This book is a comprehensive introduction to the field of discrete event systems, offering a breadth of coverage that makes the material accessible to readers of varied backgrounds. The book ...emphasizes a unified modeling framework that transcends specific application areas, linking the following topics in a coherent manner: language and automata theory, supervisory control, Petri net theory, Markov chains and queuing theory, amd more.
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There is a large variability in profitability and productivity between farms operating with automatic milking systems (AMS). The objectives of this study were to identify the physical factors ...associated with profitability and productivity of pasture-based AMS and quantify how changes in these factors would affect farm productivity. We utilised two different datasets collected between 2015 and 2019 with information from commercial pasture-based AMS farms. One contained annual physical and economic data from 14 AMS farms located in the main Australian dairy regions; the other contained monthly, detailed robot-system performance data from 23 AMS farms located across Australia, Ireland, New Zealand, and Chile. We used linear mixed models to identify the physical factors associated with different profitability (Model 1) and partial productivity measures (Model 2). Additionally, we conducted a Monte Carlo simulation to evaluate how changes in the physical factors would affect productivity. Our results from Model 1 showed that the two main factors associated with profitability in pasture-based AMS were milk harvested/robot (MH; kg milk/robot per day) and total labour on-farm (full-time equivalent). On average, Model 1 explained 69% of the variance in profitability. In turn, Model 2 showed that the main factors associated with MH were cows/robot, milk flow, milking frequency, milking time, and days in milk. Model 2 explained 90% of the variance in MH. The Monte Carlo simulation showed that if pasture-based AMS farms manage to increase the number of cows/robot from 54 (current average) to ∼ 70 (the average of the 25% highest performing farms), the probability of achieving high MH, and therefore profitability, would increase from 23% to 63%. This could make AMS more attractive for pasture-based systems and increase the rate of adoption of the technology.
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The Power-to-Gas process chain could play a significant role in the future energy system. Renewable electric energy (wind or solar) can be transformed into storable methane via electrolysis and ...subsequent methanation through Sabatier reaction. The aim of this research is a design and techno-economic analysis of a Power-to-Gas process. The plant is integrated with an anaerobic digester to have the carbon dioxide, by the up-grading of the biogas, for the Sabatier reaction. Electrical and thermal power are produced by a cogeneration system. A business to business analysis, not present in other literature works, is carried out for the process: it is then the main innovation of this research. Results show that to produce 1000 kWel, 10 kmol/h of methane are needed: in the Sabatier reaction, the flow rate of carbon dioxide and hydrogen are respectively equal to 4.6 kmol/h and 22 kmol/h. The power of the eolic park is 1980 kWel. In the business to business analysis, a swot analysis (strengths, weaknesses, opportunities and threats) is developed. Success critical factors and risks are found in addition to several strategies that must to be involved to be successful in the project. The success of the process is the use of 100% of renewable energy to produce a need for the society, enhancing a waste material as the carbon dioxide to produce methane. The techno-economic feasibility shows that the plant is economic feasible with VAN, PBP and LCOE equal to 8 million €,4 years and 260 €/MWh respectively. Economic incentives are also obtained.
The future construction of the plant in Germany will verify the obtained results and future researches should realize the most sustainable process with the lower environmental impact.
•Design of power to gas process integrated with an anaerobic digestion plant.•Simulation of Sabatier reactor for the power to gas process.•Technical and economic analysis of the power to gas process.•Business to business analysis for a power to gas process.
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Decision making in the renewable energy industry involves an increasingly complex constellation of factors and a growing range of input. This volume shows how multi-criteria techniques provide ...decision makers with a valuable and inclusive methodology.
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Reactive Search integrates sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. By automatically adjusting the working parameters, a reactive ...search self-tunes and adapts, effectively learning by doing until a solution is found. Intelligent Optimization, a superset of Reactive Search, concerns online and off-line schemes based on the use of memory, adaptation, incremental development of models, experimental algorithms applied to optimization, intelligent tuning and design of heuristics. Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics. Individual chapters cover reacting on the neighborhood, reacting on the annealing schedule, reactive prohibitions, model-based search, reacting on the objective function, relationships between reactive search and reinforcement learning, and much more. Each chapter is structured to show basic issues and algorithms, the parameters critical for the success of the different methods discussed, and opportunities and schemes for the automated tuning of these parameters. Anyone working in decision making in business, engineering, economics or science will find a wealth of information here.
