1. Transportation, Assignment, and Transshipment Problems
Transportation Problem
Network Representation
General LP Formulation
Assignment Problem
Transshipment Problem

2. Transportation, Assignment, and Transshipment Problems
A network model is one which can be represented by a set of nodes, a set of arcs, and functions (e.g. costs, supplies, demands, etc.) associated with the arcs and/or nodes.
Transportation, assignment, and transshipment problems of this chapter as well as the PERT/CPM problems (in another chapter) are all examples of network problems.

3. Transportation, Assignment, and Transshipment Problems
Each of the three models of this chapter can be formulated as linear programs and solved by general purpose linear programming codes.
For each of the three models, if the right-hand side of the linear programming formulations are all integers, the optimal solution will be in terms of integer values for the decision variables.
However, there are many computer packages (including The Management Scientist) that contain separate computer codes for these models which take advantage of their network structure.

4. Transportation Problem
The transportation problem seeks to minimize the total shipping costs of transporting goods from m origins (each with a supply si) to n destinations (each with a demand dj), when the unit shipping cost from an origin, i, to a destination, j, is cij.
The network representation for a transportation problem with two sources and three destinations is given on the next slide.

5. Transportation Problem
Network Representation 1 d1
c11 1
c12 s1
c13 2 d2
c21 2
c22 s2
c23 3 d3
Sources
Destinations

6. Transportation Problem
LP Formulation
The LP formulation in terms of the amounts shipped from the origins to the destinations, xij , can be written as:
Min cijxij
i j
s.t. xij < si for each origin i j
xij = dj for each destination j i
xij > 0 for all i and j

7. Transportation Problem
LP Formulation Special Cases
The following special-case modifications to the linear programming formulation can be made:
Minimum shipping guarantee from i to j:
xij > Lij
Maximum route capacity from i to j:
xij < Lij
Unacceptable route:
Remove the corresponding decision variable.

8. Acme Example: Acme Block Co.
Acme Block Company has orders for 80 tons of
concrete blocks at three suburban locations
as follows: Northwood tons,
Westwood tons, and
Eastwood tons. Acme
has two plants, each of which
can produce 50 tons per week.
Delivery cost per ton from each plant
to each suburban location is shown on the next slide.
How should end of week shipments be made to fill
the above orders?
Acme

9. Example: Acme Block Co. Delivery Cost Per Ton
Northwood Westwood Eastwood
Plant
Plant

10. Example: Acme Block Co.
Partial Spreadsheet Showing Problem Data

11. Example: Acme Block Co.
Partial Spreadsheet Showing Optimal Solution

12. Example: Acme Block Co. Optimal Solution From To Amount Cost
Plant 1 Northwood
Plant 1 Westwood ,350
Plant 2 Northwood
Plant 2 Eastwood
Total Cost = $2,490

13. Example: Acme Block Co.
Partial Sensitivity Report (first half)

14. Example: Acme Block Co.
Partial Sensitivity Report (second half)

15. Assignment Problem
An assignment problem seeks to minimize the total cost assignment of m workers to m jobs, given that the cost of worker i performing job j is cij.
It assumes all workers are assigned and each job is performed.
An assignment problem is a special case of a transportation problem in which all supplies and all demands are equal to 1; hence assignment problems may be solved as linear programs.
The network representation of an assignment problem with three workers and three jobs is shown on the next slide.

16. Assignment Problem Network Representation c11 c12 c13 Agents Tasks c21

17. Assignment Problem LP Formulation Min cijxij i j
s.t. xij = for each agent i j
xij = for each task j i
xij = 0 or 1 for all i and j
Note: A modification to the right-hand side of the first constraint set can be made if a worker is permitted to work more than 1 job.

18. Assignment Problem LP Formulation Special Cases
Number of agents exceeds the number of tasks:
xij < for each agent i j
Number of tasks exceeds the number of agents:
Add enough dummy agents to equalize the
number of agents and the number of tasks.
The objective function coefficients for these
new variable would be zero.

19. Assignment Problem LP Formulation Special Cases (continued)
The assignment alternatives are evaluated in terms of revenue or profit:
Solve as a maximization problem.
An assignment is unacceptable:
Remove the corresponding decision variable.
An agent is permitted to work a tasks:
xij < a for each agent i j

20. Example: Who Does What?
An electrical contractor pays his subcontractors a fixed fee plus mileage for work performed. On a given day the contractor is faced with three electrical jobs associated with various projects. Given below are the distances between the subcontractors and the projects.
Projects
Subcontractor A B C
Westside
Federated
Goliath
Universal
How should the contractors be assigned to minimize total mileage costs?

