Cost Of Operations Research Articles

Covert political campaigning: Mapping the scope, scale, and cost of cross-platform election influence operations

This study investigates gray areas of contemporary political campaigning from a political economy perspective. Using qualitative field and digital methods, computational methods, and economic modeling, it analyzes the scope, scale, and cost of commissioning social media influencers in the 2022 Philippine Elections across Facebook, Twitter, YouTube, and TikTok. The researchers find that there is a high demand for influencers to campaign for candidates, characterized by premium and dynamic incentives under informal and obscure arrangements. We identified 1425 influencer accounts across the four social media platforms that engage in covert political campaigning and categorized them into seven types. The cost of these influencer-led campaigns is estimated to range from USD 27M following a pay-per-post compensation model, to USD 10.9M using a retainer model. This article serves as a model for both election research and election policy by providing a modular framework that addresses knowledge gaps in various country contexts.

Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques

Milling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. Inserts for compound cutting tools can reduce the cost of operations by optimizing their lifespan. This study analyzes the flank wear of cutting tools in milling machines, with an emphasis on evaluating different approaches to predict their lifespan. It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. Each model is evaluated independently, and this comparative analysis aims to determine which modeling strategy best captures the intricate interactions between various process variables affecting tool wear. This method ensures greater efficiency and accuracy than conventional techniques, providing a scalable, resource-efficient solution for reliable and insightful tool wear predictions. The results obtained from the dataset of an insert tool can be extrapolated to other milling inserts and demonstrate the progression of tool wear over time under varying cutting parameters, providing critical insights for optimizing milling operations. The integration of uncertainty awareness in the predictive outputs is a unique feature of this research and enhances decision-making for smarter manufacturing. This proactive approach enhances operational efficiency and reduces overall production costs. Furthermore, the data-driven, AI-centric methodology developed in this study offers a transferable approach that can be adapted to other machining processes, advancing state-of-the-art tool wear prediction.

Разработка инновационной технологии закладки выработанного пространства на основе использования пастообразных закладочных смесей и оценка ее технико-экономической и экологической эффективности

The article discusses the results of developing an innovative technology for backfilling of the mined-out space using paste-like backfill mixtures with addition of concentration tailings. The relevance of the research is explained by the need to improve the efficiency and environmental friendliness of backfilling operations at mining enterprises. The objective of the work is to create and evaluate the technical, economic and environmental efficiency of a new backfilling technology. The research methodology included an analysis of the current state and bottlenecks of backfilling operations, development of formulations and studying properties of the paste-like mixtures with concentration tailings, carrying out pilot tests, feasibility study and an ecological justification of the proposed solutions. It has been established that addition of the concentration tailings to the paste-like mixtures allows to reduce cement consumption by 20-30% without deterioration of rheological and strength properties. Optimal compositions of the mixtures were developed as well as efficient technological schemes of their preparation and transportation. Pilot tests confirmed the possibility to utilize up to 50% of the current output of the concentration tailings in the backfill mixtures. Application of the proposed technology provides a 15-20% reduction in the cost of backfilling operations and a 40-50% reduction in the volume of waste dumping. The results obtained are important for improving the efficiency and environmental safety of mining operations and can be widely applied at companies that use mining systems with backfilling of the mined-out space.

FedGE: Break the Scalability Limitation of Graph Neural Network With Federated Graph Embedding

Neighborhood aggregation algorithms, represented as graph convolutional networks, have attained non-negligible success in numerous topological structure-based scenarios with the assumption that the topological structure of the given graph is pre-defined and relatively small. However, a real-world graph generally is a super large graph consisting of many small graphs that are interconnected and overlapping. Which makes graph embedding in real-life industries, by nature, fall into the federated learning scheme. While current graph-based algorithms are only able to capture the individual topology of each natural graph, learning the complete structural information of the merged large graph remains challenging due to the unsustainable computational cost of graph convolutional operations. We propose a tailored federated graph embedding framework to learn the intact structural information of the numerous inherently linked small-scale graphs and the embedding of each node. We leverage graphs with around two and a half million nodes to validate the effectiveness and the correctness of the proposed framework.

