چالش های مدیریت مالی برای بیمارستان عمومی روانپزشکی 2001
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|8466||2001||6 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : General Hospital Psychiatry, Volume 23, Issue 2, March–April 2001, Pages 67–72
Psychiatry programs are facing significant business and financial challenges. This paper provides an overview of these management challenges in five areas: departmental, hospital, payment system, general finance, and policy. Psychiatric leaders will require skills in a variety of business management areas to ensure their program success. Many programs will need to develop new compensation models with more of an emphasis on revenue collection and overhead management. Programs which cannot master these areas are likely to go out of business. For academic programs, incentive systems must address not only clinical productivity, but academic and teaching output as well. General hospital programs will need to develop increased sophistication in differential cost accounting in order to be able to advocate for their patients and program in the current management climate. Clinical leaders will need the skills (ranging from actuarial to negotiations) to be at the table with contract development, since those decisions are inseparable from clinical care issues. Strategic planning needs to consider the value of improving integration with primary care, along with the ability to understand the advantages and disadvantages of risk-sharing models. Psychiatry leaders need to define and develop useful reports shared with clinical division leadership to track progress and identify problems and opportunities. Leaders should be responsible for a strategy for developing appropriate information system architecture and infrastructure. Finally, it is hoped that some leaders will emerge who can further our needs to address inequities in mental health fee schedules and parity issues which affect our program viability.
The conversion of fossil fuel into electricity is an inefficient process. Even the most modern combined cycle plants are between 50% and 60% efficient. Most of the energy wasted in the conversion process is heat. The principle of combined heat and power, known as cogeneration, is to recover and make beneficial use of this heat and as a result the overall efficiency of the conversion process is increased. Combined heat and power generation has higher energy efficiency and less green house gas emission as compared with the other forms of energy supply. Recently, cogeneration units have been extensively used in utility industry. The heat production capacity of most cogeneration units depends on the power generation and vice versa. The mutual dependencies of heat and power generation introduce a complication in the integration of cogeneration units into the power economic dispatch. Nonlinear optimization methods, such as dual and quadratic programming , and gradient descent approaches, such as Lagrangian relaxation , have been applied for solving combined heat and power economic dispatch (CHPED). However, these methods cannot handle nonconvex fuel cost function of the generating units. The advent of stochastic search algorithms has provided alternative approaches for solving CHPED problem. Improved ant colony search algorithm , evolutionary programming  genetic algorithm , harmonic search algorithm  and multi-objective particle swarm optimization  have been successfully applied to solve CHPED problem. In  stochastic PSO-based method is used for solving CHPED problem including wind power and pollutant emissions constraints. In  artificial immune system has been used for solving CHPED problem. Chen et al.  have solved CHPED problem by using a novel approach based on the direct search method. Sashirekha et al.  have presented Lagrangian relaxation with surrogate subgradient multiplier updates to solve CHPED problem. This paper incorporates the combined heat and power units into the economic emission dispatch problem which plays a vital role in the power system dispatch. The objective of the combined heat and power economic emission dispatch (CHPEED) is to find the optimal point of power and heat generation with optimum fuel cost and emission level simultaneously such that both heat and power demands and other constraints are met while the combined heat and power units are operated in a bounded heat versus power plane. Over the past few years, several researches have been made on the development of multi-objective evolutionary search strategies. Strength Pareto evolutionary algorithm (SPEA) , nondominating sorting genetic algorithm II (NSGA II) , multi-objective evolutionary algorithm (MOEA) , multi-objective particle swarm optimization  and , fuzzy clustering-based particle swarm optimization (FCPSO) , etc., constitute the pioneering multi-objective approaches that have been applied to solve the economic environmental dispatch (EED) problem. These methods are population-based techniques and multiple pareto-optimal solutions can be found in one single run. This paper proposes nondominating sorting genetic algorithm II (NSGA II) for solving the combined heat and power economic emission dispatch (CHPEED) problem. Here, transmission loss is accounted for through the use of loss coefficients. This problem is formulated as a nonlinear constrained multi-objective optimization problem. Due to difficulties of binary representation when dealing with continuous search space with large dimensions, the proposed approach has been implemented by using real-coded genetic algorithm (RCGA)  and . In order to show the validity of the proposed approach the developed algorithm is illustrated on two test systems. Results obtained from the proposed approach have been compared with those obtained from strength pareto evolutionary algorithm 2 (SPEA 2).
نتیجه گیری انگلیسی
This paper has presented nondominated sorting genetic algorithm-II for solving combined heat and power economic emission dispatch problem. The problem has been formulated as multi-objective optimization problem with competing production cost and emission objectives. Results obtained from the proposed approach have been compared with those obtained from strength pareto evolutionary algorithm 2. It is seen from the comparison that the proposed approach provides a competitive performance in terms of solution quality and a better performance in terms of CPU time.