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البرمجة الديناميكية و استخدامها في توزيع الاستثمارات بين القطاعات الاقتصادية في سورية

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 Publication date 2002
and research's language is العربية
 Created by Shamra Editor




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References used
أ.د. سلطان تركي ابراهيم: التحليلات الكمية في اتخاذ القرارات – منشورات جامعة الملك سعود .1984
أ.د. بقجه جي صباح الدين: بحوث العمليات – المعهد العالي للعلوم السياسية – وزارة التعليم . العالي عام 2000
أ.د. بقجه جي صباح الدين: بحوث العمليات – ترجمة عن المنظمة العربية للتربية والثقافة . والعلوم – المركز الثقافي العربي للتعريب والترجمة والتأليف والنشر – دمشق 1998
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This research aims at studying the relationship between the industrial demographic variables as it is one of the most important Syrian economic sectors and the most laboremployment, and the inputs of this sector in particular and the inputs of the rest of the economic sectors. A number of results were found, most notably the existence of a statistically significant relationship between the inputs of the industrial sector and the total number of workers in the industrial sector, and the relationship between these inputs and the qualitative, sectoral and educational structure of the industrial sector.
يتناول هذا البحث دور الاستثمار الأجنبي المباشر في اليمن باعتباره أحد مصادر التمويل الخارجي المرغوب فيه في الوقت الحاضر. و يهدف هذا البحث إلى استعراض المناخ الاستثماري السائد في اليمن و معوقات الاستثمار فيه، كما يهدف إلى تسليط الضوء على الاستثمارات الأجنبية المباشرة في التنمية الاقتصادية. و توصل البحث إلى أن حجم الاستثمارات متواضع، و أن دورها في التنمية الاقتصادية ضعيف. و يعود ذلك إلى المناخ الاستثماري السائد الذي لم يكن مهيئًا لجذب هذه الاستثمارات، على الرغم من أن قانون الاستثمار تضمن العديد من الميزات و الإعفاءات الممنوحة للمشاريع الاستثمارية، و معاملة المستثمر الأجنبي معاملة المستثمر اليمني، إلا أن هذا القانون و تعديلاته لم تؤد إلى زيادة حجم الاستثمار الأجنبي المتوقع و ذلك بسبب العوامل الأخرى المكونة للمناخ الاستثماري.
Earthmoving is the process of moving and processing soil from one location to another to alter an existing land surface into a desired configuration. Highways, dams, and airports are typical examples of heavy earthmoving projects. Over the years, con struction managers have devised ways to determine the quantities of material to be moved from one place to another. Various types of soil (soft earth, sand, hard clay, …, etc.) create different level of difficulty of the problem. Earthmoving problem has traditionally been solved using mass diagram method or variety of operational research techniques. However, existing models do not present realistic solution for the problem. Multiple soil types are usually found in cut sections and specific types of soil are required in fill sections. Some soil types in cut sections are not suitable to be used in fill sections and must be disposed of. In this paper a new mathematical programming model is developed to find-out the optimum allocation of earthmoving works. In developing the proposed model, different soil types are considered as well as variation of unit cost with earth quantities moved. Suggested borrow pits and/or disposal sites are introduced to minimize the overall earthmoving cost. The proposed model is entirely formulated using the programming capabilities of VB6 while LINDO is used to solve the formulated model to get the optimum solution. An example project is presented to show how the developed model can be implemented.
Shadow economy is considered the most important economic problem which is still of concern to researchers for it is linked to all economic variables. It is found in all States with different economic patterns. But it is more widespread in developing economies includes a recipe for lawful activities and other illegal .And it exists in all economic levels and affect all social strata and in all ages. Despite its characterization as a phenomenon, it cannot be overlooked as a reality. Depending on that, this research tried to clarify the concept of the shadow economy, and identify its components. As well as, this research shows the most important macroeconomic indicators in Syria and the impact of economic variables on the shadow economy.

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