Community Water Fluoridation a Cost–Benefit–Risk Consideration

ABSTRACTFor over 70 years, the addition of fluoride to public water as community water fluoridation (CWF or fluoridation) with intent to prevent dental caries continues to be controversial, and risks are seldom included in monetary evaluations. Published operational costs and benefits of fluoridation are used, whereas published and clinical experience treating dental fluorosis are utilized to estimate treatment costs of patient‐perceived dental fluorosis and lost wages from lower IQ (intelligence quotient). Published estimated caries averted, less operational costs at $8 PPPY (per person per year) were used, compensation for functional and cosmetic dental fluorosis $126 PPPY, and lower earnings from presumed harm of developmental neurotoxicity estimated at $438 PPPY. Net loss from CWF is estimated at $556 PPPY, although some individuals will have significantly more or less loss. Previous economic evaluations of fluoridation have estimated caries averted and costs of operations; however, few evaluations include the costs of treating harm. Fluoridation is not cost‐effective if the cost of harm is included. Alternatives for the prevention of dental caries should be promoted, and the cessation of fluoridation is indicated.

Flight, aircraft, and crew integrated recovery policies for airlines - A deep reinforcement learning approach

Airline schedules are easily affected by disruptions, leading to flight delays or (and) cancellations, causing significant financial losses to airline companies and inconvenience for passengers. When making recovery decisions, airlines need to simultaneously consider various entities, including flights, aircraft, and crew. This paper examines the integrated recovery policies for airlines to help re-schedule flights, re-route aircraft, and reassign crew members. To realize quick responses upon the occurrence of disruptions, an attention-based end-to-end deep reinforcement learning approach is proposed to learn a parameterized stochastic policy for the integrated airline recovery problem. Numerical experiments based on randomly generated disruption instances demonstrate that the proposed method outperforms the existing approaches and is applicable in realistic situations. The key insights obtained from our analyses are summarized as follows: (1) traditionally, among all disruption sources, it is most challenging and time-consuming to determine the recovery policies in reaction to aircraft delays and airport closures. However, the new approach developed in this study overcomes this difficulty and can provide high-quality recovery policies for aircraft delays and airport closures quickly. Thus, our work is especially valuable for airports and regions that suffer from frequent flight delays and closures, and can significantly improve their operational efficiency and service quality; (2) when traditional approaches are applied, the adoption of the well-known schedule robustness enhancement strategy ‘crew follow aircraft’ generally leads to high operations costs. Differently, our proposed approach can apply this strategy without encountering a significant cost growth. Therefore, airlines can fully leverage this strategy to gain additional advantages; (3) our developed new approach demonstrates high generality to accommodate various disruptions, which can benefit airlines and airports in the highly-volatile environment with various unpredictable events.

Comparing the Growth Rates of Ipomoea aquatica Using Washed Rice Water From Different Rice Varieties

Numerous agricultural sectors rely on chemical fertiliser to accelerate crop development, as demand continues to expand each year. Synthetic fertilisers may leak into groundwater, increasing its toxicity and resulting in water contamination. Alternatives such as washed rice water may be utilised to mitigate the environmental impact of chemicals. This research aims to assess the effects of washed rice water on the growth rate of Ipomoea aquatica. Three types of rice water were used in this study: washed white rice water, washed brown rice water, and washed sticky rice water. Two of the rice water served as the control and each was treated with distilled water and NPK fertiliser. Significant values were determined using Analysis of Variance (ANOVA) and post-hoc tests for multiple comparisons. Resultantly, washed white rice water demonstrated the most significant effect in increasing the growth of the plant height, length of root, number of leaves, circumference of the stem and suitability of soil pH. Alternative methods such as washed white rice water could be employed to reduce the cost of agricultural operations to protect the environment from further degradation.

Manufacturing sulfated cellulose nanofibers using a unique combined DES-based pretreatment-functionalization protocol for metal ion decontamination through porous adsorbents

This study confirms the efficacy of a unique combined pretreatment-functionalization protocol based on the use of deep eutectic solvent (DES) to obtain sulfated lignocellulose and cellulose nanofibers (SLNF or SNF) hydrogels, which have been successfully shaped as sponge-based adsorbents and fruitfully assessed for the removal of heavy metals from water. A comprehensive characterization study was conducted, demonstrating an excellent degree of sulfation (0.62) in DES-treated wheat straw-derived nanofibers. The direct use of SLNF or SNF hydrogels and their application as porous sponges exhibited highly favorable characteristics for successful ion decontamination. Cu2+ removal was up to 70 % higher using DES-sulfated nanocellulose hydrogels compared to conventional treated-nanocellulose. Various isotherm models were studied, and the analysis of the kinetic and diffusion studies confirmed the influence of the sample format in the removal behavior. SLNF and SNF-sponges proved to be the most effective in adsorption, achieving Cu2+ removal rates of up to 60 %. More profitable decontamination processes with lower run times could be guessed when the application of nanocellulose is led through the processing of advanced formats. The easy handling of sponges would avoid the extra costs of the downstream unit operations which are sometimes needed to separate the hydrogel of the decontaminated media.

Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment

Mining safety heavily depends on ventilation, which constitutes a significant portion of the energy costs in operations. Optimizing mine ventilation systems (MVSO) is crucial for minimizing this energy expenditure. However, current algorithms encounter challenges when applied to large-scale mines, primarily due to the complexity of variables and limited attention to optimizing main fans. This study introduces a theoretical knowledge enhanced genetic algorithm for MVSO, incorporating main fan adjustments. The algorithm models changes in the main fan’s operational status and integrates ventilation network equivalent simplification (VNES) and the minimum spanning tree (MST) to reduce the number of variables in the mine ventilation network. Additionally, leveraging mine ventilation sensitivity theory (MVST) enhances the quality of the initial algorithmic population. A simple case and two engineering cases collectively validated that the algorithm consistently provides effective and reliable optimization solutions for mine ventilation systems across varying scales. Specifically, the algorithm reduced energy consumption from 326.94 to 186.99 kW, 433.14 to 239.48 kW, and 520.53 to 324.90 kW across three different scales of mine ventilation systems. Comparative analysis with four other algorithms shows that, although this algorithm has a longer runtime due to the need to identify the minimum spanning tree during iterations, its ability to reduce problem dimensionality and improve population quality results in more stable and superior convergence performance, especially for large-scale mine ventilation systems.Graphical abstract

Performance analysis of the Village Credit Institution (LPD) of Tanjung Bungkak Traditional Village using the balanced scorecard approach

The balanced scorecard is a tool for measuring company performance from financial and non-financial aspects through four perspectives including the financial perspective, customer perspective, internal business process perspective, and learning and growth perspective. This research uses a quantitative descriptive approach carried out at the Tanjung Bungkak Traditional Village LPD located in Denpasar with the aim of knowing the performance of the Tanjung Bungkak Traditional Village LPD seen from the four perspectives in the balanced scorecard. Data collection techniques used include documentation, observation, interviews and surveys. The results of the analysis show that the performance of the Tanjung Bungkak Traditional Village LPD, when viewed from a financial perspective, is not good with the calculation of Cash Ratio (CR) and Loan to Deposit Ratio (LDR) categorized as quite good, Debt to Equity Ratio (DER) categorized as good, but Operations Cost to Operating Income (BOPO), Non-Performing Loans (NPL), Return on Assets (ROA) and Return on Equity (ROE) are categorized as poor. From a customer perspective, LPD's performance is assessed as good from the results of customer retention and customer satisfaction calculations with a percentage of positive statements reaching 93%. From an internal business process perspective, LPD performance is in fairly good criteria from the results of calculations of the innovation process and operational processes using service cycle effectiveness. The learning and growth perspective shows good LPD performance from calculating employee retention, employee productivity and employee satisfaction with the percentage of positive statements reaching 83%.

Оцінка можливості виявлення тріщин під час контролю ЛЮМ1-ОВ на лопатках турбін після нанесення комплексних жаростійких покриттів

