哈尔滨工程大学：《数学建模》课程教学资源（MCM写作模版）优缺点

Evaluations of solutions Strengths Y Our main model's strength is its enormous edibility. For instance ncluding all these factors into a single robust framework, our model enables We developed a theoretical line formation model which agrees without rough data. Our computer model agrees with both despite working on different principles, imply ing it behaves as we want y This allows us to make substantive conclusions about Y Finally, our model is strong because of monte Carlo simulation has been perfectly used in our models, and the simulation results are consistent with the reality We introduced... in order to improve the exchange quality. The chain rules can also modified in a degree t The models used in our paper is promotional, in view of different consideration t we can modify our models conveniently t the model is independent of the site simulated() Y the( )model is intuitive t the algorithm is efficient Y a corresponding strength of our model is that it would be relatively easy to include a parameter for probability of Y Our model is particularly appropriate for simulation of.... a problem that naturally lends itself to such discrete modeling t The fundamental strengths of our model are The model is independent of Y Processor-based model has few input parameters, leading to good robustness and Y Uses a variety of modeling techniques in an integrated, holistic model Y Our model effectively achieved all of the goals we set initially. It was fast and could handle large quantities of data, but also had the flexibility we desired. Though we did not test all possibilities, we showed that our model optimizes state districts for any of a number of variables. If we had chosen to input income, poverty, crime or education data into our nterest function, we could have produced high-quality results with virtually no added difficulty. As well, our method was robust Y Our main model's strength is its enormous flexibility. For instance Y This allows us to make substantive conclusions about policy issues, even without extensive data sets. By varying parameters, allocation rules, and our program's objective function--all quite feasible within the structure--we can examine the guts of policymaking: the ethical principles underly ing a policy, the implementation rules designed to fulfill them, and the sometimes nebulous numbers that govern the results Y Finally, our model is strong because of its discrete setup The fundamental strengths of our model are its robustness and flexibility. All of

Evaluations of solutions Strengths  Our main model's strength is its enormous edibility. For instance,……..Including all these factors into a single, robust framework, our model enables  We developed a theoretical line formation model which agrees without rough data. Our computer model agrees with both despite working on different principles, implying it behaves as we want.  This allows us to make substantive conclusions about  Finally, our model is strong because of  The Monte Carlo simulation has been perfectly used in our models, and the simulation results are consistent with the reality.  We introduced …… in order to improve the exchange quality. The chain rules can also modified in a degree.  The models used in our paper is promotional, in view of different consideration,  we can modify our models conveniently.  the model is independent of the site simulated( )…  the( )model is .intuitive  the algorithm is efficient ::  a corresponding strength of our model is that it would be relatively easy to include a parameter for probability of ……  Our model is particularly appropriate for simulation of ……, a problem that naturally lends itself to such discrete modeling.  The fundamental strengths of our model are…  The model is independent of…  Processor-based model has few input parameters, leading to good robustness and sensitivity.  Uses a variety of modeling techniques in an integrated, holistic model.  Our model effectively achieved all of the goals we set initially. It was fast and could handle large quantities of data, but also had the flexibility we desired. Though we did not test all possibilities, we showed that our model optimizes state districts for any of a number of variables. If we had chosen to input income, poverty, crime or education data into our interest function, we could have produced high-quality results with virtually no added difficulty. As well, our method was robust.  Our main model's strength is its enormous flexibility. For instance  This allows us to make substantive conclusions about policy issues, even without extensive data sets. By varying parameters, allocation rules, and our program's objective function——all quite feasible within the structure——we can examine the guts of policymaking: the ethical principles underlying a policy, the implementation rules designed to fulfill them, and the sometimes nebulous numbers that govern the results.  Finally, our model is strong because of its discrete setup.  The fundamental strengths of our model are its robustness and flexibility. All of

the data is fully parameterized, so the model can be applied to We eaknesses Y Some special data cant be found, and it makes that we have to do some proper assumption before the solution of our models. A more abundant data resource can guarantee a better result in our models. Current line length is not taken into account by the line formation model. In real life Y Weaknesses of the model included assumptions made for simplicity that likely do not hold. For instance, in most runs of our model on(sides ...), cases(impact/conclusion This feature is likely a result of our assumption that /The primary weakness of this model is the( ) It should be to eliminate this. another weakness that could be corrected with more analysis is() t The primary weakness of this model is the Another weakness that could be corrected with more analysis is Y Parameters have to be derived from physical occurrences Y The other primary weakness of our model is our lack of metrics for comparison Although we list the model's comprehensive, discrete simulation as a strength, it is Y(Paradoxically )also the most notable weakness. Our results lack clear.. Second,our model demands great attention to. While its general structure and methodology are valid, the specific figures embedded in its code are not airtight ength, it is (paradoxically) also the most notable weakness. Our results lack clear illustrative power; data manipulated through a computer program cannot achieve the same Indeed there is a fundamental tradeoff here between realism and elegance, and our model arguably veers toward over realism

the data is fully parameterized, so the model can be applied to…… Weaknesses  Some special data can’t be found, and it makes that we have to do some proper assumption before the solution of our models. A more abundant data resource can guarantee a better result in our models. Current line length is not taken into account by the line formation model. In real life……  Weaknesses of the model included assumptions made for simplicity that likely do not hold. For instance, in most runs of our model on(sides……), cases (impact/conclusion) to…… This feature is likely a result of our assumption that /The primary weakness of this model is the( ), It should be possible to eliminate this, another weakness that could be corrected with more analysis is ( )`  The primary weakness of this model is the… Another weakness that could be corrected with more analysis is …  Parameters have to be derived from physical occurrences.  The other primary weakness of our model is our lack of metrics for comparison.  Although we list the model's comprehensive, discrete simulation as a strength, it is  (Paradoxically) also the most notable weakness. Our results lack clear….Second ,our model demands great attention to….While its general structure and methodology are valid, the specific figures embedded in its code are not airtight.  Although we list the model's comprehensive, …… as a strength, it is (paradoxically) also the most notable weakness. Our results lack clear illustrative power; data manipulated through a computer program cannot achieve the same effect as ……  Indeed, there is a fundamental tradeoff here between realism and elegance, and our model arguably veers toward over realism