Masterarbeit, 2016
45 Seiten, Note: B
CHAPTER ONE
INTRODUCTION
1.1Background to the Study
1.2 Statement of the Problem
1.3 Objectives of the Study
1.4 Research Question.
1.5 Research Hypothesis
1.6 Scope of the Study
1.7 Significance of the Study.
1.8 Limitations to the Study
1.9 Definition of Terms
CHAPTER TWO
REVIEW OF RELATED LITERATURE
2.1 Conceptual Review
2.2 Empirical Studies
2.3 Theoretical Framework
CHAPTER THREE
RESEARCH METHOD
3.1 Research Design
3.2 Population of the Study
3.3 Sampling Procedure
3.4 Method of Data Collection
3.5 Model Specification
3.6 Recursive Analysis by a Network Model.
CHAPTER FOUR
ANALYSIS OF DATA AND DISCUSSIONS
4.1 Model Analysis
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary
5.2 Conclusion
5.3 Recommendations
5.4 Contributions to Knowledge
5.5 Suggestions for further research
This study aims to develop a mathematical model that facilitates the development of the Niger Delta region through a phase-by-phase approach. The primary research objective is to demonstrate that integrating development planning with dynamic programming can effectively solve the region's complex problems of underdevelopment, youth restiveness, and environmental degradation, by breaking down long-term goals into manageable, optimal project stages.
Meaning of Dynamic programming.
Dynamic programming (DP) determines the optimum solution of a multivariable problem by decomposing it into stages, each stage comprising a single variable sub problem. The advantage of the decomposition is that the optimization process at each stage involves one variable only, a simpler task computationally than dealing with all the variables simultaneously (Taha, 2007). The mathematical technique of optimizing sequence of interrelated decision over a period of time is called dynamic programming. It uses the idea of recursion to solve a complex problem, broken into a series of interrelated (sequential) decision stages (also called sub- problems) where the outcome of a decision at one stage affects the decision at each of next stage (Sharma, 2005).
Vohra (2007) defines dynamic programming as a useful quantitative analysis technique that can be use to solve many optimization problems. It deals with relatively large and complex problems, and involving making a sequence of interrelated decisions. The techniques provide a system procedure for determining optimal combination of decisions. A given problem may be complex if it is not possible to solve it in one go. It may be solved by breaking it into a series of smaller and more tractable sub-problems. This is exactly what dynamic programming does. In most applications; it obtains solutions by working backward from the end of the problem toward the beginning. But, in solving the Niger- Delta problem of under-development, forward recursive approach is advanced.
CHAPTER ONE: INTRODUCTION: This chapter introduces the context of underdevelopment in the Niger Delta, defining the problem statement, research objectives, and the significance of the study regarding the need for an integrated, dynamic development approach.
CHAPTER TWO: REVIEW OF RELATED LITERATURE: This chapter examines existing literature on development, underdevelopment, strategic planning, and the mathematical principles of dynamic programming, establishing the theoretical framework for the study.
CHAPTER THREE: RESEARCH METHOD: This section details the research design, population, sampling procedures, and the specific methodology of using a network model analyzed through recursive equations to map the development journey.
CHAPTER FOUR: ANALYSIS OF DATA AND DISCUSSIONS: This chapter provides the practical model analysis, applying secondary data to the network model to calculate optimal development pathways and evaluate findings against the research objectives.
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS: This final chapter synthesizes the findings, provides conclusions on the viability of the dynamic programming approach, and offers specific recommendations for stakeholders to foster sustainable development.
Underdevelopment, Development, Dynamic approach, Mathematical model, Niger-Delta, Dynamic programming, Recursive equation, Network model, Strategic planning, Resource optimization, Infrastructure development, Economic growth, Youth restiveness, Policy implementation, Sustainable development.
The research focuses on tackling the persistent underdevelopment of the Niger Delta region by proposing a structured, mathematical, and phase-by-phase development framework.
The key themes include the impact of oil exploration, regional underdevelopment, the necessity of political will, and the application of operations research to solve complex socio-economic problems.
The primary objective is to create a dynamic mathematical model that can effectively guide the execution of development projects to transform the region from its current state of underdevelopment to a more desirable level.
The study employs a quantitative research method, specifically utilizing Dynamic Programming (DP) and a Network Model, to analyze and determine the optimal sequence of development decisions.
The main body covers the conceptual definitions of development, a comprehensive literature review, the formulation of a recursive mathematical model, and the application of that model to analyze project completion data from the Niger Delta.
Key terms include Niger-Delta, Underdevelopment, Dynamic programming, Mathematical model, Strategic planning, and Sustainable development.
The author argues that while backward recursion is standard in literature, it can become overly complicated; therefore, the forward recursive approach was adopted for better logic and clarity in solving the specific developmental sequence of the Niger Delta.
The model uses circular nodes to represent specific development indicators (such as militancy eradication, agriculture, and infrastructure) and arrows to represent the stages of implementation, helping stakeholders visualize the journey of development as a continuous, manageable process.
Time acts as a critical factor in the model, where the objective is to minimize the total duration of project completion to avoid the costs of inflation, project abandonment, and corruption.
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