Currently, gas turbine engines (GTD) are being improved and made more powerful by increasing the temperature of the gases in front of the turbine and increasing the air compression ratio in the compressor. In the aviation industry, nickel heat-resistant alloys such as ЖС6У-ВІ, ЖС26-ВІ, ЖС32-ВІ, etc. are used to manufacture GTD blades. Despite their high properties in terms of corrosion resistance and heat resistance, the service life of GTD blades and their protection against oxidation and corrosion damage is ensured by applying coatings using various technologies both to the flowing surface of the blade blades (heat-resistant and heat-protective) and to the inner surfaces of the cooled channels (diffusion coatings). Failure of turbine blades is one of the most severe types of damage to turbine blades, so at different stages of serial production, including after applying heat-resistant coatings, LUM1-OV inspection of blades is performed to detect cracks that can form during processing and lead to their failure during engine operation. The paper evaluates the possibility of detecting cracks on GTD turbine blades with complex heat-resistant coatings GCP+SDP-2. The study was carried out on samples of working blades in the form of an experiment. First, fatigue tests of the samples were carried out on a test bench until cracks appeared. Then, a complex heat-resistant coating of GCP (CrAl gas circulation coating) + SDP-2 (heat-resistant alloy of the Ni-Cr-Al-Y system) was applied to the blade profile in stages according to the serial technology. After each coating was applied, the blades were inspected by the LUM1-OV method to determine the possibility of crack detection. The results showed that immediately after the application of the complex heat-resistant coating, cracks on the blades were not detected, because they spread only in the GCP layer and partially pass into the SDP-2 layer without reaching the surface. The results obtained can be the basis for removing the luminescent inspection operation from the technological process, immediately after applying complex heat-resistant coatings. Since this does not allow detecting cracks in the blade material and can lead to unnecessary costs for additional technological operations.

Euroization of the Economy and the Negative Impact of the Strengthening of the Local Currency on Exports and Other Economic Indicators in Albania

The Albanian economy is a small and open economy. As such, it is quickly influenced by the turbulence of the global, European, and regional economies. Economic theory emphasizes the link between the exchange rate, exports, the consumer price index, and the balance of foreign trade. The purpose of this article is to study the impact of the exchange rate, primarily at the export level, while also considering other economic factors within the framework of the overvaluation of the Albanian currency (ALL) against the currencies of partner countries, mainly the Euro. The overvaluation of the Albanian currency has led to discouraging exports and increasing imports, influencing the rise in the Consumer Price Index, meaning higher consumer prices. This is due to the fact that the Albanian economy is mainly dependent on imports. The empirical and econometric models used, along with the factors included in this study according to the hypotheses raised, are related to assessing the mutual relationships of the exchange rate with other economic factors for the time period 2018-2023.The results imply and depict the integrated relationship between the exchange rate and exports and other economic factors, as well as supporting the fact that the real exchange rate (RER) has deviated from its equilibrium level. The conclusions in this study confirm that an overvalued domestic currency has increased costs for operations conducted in the local currency ALL, reducing income for operations conducted in Euro and other currencies. This overvaluation of the domestic currency has resulted in losses for exporting companies, impacting the trade balance. Based on the findings in this study, it is crucial for financial institutions and policymakers to be consistent in monitoring the exchange rate, especially EUR/ALL, to improve the long-term relationships between the exchange rate, exports, and other economic factors.

Integrating Activity-based Costing with Continuous Enhancement: A Strategic Approach to Operational Efficiency and Cost Management

The main objective of this research is to show how integrating activity-based costing (ABC) with continuous improvement processes can enhance cost management and operational efficiency. The development of automated firms and global firms has necessitated a shift in principles and measurement accuracy. The principles and concepts have been modified based on economic theory, moving from stable continuous improvement to continuous enhancement, or Kaizen enhancement. The accuracy of the proposed costing system has improved by using modified activity-based costing. The current research has focused on multi-variation in explanatory variables, including the costing system and the degree of enhancement. The suggested costing system should include the modified version of activity-based costing and the new form of continuous enhancement system. The concept of an intelligent supply chain has led to the idea of a cooperative game between all parties in the firm/chain. Intelligent automated systems are closed-loop systems where all members are partners, aiming to maximize global benefits. Resilient considerations indicate different attempts to measure and estimate the multi-effects of externalities and other external effects. This work has closed-to-open excess quantitative models, achieving the cooperative state between different partners. Findings show that ABC helps manufacturing and service sectors identify non-value-adding activities, optimize resource allocation, and reduce costs by providing detailed insights into the true cost of operations. Its integration with continuous improvement strategies amplifies these benefits, enabling businesses to streamline processes, improve efficiency, and enhance profitability. ABC is a valuable tool for identifying non-value-adding activities and optimizing resource allocation. By providing a more accurate understanding of how resources are consumed, ABC can help organizations improve their efficiency, reduce costs, and enhance customer satisfaction.

