This survey relates to using dynamic optimization to solve for optimal capacity investment planning regarding the problem in the academic journal presented “A dynamic programming model for environmental investment decision-making in coal mining” by Siwei Gao, published in Applied Energy. Additionally, it should be acknowledged this survey report was produced for ISE 3210 (Nonlinear and Dynamic Optimization) at Ohio State University and is not meant for reproduction or publication.
To protect the environments of coal mining areas, major coal producing countries such as China have issued law and regulations for the coal mining industry. Specifically, the Chinese government released al industry. For example, the Chinese government released the Emission Standard for Pollutants from the Coal Industry (GB 20426-2006) guidelines for coal miners to abide by. The major pollutants addressed are coal-bed methane, mine-water, fly-ash and dust, SO2, and coal gangue, as these present both safety hazards and severe environmental impact. The guidelines state all large coal companies should invest in prescribed pollution emission control projects to meet standards or coal companies will be fined or forced to shut down. These fines are called “penalty costs.” Coal mines are needing to invest monetary capital in current pollution treatment projects such as environmental technologies or waste recycling utilization procedures through expanding pollution treatment capacity to avoid such penalties. Coal mines seek to make annual pollution treatment capacity expansion investments to minimize the total investment capital cost of these undertakings. Dynamic programming procedure can aid in finding a sequence of decisions regarding how much a coal mining company should increase pollution treatment capacity on an annual basis while minimizing investment capital cost and pollution penalty cost.
THIS A HMK ASSIGMENT FOR OHIO STATE ISE 3210: NONLINEAR AND DYNAMIC OPTIMIZATION
Select one real-world system that can be or has been optimized or improved using dynamic optimization. Conduct a survey of any academic literature related to using dynamic programming to optimize or improve the system in question. Note that 'academic literature' means academic books or papers published in academic journals. It does not mean wikipedia (Links to an external site.), blogs, or random websites that you stumble upon. Of course, you may use such resources to get your research underway.
1.) Prepare a report that summarizes your research. Your report does need to provide some technical details. At a minimum it should provide an outline of a model structure that can be used in said optimization or improvement. This model outline should be succinct, but provide enough detail so that someone who is familiar with dynamic optimization, but not the particular application in question, can understand how the system is modeled. The outline of the model structure does not have to be given necessarily as a mathematical formulation. You can use a mathematical formulation if doing so makes conveying the model structure easier. However, if it is easier to convey the structure in another manner (e.g., as a narrative), you are free to do so. Your report should be no more than three pages in length, excluding references, which can be on a separate page and formatted in any reasonably decipherable manner. Your report should use at least 10-point font and can be single-spaced.
2.) Develop MATLAB code that can solve an arbitrary instance of the dynamic optimization application that you surveyed in Problem 1. To do so, you should provide first a succinct model formulation. The model formulation that you provide may be a simplification of models appearing in the literature that you survey. However, the model formulation should be non-trivial (e.g., it could not be solved easily by hand or visual inspection). Second, you should develop MATLAB code that can solve an arbitrary instance of the problem (i.e., with any sensible set of problem data). Finally, you should provide required problem data for a small but non-trivial instance of the problem and a summary of the solution and solution time for the problem instance.