### 数学代写|运筹学作业代写operational research代考|MAT2200

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 数学代写|运筹学作业代写operational research代考|Mathematical modeling by example

A toy company makes two types of toys: toy soldiers and trains. Each toy is produced in two stages, first it is constructed in a carpentry shop, and then it is sent to a finishing shop, where it is varnished, vaxed, and polished. To make one toy soldier costs $\$ 10$for raw materials and$\$14$ for labor; it takes 1 hour in the carpentry shop, and 2 hours for finishing. To make one train costs $\$ 9$for raw materials and$\$10$ for labor; it takes 1 hour in the carpentry shop, and 1 hour for finishing.

There are 80 hours available each week in the carpentry shop, and 100 hours for finishing. Each toy soldier is sold for $\$ 27$while each train for$\$21$. Due to decreased demand for toy soldiers, the company plans to make and sell at most 40 toy soldiers; the number of trains is not restriced in any way.
What is the optimum (best) product mix (i.e., what quantities of which products to make) that maximizes the profit (assuming all toys produced will be sold)?

Constraints:

• producing $x_1$ toy soldiers and $x_2$ toy trains requires
(a) $1 x_1+1 x_2$ hours in the carpentry shop; there are 80 hours available
(b) $2 x_1+1 x_2$ hours in the finishing shop; there are 100 hours available
• the number $x_1$ of toy soldiers produced should be at most 40
Variable domains: the numbers $x_1, x_2$ of toy soldiers and trains must be non-negative (sign restriction)
\begin{aligned} \operatorname{Max} 3 x_1+2 x_2 & \ x_1+x_2 & \leq 80 \ 2 x_1+x_2 & \leq 100 \ x_1 & \leq 40 \ x_1, x_2 & \geq 0 \end{aligned}
We call this a program. It is a linear program, because the objective is a linear function of the decision variables, and the constraints are linear inequalities (in the decision variables).

## 数学代写|运筹学作业代写operational research代考|Activity-based formulation

Instead of constructing the formulation as before (row-by-row), we can proceed by columns.
We can view columns of the program as activities. An activity has
inputs: materials consumed per unit of activity
(1lb of steel and $41 \mathrm{bs}$ of wood)
outputs: products produced per unit of activity
(\$12 of profit) activity level: a level at which we operate the activity (indicated by a variable$x_1$) Operating the activity “Chair$1 “$at level$x_1$means that we produce$x_1$chairs of type 1 , each consuming 1 lb of steel,$4 \mathrm{lbs}$of wood, and producing$\$12$ of profit. Activity levels are always assumed to be non-negative.
The materials/labor/profit consumed or produced by an activity are called items (correspond to rows).
The effect of an activity on items (i.e. the amounts of items that are consumed/producedby an activity) are input-output coefficients.
The total amount of items available/supplied/required is called the external flow of items.
We choose objective to be one of the items which we choose to maximize or minimize.
Last step is to write material balance equations that express the flow of items in/out of activies and with respect to the external flow.

# 运筹学代考

## 数学代写|运筹学作业代写operational research代考|Mathematical modeling by example

• 生产 $x_1$ 玩具士兵和 $x_2$ 玩具火车需要
(a) $1 x_1+1 x_2$ 在木工车间工作的时间；有 80 小时可用
(b) $2 x_1+1 x_2$ 在精加工车间工作的时间；有 100 小时可用
• 号码 $x_1$ 生产的玩具士兵数量最多应为 40
变量域：数字 $x_1, x_2$ 玩具士兵和火车的数量必须为非负数（符号限制)
$$\operatorname{Max} 3 x_1+2 x_2 x_1+x_2 \quad \leq 802 x_1+x_2 \leq 100 x_1 \quad \leq 40 x_1, x_2 \geq 0$$
我们称之为程序。它是一个线性程序，因为目标是决策变量的线性函数，约束是线性不等式（在决策 变量中）。

（1 磅钢材和41b秒木材）

（利润 12美元
）活动水平：我们开展活动的水平
（由变量表示X1)

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。