusion devices power supply poses a significant challenge to the stability of power grid, as it can lead to power outages and jeopardize the safety of fusion devices. And traditional distribution methods result in a significant waste of resources. This paper proposes novel topologies with integrated energy storage. In these topologies, high-amplitude pulsed power is supplied by the energy storage devices, while low-amplitude stable power is obtained from the grid. This decouples the pulsed power from power grid, and significantly reducing its impact. Moreover, it can reduce the design capacity of distribution equipment and lowers investment costs. To optimize the deployment of the energy storage device, a hybrid topology is proposed, which further reducing the cost of the novel power supply. Additionally, a cost model for the fusion power supply is developed and validated using simulation data from ITER. Through the case study shows that the HEPS topology saves more than 10 % of the investment cost and about 60 % of the annual operating cost compared to the traditional converter topology.

An Activity-Based Costing model applied to the management process of patients with migraine at IRCCS Mondino Foundation

Background and aim: The ageing of the general population, with the increase in chronic diseases, poses the need for using technological innovation to face the growing healthcare needs. The present analysis applied the Activity-Based Costing tool in migraine patients followed at the IRCCS Mondino of Pavia to formalize and quantify the costs of the whole patient’s management process and to estimate the overall economic burden of migraine from the perspective of the National Health Service (NHS). Methods: A two-stage analysis was conducted. The first stage aimed at drawing patient’s pathway, defining all the possible activities and actors involved in the healthcare delivery and treatment for migraine at the IRCCS during year 2022. The second phase was the development of a model to estimate the costs of the entire process by pricing the individual activities. Results: The described model yielded an estimated total annual cost for the overall management of migraine of € 1,222,392, corresponding to a direct cost for a therapy of approximately 6 months per single patient of € 814. The most impactive items on the estimated annual expenditures were hospitalizations, followed by diagnostic examinations after follow-up visits and drugs dispensed by Hospital Pharmacy for home-administration (monoclonal antibodies). Conclusions: This analysis allowed detailing the single activities and resources used in migraine patient’s pathway and then estimating the direct costs sustained by the NHS. The model could be translated to other diseases for optimizing the diagnostic/therapeutic and economic management of assisted patients and improving healthcare resource allocation.

Comparative Cost Modeling of Battery Cell Formats and Chemistries on a Large Production Scale

As lithium-ion batteries increasingly become a cornerstone of the automotive sector, the importance of efficient and cost-effective battery production has become paramount. Even though electric vehicle battery cells are produced in three different geometries—cylindrical, prismatic, and pouch—no specific model exists to compare the manufacturing costs of producing cells with different geometries but similar performances. In this paper, we present a process-based cost model with a cell design functionality which enables design and manufacturing cost prediction of user-defined battery cells.

Heterogeneous impact of cost of carry on corporate money demand

AbstractTransaction cost model predicts opportunity costs should negatively affect money demand. Examining the effect of cost of carry (CC) on cash holdings at the firm‐level, rather the average effect for entire population, we find that such a pervasive negative relation does not hold in times of low interest rate with about a 10% chance of observing positive effects. Firm size emerges as the primary driver of this heterogeneity, demonstrating a hump‐shaped effect on the cash‐CC link. Our findings suggest that policymakers should track the distributional impacts of opportunity cost of money demand over time to better evaluation of monetary policy.

A Microchannel Heat Exchanger Produced From a Metal Matrix Composite by Hybrid Laser Powder Bed Fusion and Inkjet Printing

Abstract This paper explores the production of an oxide dispersion strengthened (ODS) 304L stainless steel microchannel heat exchanger (HX) using a hybrid additive manufacturing process of laser powder bed fusion and inkjet printing. The study investigates the capabilities and economics of the hybrid inkjet-laser powder bed fusion (LPBF) process and evaluates the dimensional accuracy, functionality, and mechanical properties of the resulting ODS alloy. The effectiveness and pressure drop of the ODS heat exchangers produced by the hybrid LPBF tool are also determined. Results show that the inkjet-doped samples have a lower mean channel height with higher standard deviation than samples produced by LPBF alone. This is attributed to greater absorption of laser energy for the powder coated with the oxide precursor. The economic analysis shows that the hybrid process has a potential for reducing the unit cost of the heat exchanger based on cost modeling assumptions.

