1,720,971 research outputs found
How to evaluate the investment and management economic sustainability for different photovoltaic plant installations
No full textCome valutare la sostenibilità economica degli investimenti in impianti fotovoltaici e della loro gestion
Attraction Force Optimization (AFO): A deterministic nature-inspired heuristic for solving optimization problems in stochastic simulation
No full textThe paper presents a new optimization heuristic called AFO-Attraction Force Optimization, able to maximize discontinuous, non-differentiable and highly non-linear functions in discrete simulation problems. The algorithm was developed specifically to overcome the limitations of traditional search algorithms in optimization problems performed on discrete-event simulation models used, for example, to study industrial systems and processes. Such applications are characterized by three particular aspects: the response surfaces of the objective function is not known to the experimenter, a few number of independent variables are involved, very high computational time for each single simulation experiment. In this context it is therefore essential to use an optimization algorithm that on one hand tries to explore as effectively as possible the entire domain of investigation but, in the same time, does not require an excessive number of experiments. The article, after a quick overview of the most known optimization techniques, explains the properties of AFO, its strengths and limitations compared to other search algorithms. The operating principle of the heuristic, inspired by the laws of attraction occurring in nature, is discussed in detail in the case of 1, 2 and N-dimensional functions from a theoretical and applicative point of view. The algorithm was then validated using the most common 2-dimensional and N-dimensional benchmark functions. The results are absolutely positive if compared, for the same initial conditions, with the traditional methods up to 10-dimensional vector spaces. A higher number of independent variables is generally not of interest for discrete simulation optimization problems in industrial applications (our research field)
Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing
No full textNon-core services efficiency improvement: How to find resources in the healthcare system
No full textIn countries where the health service is run by national and regional governments, costs have risen dramatically since the early 2000s. The consequently smaller contributions granted to hospital facilities have forced managers to implement cost-reduction strategies. The Authors' purpose is to avoid strategies which interfere with care processes such as linear cuts (fewer services to patients) and standard cost (a policy of equalizing the cost of purchasing goods and services). A health care system is strongly similar, in an operative way, to a production system. For this reason, the Authors decide to face the efficiency improvement of this kind of system with the same approach they generally use for production systems. Through the construction of simulation models (Discrete Event Simulation, System Dynamics) are, therefore, obtained quantitative data on the basis of which, in agreement with the Management, the corresponding improvement actions are made. The proposed approach allows Managers, at zero cost and without interfering with care processes, the recovery of significant resources as demonstrated in the test case application. Through the System efficiency improvement proposed by the Authors it is possible to maintain a high level of care even in the presence of budget reductions. Strong savings for each system where the methodology is applied and for the whole Italian system
Stochastic techno-economic assessment based on Monte Carlo simulation and the Response Surface Methodology: The case of an innovative linear Fresnel CSP (concentrated solar power) system
No full textCombining technological solutions with investment profitability is a critical aspect in designing both traditional and innovative renewable power plants. Often, the introduction of new advanced-design solutions, although technically interesting, does not generate adequate revenue to justify their utilization. In this study, an innovative methodology is developed that aims to satisfy both targets. On the one hand, considering all of the feasible plant configurations, it allows the analysis of the investment in a stochastic regime using the Monte Carlo method. On the other hand, the impact of every technical solution on the economic performance indicators can be measured by using regression meta-models built according to the theory of Response Surface Methodology. This approach enables the design of a plant configuration that generates the best economic return over the entire life cycle of the plant. This paper illustrates an application of the proposed methodology to the evaluation of design solutions using an innovative linear Fresnel Concentrated Solar Power system
A design of experiments/response surface methodology approach to study the economic sustainability of a 1 MWe photovoltaic plant
No full textTo obtain a rigorous investment analysis for photovoltaic power generation plants, a dynamic Business Plan has been studied. It allows us to determine, in real time, the investment parameters (Net Present Value, Internal Rate of Return, Return on Investment, etc) starting from the technical, economical, financial and positional data. The Business Plan provides, as output, the exact values of the parameters (dependent variables or objective functions) basing on the exact values of the input variables (independent variables). By suitably varying the independent variables it is possible to obtain a sensitivity analysis on the dependent variables due to which different evaluations about the investment in multiple pre-set scenarios can be obtained. On the other hand, by using the Business Plan as data creator and then applying the Response Surface Methodology techniques, it has been possible to obtain mathematical functions (regression meta-models) which are able to explain the link among dependent and independent variables in the definition range. This approach allows to generate a sort of k dimensional state equation of order n for the different investigated economic parameters. These equations provide, in the chosen dimensional space (two or three-dimensional), a view of the dependent variable behaviour (e.g. Net Present Value, Internal Rate of Return) while changing one or two independent variables (e.g. government incentives, discount rate). The descriptive ability of this approach has a higher level in terms of quality and reliability than a traditional sensitivity analysis. In this paper, a case study about a 1 MWe photovoltaic plant located in Liguria (North-Italy) and a comparison with a similar plant located in Sicily (South-Italy) are also presented
Monte carlo method for pricing complex financial derivatives: An innovative approach to the control of convergence
No full textThe global financial crisis of 2007-2008 has highlighted the importance of a correct pricing of the so-called financial derivatives. Analyzing the methodology of pricing of non-listed derivatives by using the Monte Carlo method, the Authors have realized that the determination of the sample size is not managed properly. This is because the research offices of banks rely on, as suggested by the literature of the field and technical manuals for practitioners, a standard number of simulation runs, by rules of thumb, between 1,000 and 10,000. The consequence is that financial institutions lead to financial statements fair values with no knowledge of its uctuation band and the robustness of the result. Conscious of this practice, the Authors, dealing from a long time to the topic of output reliability in applications of discrete event simulation and Monte Carlo simulation, address the problem through the use of a methodology based on the control of Mean Pure Square Error (MSPE), already successfully tested in other contexts. Thanks to the proposed approach, applied for pricing complex derivatives, it is possible to determine the size of the experimental sample in order to ensure a pre-assigned degree of reliability of the output results
Optimal sizing of a pediatric hospital patients reception desk using discrete event simulation
No full textThe study presented derives from the request of the management of an Italian important pediatric hospital to reorganize the patient acceptance service. The authors to address the analysis had built a stochastic discrete event simulation model through which it was possible to study the behavior of the system under consideration in the different days of the week and at different times of the same day with the aim of minimizing the number of employees to the service, ensuring, in parallel, a short time of waiting in the queue for the users. The complexity of the problem lay in the strong variability of the service request in different time slots and in the significant diversification of activities carried out by the acceptance desk. The case study showed that by using simulation techniques and defining suitable Key Performance Indicators health institutions can save several hundreds of thousands of Euros a year, even by tackling non-core health services such the one studied by the authors. Considering that public health in countries like Italy is undergoing continuous spending cuts, the costs saved through interventions as the one shown in the paper become crucial to safeguard the resources to be allocated to patient care activities
Improving the Efficiency of a Hospital ED According to Lean Management Principles Through System Dynamics and Discrete Event Simulation Combined with Quantitative Methods
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