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## PowerPoint Slideshow about 'Transportation Models' - libitha

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Operations Research Modeling Toolset

Queueing Theory

Markov Chains

PERT/ CPM

Network Programming

Dynamic Programming

Simulation

Markov Decision Processes

Inventory Theory

Linear Programming

Stochastic Programming

Forecasting

Integer Programming

Decision Analysis

Nonlinear Programming

Game Theory

Network Problems

- Linear programming has a wide variety of applications
- Network problems
- Special types of linear programs
- Particular structure involving networks
- Ultimately, a network problem can be represented as a linear programming model
- However the resulting A matrix is very sparse, and involves only zeroes and ones
- This structure of the A matrix led to the development of specialized algorithms to solve network problems

Types of Network Problems

- Shortest Path

Special case: Project Management with PERT/CPM

- Minimum Spanning Tree
- Maximum Flow/Minimum Cut
- Minimum Cost Flow

Special case: Transportation and Assignment Problems

- Set Covering/Partitioning
- Traveling Salesperson
- Facility Location

and many more

The Transportation Problem

- The problem of finding the minimum-cost distribution of a given commodity from a group of supply centers (sources) i=1,…,mto a group of receiving centers (destinations) j=1,…,n
- Each source has a certain supply (si)
- Each destination has a certain demand (dj)
- The cost of shipping from a source to a destination is directly proportional to the number of units shipped

Simple Network Representation

Sources

Destinations

1

Supply s1

1

Demand d1

Supply s2

2

2

Demand d2

…

…

xij

n

Demand dn

Supply sm

m

Costs cij

Example: P&T Co.

- Produces canned peas at three canneries

Bellingham, WA, Eugene, OR, and Albert Lea, MN

- Ships by truck to four warehouses

Sacramento, CA, Salt Lake City, UT, Rapid City, SD, and Albuquerque, NM

- Estimates of shipping costs, production capacities and demands for the upcoming season is given
- The management needs to make a plan on the least costly shipments to meet demand

Example: P&T Co. Data

Shipping cost per truckload

Example: P&T Co.

- Network representation

Feasible Solutions

- A transportation problem will have feasible solutions if and only if
- How to deal with cases when the equation doesn’t hold?

Integer Solutions Property: Unimodularity

- Unimodularity relates to the properties of the A matrix(determinants of the submatrices, beyond scope)
- Transportation problems are unimodular, so we get the integers solutions property:

For transportation problems, when every si and dj have an integer value, every BFS is integer valued.

- Most network problems also have this property.

Transportation Simplex Method

- Since any transportation problem can be formulated as an LP, we can use the simplex method to find an optimal solution
- Because of the special structure of a transportation LP, the iterations of the simplex method have a very special form
- The transportation simplex method is nothing but the original simplex method, but it streamlines the iterations given this special form

Transportation Simplex Method

Initialization

(Find initial CPF solution)

Is the current CPF solution optimal?

Yes

Stop

No

Move to a better adjacent CPF solution

Prototype Problem

- Holiday shipments of iPods to distribution centers
- Production at 3 facilities,
- A, supply 200k
- B, supply 350k
- C, supply 150k
- Distribute to 4 centers,
- N, demand 100k
- S, demand 140k
- E, demand 300k
- W, demand 250k
- Total demand vs. total supply

Finding an Initial BFS

- The transportation simplex starts with an initial basic feasible solution (as does regular simplex)
- There are alternative ways to find an initial BFS, most common are
- The Northwest corner rule
- Vogel’s method
- Russell’s method (beyond scope)

The Northwest Corner Rule

- Begin by selecting x11, let x11 = min{ s1, d1 }
- Thereafter, if xij was the last basic variable selected,
- Select xi(j+1) if source i has any supply left
- Otherwise, select x(i+1)j

The Northwest Corner Rule

Z = 10770

Vogel’s Method

- For each row and column, calculate its difference:

= (Second smallest cij in row/col) - (Smallest cij in row/col)

- For the row/col with the largest difference, select entry with minimum cij as basic
- Eliminate any row/col with no supply/demand left from further steps
- Repeat until BFS found

Optimality Test

- In the regular simplex method, we needed to check the row-0 coefficients of each nonbasic variable to check optimality and we have an optimal solution if all are 0
- There is an efficient way to find these row-0 coefficients for a given BFS to a transportation problem:
- Given the basic variables, calculate values of dual variables
- ui associated with each source
- vj associated with each destination

using cij – ui – vj = 0 for xij basic, or ui + vj = cij

(let ui = 0 for row i with the largest number of basic variables)

- Row-0 coefficients can be found from c’ij=cij-ui-vj for xij nonbasic

Optimality Test (2)

- Calculate ui, vj using cij – ui – vj = 0 for xij basic

(let ui = 0 for row i with the largest number of basic variables)

60

140

210

140

100

50

90

Optimal Solution

Sources

Destinations

N

Demand = 100

A

140

Supply = 200

60

S

Demand = 140

210

Supply = 350

B

140

E

Demand = 300

(shortage of 90)

100

Supply = 150

C

50

W

Demand = 250

Cost Z = 10330

An Iteration

- Find the entering basic variable
- Select the variable with the largest negative c’ij
- Find the leaving basic variable
- Determine the chain reaction that would result from increasing the value of the entering variable from zero
- The leaving variable will be the first variable to reach zero because of this chain reaction

Optimal Solution

Sources

Destinations

N

Demand = 100

60

A

Supply = 200

140

S

Demand = 140

40

Supply = 350

B

210

E

Demand = 300

(shortage of 90)

100

Supply = 150

C

150

W

Demand = 250

Cost Z = 10330

The Assignment Problem

- The problem of finding the minimum-costly assignment of a set of tasks (i=1,…,m) to a set of agents (j=1,…,n)
- Each task should be performed by one agent
- Each agent should perform one task
- A cost cij associated with each assignment
- We should have m=n (if not…?)
- A special type of linear programming problem, and
- A special type of transportation problem,with si=dj= ?

Prototype Problem

- Assign students to mentors
- Each assignment has a ‘mismatch’ index
- Minimize mismatches

Solving the Assignment Problem

- It is a linear programming problem, so we could use regular simplex method
- It is a transportation problem, so we could use transportation simplex method
- However, it has a very special structure, such that it can be solved in polynomial time
- Many such algorithms exist, but the best known (and one of the oldest) is the Hungarian Method

The Hungarian Method

- Subtract row minimums from each element in the row
- Subtract column minimums from each element in the column
- Cover the zeroes with as few lines as possible
- If the number of lines = n, then optimal solution is hidden in zeroes
- Otherwise, find the minimum cost that is not covered by any lines
- Subtract it from all uncovered elements
- Add it to all elements at intersections (covered by two lines)
- Back to step 3

The Hungarian Method – Optimal Solution

How to identify the optimal solution:

- Make the assignments one at a time in positions that have zero elements.
- Begin with rows or columns that have only one zero. Cross out both the row and the column involved after each assignment is made.
- Move on to the rows and columns that are not yet crossed out to select the next assignment, with preference given to any such row or column that has only one zero that is not crossed out.
- Continue until every row and every column has exactly one assignment and so has been crossed out.

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