21. Example: Who Does What? Network Representation A Subcontractors50
West. A 36 16
Subcontractors
Projects 28
Fed. 30 B 18 35 32
Gol. C 20 25 25
Univ. 14

22. Example: Who Does What? Linear Programming Formulation
Min 50x11+36x12+16x13+28x21+30x22+18x23
+35x31+32x32+20x33+25x41+25x42+14x43
s.t. x11+x12+x13 < 1
x21+x22+x23 < 1
x31+x32+x33 < 1
x41+x42+x43 < 1
x11+x21+x31+x41 = 1
x12+x22+x32+x42 = 1
x13+x23+x33+x43 = 1
xij = 0 or 1 for all i and j
Agents
Tasks

23. Example: Who Does What? The optimal assignment is:
Subcontractor Project Distance
Westside C
Federated A
Goliath (unassigned)
Universal B
Total Distance = 69 miles

24. Transshipment Problem
Transshipment problems are transportation problems in which a shipment may move through intermediate nodes (transshipment nodes)before reaching a particular destination node.
Transshipment problems can be converted to larger transportation problems and solved by a special transportation program.
Transshipment problems can also be solved by general purpose linear programming codes.
The network representation for a transshipment problem with two sources, three intermediate nodes, and two destinations is shown on the next slide.

25. Transshipment Problem
Network Representation
c36 3
c13 1
c37 6 s1 d1
c14
c15
c46 4
c47
Demand
Supply
c23
c24
c56 2 7 d2 s2
c25 5
c57
Sources
Destinations
Intermediate Nodes

26. Transshipment Problem
Linear Programming Formulation
xij represents the shipment from node i to node j
Min cijxij
i j
s.t. xij < si for each origin i j
xik - xkj = for each intermediate
i j node k
xij = dj for each destination j i
xij > for all i and j

27. Example: Zeron Shelving
The Northside and Southside facilities of Zeron Industries supply three firms (Zrox, Hewes, Rockrite) with customized shelving for its offices. They both order shelving from the same two manufacturers, Arnold Manufacturers and Supershelf, Inc.
Currently weekly demands by the users are 50 for Zrox, 60 for Hewes, and 40 for Rockrite. Both Arnold and Supershelf can supply at most 75 units to its customers.
Additional data is shown on the next slide.

28. Example: Zeron Shelving
Because of long standing contracts based on past orders, unit costs from the manufacturers to the suppliers are:
Zeron N Zeron S
Arnold
Supershelf
The costs to install the shelving at the various locations are:
Zrox Hewes Rockrite
Thomas
Washburn

29. Example: Zeron Shelving
Network Representation
ZROX
Zrox 50 1 5
Arnold
Zeron N 75
ARNOLD 5 8 8
Hewes 60
HEWES 3 7
Super
Shelf
Zeron S 4 75
WASH
BURN 4 4
Rock-
Rite 40

30. Example: Zeron Shelving
Linear Programming Formulation
Decision Variables Defined
xij = amount shipped from manufacturer i to supplier j
xjk = amount shipped from supplier j to customer k
where i = 1 (Arnold), 2 (Supershelf)
j = 3 (Zeron N), 4 (Zeron S)
k = 5 (Zrox), 6 (Hewes), 7 (Rockrite)
Objective Function Defined
Minimize Overall Shipping Costs:
Min 5x13 + 8x14 + 7x23 + 4x24 + 1x35 + 5x36 + 8x37
+ 3x45 + 4x46 + 4x47

31. Example: Zeron Shelving
Constraints Defined
Amount Out of Arnold: x13 + x14 < 75
Amount Out of Supershelf: x23 + x24 < 75
Amount Through Zeron N: x13 + x23 - x35 - x36 - x37 = 0
Amount Through Zeron S: x14 + x24 - x45 - x46 - x47 = 0
Amount Into Zrox: x35 + x45 = 50
Amount Into Hewes: x36 + x46 = 60
Amount Into Rockrite: x37 + x47 = 40
Non-negativity of Variables: xij > 0, for all i and j.

32. Example: Zeron Shelving
Optimal Solution (from The Management Scientist )
Objective Function Value =
Variable Value Reduced Costs X X X X X X X X X X

33. Example: Zeron Shelving
Optimal Solution
Zrox
ZROX 50 50 75 1 5
Arnold
Zeron N 75
ARNOLD 5 25 8 8
Hewes 60 35
HEWES 3 7 4
Super
Shelf
Zeron S 40 75
WASH
BURN 4 75 4
Rock-
Rite 40

34. Example: Zeron Shelving
Optimal Solution (continued)
Constraint Slack/Surplus Dual Prices

35. Example: Zeron Shelving
Optimal Solution (continued)
OBJECTIVE COEFFICIENT RANGES
Variable Lower Limit Current Value Upper Limit X
X No Limit
X No Limit
X No Limit
X No Limit X
X No Limit
X No Limit X
X No Limit

36. Example: Zeron Shelving
Optimal Solution (continued)
RIGHT HAND SIDE RANGES
Constraint Lower Limit Current Value Upper Limit
No Limit

37. End of Chapter 7, Part A