Operational Cost Models for an Earth Station System Using 2-Parallel and 4-Parallel Configurations

Operational Cost Models for an Earth Station System Using 2-Parallel and 4-Parallel Configurations

A cost modelling methodology based on machine learning for engineered-to-order products

Recent scientific studies are targeted at applying and assessing the effectiveness of Machine Learning (ML) approaches for cost estimation during the preliminary design phases. To train ML prediction models, comprehensive and structured datasets of historical data are required. This solution is inapplicable when such information is unavailable or sparse due to the lack of structured datasets. For engineered-to-order products, the number of historical records is often limited and strongly influenced by different purchasing or manufacturing strategies, thus requiring complex normalisation of such data.This method overcomes the above limitations by presenting an ML-based cost modelling methodology for the conceptual design that is applicable even when historical data are insufficient to train the prediction algorithms. The training dataset is generated through an analytical and automatic software tool for manufacturing cost estimation. Such a tool, starting from a 3D model of a product, can quickly and autonomously assess the related cost in different scenarios. An extensive and structured training dataset can be easily generated. The proposed methodology was based on CRISP-DM (Cross Industry Standard Process for Data Mining).Cost engineers of an Oil & Gas company used the method to develop parametric cost models for discs and spacers of an axial compressor. The solution guarantees lower error (7% vs 9%) and significant time-saving (minutes instead of hours) than estimations based on other approaches. Cost models are more comprehensive (capable of analysing different scenarios), explainable (not conceived as a black box), and self-learning (can be updated by extending the training dataset).

RNDDNet: A residual nested dilated DenseNet based deep-learning model for chilli plant disease classification

The most significant peril to food safety arises from plant diseases, capable of substantially diminishing both the quantity and quality of agricultural yields. Identifying these plant diseases stands out as the foremost challenge within the agricultural sector. Convolutional and deep neural networks prove effective in resolving image classification challenges within the realm of computer vision. Numerous Deep Neural Network(DNN)-based structures have been employed to diagnose plant diseases. Many DNN models in the field make use of various iterations of Dense and DenseNet layers in order to enhance the receptive field and capture intricate features within the data. However, it is important to note that such models often come with a significant computational burden and can introduce aliasing artifacts due to their complexity and resource-intensive nature. To overcome those limitations, we proposed a novel Residual Nested Dilated DenseNet based deep-learning (RNDDNet) model in this paper. Residual Nested Dilated DenseNet model residual connections are achieving the required receptive field, and their dilation factors are effective in extracting more features. The RNDDNet model exhibits the highest level of accuracy in identifying plant diseases. This research introduces a less computational cost and compact model to detect diseases in plant leaves. The proposed model functions to identify diseases, utilizing a dataset comprising 3,800 photographs of chilli leaves, categorized into six distinct classes: five disorder classes and one healthy chilli class. Through experimentation, the outcomes indicate that the suggested model achieves an accuracy of 98.09 %, along with a precision of 97 %, a recall of 97.25 %, and an F1 score of 97.25%. The presented approach demonstrates its superiority over existing methodologies.

Research on energy storage capacity optimization of rural household photovoltaic system considering energy storage sharing.

With the promotion of the photovoltaic (PV) industry throughout the county, the scale of rural household PV continues to expand. However, due to the randomness of PV power generation, large-scale household PV grid connection has a serious impact on the safe and stable operation of the distribution network. Based on this background, this paper considers three typical scenarios, including household PV without energy storage, household PV with distributed energy storage, and household PV with centralized energy storage. Then, a calculation model for PV local consumption rate and annual net cost under different scenarios is constructed. Combined with a natural village in Shandong Province, the PV local consumption rate and annual net cost under three scenarios are compared and analyzed, and the potential of energy storage sharing in reducing storage capacity and improving PV local consumption is explored. The results show that configuring energy storage for household PV can significantly improve the power self-balancing capability. When meeting the same PV local consumption, household PV centralized energy storage can achieve smaller energy storage configuration and lower cost compared to household PV distributed energy storage. Finally, suggestions are proposed to further promote the development of household PV energy storage system. The research results can provide reference for improving the local consumption of rural household PV and accelerating the application of household PV energy storage system.