Ai-driven Predictive Analytics, Healthcare Outcomes, Cost Reduction, Machine Learning, Patient Monitoring

Integrated with predictive analytics and machine learning, AI has exceeded the traditional approaches of health care contexts by focusing on patient outcomes and costs. This paper aims to discuss the adoption of integrated AI in healthcare systems, categorizing these by how AI predictive models help improve patient health by proactively estimating the course of their illness and its potential impact, prioritizing patient readmissions, and developing effective individualized treatment strategies. The research also identifies more important savings realised through avoiding redundant tests, better utilisation of resources, and shorter hospitalisations. In the current study, the authors present concrete findings for AI-driven Predictive Analytics based on realistic scenarios and quantitative data of health care systems. It also points to the fact that healthcare organisations adopting the use of AI technology have gained an objective that reduced their operations costs by 25% and improved patient outcomes whereby the readmission rates were reduced by between 15% and 20%. Furthermore, there is an evaluation of the ethical considerations of applying AI in healthcare, especially on the subject of patient’s information security. To the best of our knowledge, this study is the first to systematically review AI applications in healthcare and provide detailed suggestions for better understanding the general impact of AI in healthcare to enhance patient outcomes and manage costs. With advancement in artificial intelligence technology, there is a growing importance of how the technology can transform the health care industry.

An environmental cost-benefit analysis of organic and non-organic sheep farming in Iceland

This paper presents an environmental cost-benefit analysis (CBA) comparing organic and non-organic sheep farming in Iceland over three decades. Environmental life cycle costing (ELCC) is the basis for the aggregate cost framework, encompassing costs for investment, operations, maintenance, and environmental externalities. Greenhouse gas emissions are included as an external cost as they relate to the monetization of damages to society. Net present value (NPV) and benefit-cost ratio (BCR) indicators reveal both systems as unprofitable over thirty years with and without subsidies. Depending on the discount rate, the organic farm's BCR ranges from 0.26 to 0.38, with a NPV between (−2.4) and (−1.1) million euros. The non-organic farm's BCR ranges from 0.12 to 0.19, with NPV between (−8.6) and (−3.7) million euros. Greenhouse gas emission costs constitute 28 % and 33 % of total life cycle costs for organic and non-organic farms. Across three discount rates, the LCC and ELCC per kilogram of organic lamb ranged between €13.78–25.27 and €18.23–37.07, respectively. The LCC and ELCC per kilogram of non-organic lamb ranged between €9.75–18.05 and €13.80–28.82, respectively. Due to a shortened value chain, organic sheep farming demonstrated revenue advantages as compared to non-organic. The external cost of greenhouse gas emissions from sheep farming forms a significant portion of lifetime costs suggesting that policymakers could incentivize organic practices aimed at reducing greenhouse gas emissions. Finally, this study provides a deeper understanding of the long-term economic prospectivity of sheep farming under different production approaches.

Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST

The Fourth Industrial Revolution, also known as Industry 4.0, which is the intensified digitalization and automation in industry, embraces cyber–physical systems, the Internet of Things (IoT), and artificial intelligence, among others. This study utilizes Fuzzy Integration–Rough Set Theory (FI-RST) analysis to quantify the impacts of the imperative Industry 4.0 technologies for manufacturing firms located in Fujian Province, China, namely, Manufacturing Execution Systems (MES), the Industrial Internet of Things (IIoT), and Additive Manufacturing (AM), on the sustainable development performance of firms. The findings of the study indicate that these technologies greatly improve the effectiveness of the utilization of resources, reduce the costs of operations, and reduce the impact on the environment. In addition, they have a favorable influence on social considerations, such as preserving the well-being of employees and the outcome of training programs. This research work has convincingly provided an underlying strategic adoption of these technologies for sustainability production by raising important insights that could be valuable for industry managers and policymakers, especially those seeking sustainability at the global level.

Cybersecurity in Prognostics and Health Management

PHM continues to show its value by improving operational efficiencies, increasing safety, reducing downtime, and decreasing cost of operations. PHM technologies are therefore not only being deployed as retrofit solutions but are being integrated into new systems as standard practice. Deployment covers areas such as medical equipment, nuclear power plants, aeronautics applications, oil and gas, mining, and many others. As the impact of PHM increases, it is imperative to also consider the potential vulnerabilities that are being exposed. Hackers have famously used Supervisory Control and Data Acquisition (SCADA) and Programmable Logic Controller (PLC) systems to sabotage industrial facilities. As such, it is important to understand the exposure to malfeasance to ensure that PHM does not end up being the enabling mechanism for unauthorized access to the system it is meant to keep in running order. It is also important to understand the measures that need to be taken to avoid or respond to an attack. These range from extensive penetration testing to conducting extensive counter-social engineering training, setting up a PHM-specific CERT plan and team in place. This paper discusses various threats that are emerging and that may have to be considered when designing a PHM solution. Additionally, the NIST cybersecurity framework is discussed in the context of PHM. Finally, this paper looks at the diagnostic capabilities of PHM systems to detect cyber security attacks and to contain these threats.