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One of the problems of business analysis is obtaining and processing an ever-increasing volume of economic, financial, organizational, political and legal data. Multimodal business analytics is a new ...methodology combining the methods of classical business analysis with big data technologies, intelligent business analytics, multimodal data fusion, artificial neural networks and deep machine learning. The purpose of the study is to determine the conceptual foundations of the phenomenon of multimodal business analytics and substantiate the prospects for its use in economic science and practice. Methodologically, the study rests on the systems approach, i.e., multimodal business analytics is examined as a unique integrated phenomenon comprised of several interrelated components. The evidence base covers research studies of 2000–2022 on multimodal business analytics from Scopus and the Russian online database eLibrary.ru. Empirical methods were used to collect and evaluate the dynamics of the number of relevant publications and their segmentation by subject areas. We have proposed own thesaurus and ontology of the key terms that make up the phenomenon of multimodal business analytics. It is shown that the use of the concept allows expanding the range of data, exposing hidden interrelations of organizational and economic phenomena and synthesizing fundamentally new information needed for effective decision-making in business.
Spreadsheets help businesses run effectively and efficiently. However, many spreadsheets contain errors. Research provides taxonomies and guidance on preventing spreadsheet errors; this information ...often is not incorporated into training. Training students to use Excel functions and good spreadsheet practices is critical. This instructional experiment aims to educate students on techniques that reduce the chance of errors, provide an Excel project to demonstrate their skills and implement a quiz tool that identifies students’ mistakes as they complete the project. The goal is to guide students to learn from their errors, improving confidence in their Excel skills.
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Purpose To redesign the spare parts (MRO) inventory management at Company XYZ's warehouse, considering the conditions after the COVID-19 pandemic. Design/methodology/approach To address this research ...project, the authors integrated three methodologies: action research, Lean Six Sigma (DMAIC) and Cross Industry Standard Process for Data Mining. These methodologies integrated the Lean Six Sigma (LSS) 4.0 framework applied in this project. Findings The spare parts inventory value was reduced by 15%, and inventory turnover increased by 120% without negatively impacting the internal service level. Practical implications Practitioners leading or participating in continuous improvement projects (CIPs) should consider data quality (data available and data trustworthiness), problem-solving approach and target area involvement to achieve CIP goals. Otherwise, the LSS 4.0 could fail or extend its duration by several weeks or months. Originality/value This project shows the importance of controlling a target area before deciding to conduct a LSS 4.0 project. To address this problem, the LSS 4.0 team implemented 5S during the measure phase of the DMAIC. Also, this project offers significant practitioner and theoretical contributions to the body of knowledge about LSS 4.0.
The evolution of the bitcoin economy Tasca, Paolo; Hayes, Adam; Liu, Shaowen
The journal of risk finance,
01/2018, Volume:
19, Issue:
2
Journal Article
Peer reviewed
Purpose This paper aims to gather together the minimum units of users’ identity in the Bitcoin network (i.e. the individual Bitcoin addresses) and group them into representations of business ...entities, what we call “super clusters”. While these clusters can remain largely anonymous, the authors are able to ascribe many of them to particular business categories by analyzing some of their specific transaction patterns (TPs), as observed during the period from 2009 to 2015. The authors are then able to extract and create a map of the network of payment relationships among them, and analyze transaction behavior found in each business category. They conclude by identifying three marked regimes that have evolved as the Bitcoin economy has grown and matured: from an early prototype stage; to a second growth stage populated in large part with “sin” enterprise (i.e. gambling, black markets); to a third stage marked by a sharp progression away from “sin” and toward legitimate enterprises. Design/methodology/approach Data mining. Findings Four primary business categories are identified in the Bitcoin economy: miners, gambling services, black markets and exchanges. Common patterns of transaction behavior between the business categories and their users are a “one-day” holding period for bitcoin transactions is somewhat typical. That is, a one-day effect where traders, gamblers, black market participants and miners tend to cash out on a daily basis. There seems to be a strong preference to do business within the bitcoin economy in round lot amounts, whether it is more typical of traders exchanging for fiat money, gamblers placing bets or black market goods being bought and sold. Distinct patterns of transaction behavior among the business categories and their users are flows between traders and exchanges average just around 20 BTC, and traders buy or sell on average every 11 days. Meanwhile, gamblers wager just 0.5 BTC on average, but re-bet often within the same day. Three marked regimes have evolved, as the Bitcoin economy has grown and matured: from an early prototype stage, to a second growth stage populated in large part with “sin” enterprises (i.e. gambling, black markets), to a third stage marked by a sharp progression away from “sin” and toward legitimate enterprises. This evolution of the Bitcoin economy suggests a trend toward legitimate commerce. Originality/value The authors propose a new theoretical framework that allows investigating and exploring the network of payment relationships in the Bitcoin economy. This study starts by gathering together the minimum units of Bitcoin identities (the individual addresses), and it goes forward in grouping them into approximations of business entities, what is called “super clusters”, by using tested techniques from the literature. A super cluster can be thought of as an approximation of a business entity in that it describes a number of individual addresses that are owned or controlled collectively by the same beneficial owner for some special economic purposes. The majority of these important clusters are initially unknown and uncategorized. The novelty of this study is given by the pure user group and the TP analyses, by means of which the authors are able to ascribe the super clusters into specific business categories and outline a map of the network of payment relationships among them.
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