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MATH208 Operations Research课程简介

Operations Research utlizes mathematical modeling, techniques, and algorithms, to most appropriately allocate resources and meet goals. An alternative title for the course might be “Mathematical Theory of Management, Control and Decision Making” . In general the course is intended for students with interests in applied math, statistics, economics, engineering and science. A second semester of the course devoted to probabilistic techniques will be offered in the spring semester.

PREREQUISITES 

Linear programming is a widely-used optimization technique that involves linear objective functions and linear constraints. The simplex algorithm is a widely-used algorithm for solving linear programming problems, and it involves moving from one feasible solution to another in an iterative fashion until an optimal solution is found. Sensitivity analysis involves examining the effects of changes in the objective function coefficients and constraint values on the optimal solution.

Dual problems are closely related to linear programming problems and involve the optimization of a dual objective function subject to dual constraints. The dual problem is used to provide information about the original problem, including bounds on the optimal objective value and information about the shadow prices of the constraints.

Integer programming is a type of linear programming that involves additional constraints that require the variables to take on integer values. Network models involve the modeling of complex systems using networks, such as transportation or communication systems. Dynamic programming is a powerful optimization technique that involves breaking down a problem into smaller subproblems and solving each subproblem in a recursive manner.

Finally, the KKT conditions are a set of necessary conditions for an optimization problem to have an optimal solution. These conditions involve the first-order conditions for optimality and the complementary slackness conditions, and they are used to analyze optimization problems and to derive insights about their solutions.

MATH208 Operations Research HELP（EXAM HELP， ONLINE TUTOR）

To determine a planting plan that will minimize the cost of meeting the demands, we need to set up a linear programming problem. Let’s define the following decision variables:

• x1: number of acres of land planted with wheat
• x2: number of acres of land planted with corn

We want to minimize the cost of meeting the demands, so the objective function is:

minimize 3×1 + 2×2

where 3 represents the cost of planting wheat per acre and 2 represents the cost of planting corn per acre.

We also have the following constraints:

• x1 + x2 ≤ 100 (total land available)
• 20×1 + 10×2 ≥ 11,000 (wheat demand)
• 10×1 + 30×2 ≥ 7,000 (corn demand)

The first constraint ensures that we do not plant more land than what is available. The second constraint represents the wheat demand, where 20 bushels of wheat can be produced per acre of land planted with wheat and 10 bushels of wheat can be produced per acre of land planted with corn. Similarly, the third constraint represents the corn demand, where 10 bushels of corn can be produced per acre of land planted with wheat and 30 bushels of corn can be produced per acre of land planted with corn.

Putting all of these together, we get the following linear programming problem:

minimize 3×1 + 2×2

subject to:

x1 + x2 ≤ 100 20×1 + 10×2 ≥ 11,000 10×1 + 30×2 ≥ 7,000

To solve this problem using the simplex algorithm, we need to convert it into standard form. To do this, we introduce slack variables s1, s2, and s3 to represent the slack in the constraints. The problem becomes:

minimize 3×1 + 2×2

subject to:

x1 + x2 + s1 = 100 20×1 + 10×2 – s2 = 11,000 10×1 + 30×2 – s3 = 7,000

All variables are non-negative.

Using the simplex algorithm, we can solve this problem and obtain the optimal solution:

x1 = 250, x2 = 250, s1 = 0, s2 = 0, s3 = 0

This means that we should plant 250 acres of land with wheat and 250 acres of land with corn to meet the demands at the minimum cost of $1,500.

To determine the optimal investment strategy, we need to calculate the future value of each investment and compare them. Let’s first calculate the future value of Investment A:

• In the first year, we invest $$ 1$, and it yields $$ 0.10$ in the second year.
• In the second year, we invest $$ 1.10$, and it yields $$ 0.10$ in the third year.
• In the third year, we invest $$ 1.20$, and it yields $$ 1.30$ in the fourth year.
• The total future value of Investment A is $$ 2.50$.

Now, let’s calculate the future value of Investment B:

• In the first year, we invest $$ 1$, and it does not yield any return.
• In the second year, we invest $$ 1$, and it yields $$ 1.35$ in the fourth year.
• The total future value of Investment B is $$ 2.35$.

Since Investment A has a higher future value than Investment B, we should invest our $$ 100$ in Investment A. This means that we should invest $$ 20$ each year for the next five years in Investment A.

Note that this assumes that we can invest fractional amounts of money. If we can only invest whole dollars, we would need to adjust the investment amounts to account for this constraint.

Textbooks

• An Introduction to Stochastic Modeling, Fourth Edition by Pinsky and Karlin (freely
available through the university library here)
• Essentials of Stochastic Processes, Third Edition by Durrett (freely available through
the university library here)
To reiterate, the textbooks are freely available through the university library. Note that
you must be connected to the university Wi-Fi or VPN to access the ebooks from the library
links. Furthermore, the library links take some time to populate, so do not be alarmed if
the webpage looks bare for a few seconds.

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