Regional electricity cooperation model for cost-effective electricity management with an emphasis on economic efficiency

Effectively reducing the total electricity cost while ensuring its overall efficiency is vital for the sustainable development of power systems. Herein, we develop a regional cooperation optimization model to enhance the existing power cost model. This is achieved by incorporating power trading scenarios and pollution considerations, leveraging the distinct characteristics of power generation costs from various sources. The developed regional cooperation optimization model simultaneously accounts for production efficiency and overall power costs, ensuring equitable distribution and compensation of benefits among cooperating entities. In addition, the key findings indicate that the interregional cooperation optimization model significantly reduces the total cost of electricity operations in China. Specifically, during the 11th five-year plan period, the total electricity cost in China exhibited a noteworthy decline of USD 50,876 million owing to multi-objective cooperative optimization, constituting 25.99% of the total electricity cost compared with the preoptimization period. This study identifies the eastern and western regions as principal contributors to the cost savings achieved by the interregional cooperative optimization model. Simultaneously, recognizing their significant contributions, it is recommended that the eastern and western regions provide additional cost compensation to other regions to ensure the long-term viability of the cooperative alliance.

МҰНАЙ ЖӘНЕ ГАЗ КЕНОРЫНДАРЫНЫҢ ӨНІМДЕРІН ӨНДІРУДЕ ҚАЗІРГІ ЗАМАНҒЫ ЦИФРЛЫ ЖӘНЕ ЗИЯТКЕРЛІ ТЕХНОЛОГИЯЛАРДЫ ҚОЛДАНУ АСПЕКТІЛЕРІ

Most oil fields cover thousands of square kilometers, and some have reserves in difficult local conditions (heat, permafrost, desert, deep water) or difficult to recover. Due to these factors and a number of other problems related to technological safety, there are difficulties in the development of oil fields (with a steady increase in demand for oil and gas). Therefore, oil companies still need a highly reliable digital infrastructure that provides high productivity and security and manages many of the devices used in the oil and gas business throughout the field. Therefore, in recent years, oil companies around the world are increasingly managing their oil fields using "smart" technologies. They have proven to be a powerful tool for ensuring the safety of oil fields and increasing labor productivity, as well as economic efficiency. The technologies used include the improvement of all stages of the work process through the rapid acquisition of production data associated with advanced analytics, which contributes to a more complete and efficient development of the oil field. With experience in the implementation of "intelligent" technologies, it already shows high efficiency and very reliable results (both in terms of the oil production process itself and in terms of reducing the cost of individual operations). The use of modern digital and intelligent technologies in the production process in the oil and gas sector plays an important role in order to increase efficiency, ensure safety and reduce environmental impact. Application of digital technologies-data collection and analysis: IoT sensors (Internet of Things) allow you to collect data from deposits in real time and send it to analytical platforms. Field modeling: Using digital Twin technology, virtual field models are created that allow you to predict and optimize production processes. Automation: The automated systems and robotics used help to increase productivity in the management of the oil and gas production process.

Tachis: Higher-Order Separation Logic with Credits for Expected Costs

We present Tachis, a higher-order separation logic to reason about the expected cost of probabilistic programs. Inspired by the uses of time credits for reasoning about the running time of deterministic programs, we introduce a novel notion of probabilistic cost credit. Probabilistic cost credits are a separation logic resource that can be used to pay for the cost of operations in programs, and that can be distributed across all possible branches of sampling instructions according to their weight, thus enabling us to reason about expected cost. The representation of cost credits as separation logic resources gives Tachis a great deal of flexibility and expressivity. In particular, it permits reasoning about amortized expected cost by storing excess credits as potential into data structures to pay for future operations. Tachis further supports a range of cost models, including running time and entropy usage. We showcase the versatility of this approach by applying our techniques to prove upper bounds on the expected cost of a variety of probabilistic algorithms and data structures, including randomized quicksort, hash tables, and meldable heaps. All of our results have been mechanized using Coq, Iris, and the Coquelicot real analysis library.