2. 1 Network Models Chapter 4

3. 2 For a given network find the path of minimum distance, time, or cost from a starting point, the start node, to a destination, the terminal node. Problem definition –There are n nodes, beginning with start node 1 and ending with terminal node n. –Bi-directional arcs connect connected nodes i and j with nonnegative distances, d ij –Find the path of minimum total distance that connects node 1 to node n. 4.5 The Shortest Path Model

4. 3 Determine the shortest route from Seattle to El Paso over the following network of highways. FAIRWAY VAN LINES

5. 4 8 9 7 11 12 1 43 6 2 10 5 Salt Lake City El Paso Seattle Boise Portland Butte Cheyenne BakersfieldLas Vegas Albuquerque Tucson Phoenix 599 691 497 180 432 345 440 554 621 420 280 432 403 314 893 500 290 116 268 577 Comment In case some arcs are bi-directional, create two directed arcs in two opposite directions, between the same two nodes.

6. 5 Decision variables Objective = Minimize  d ij X ij FAIRWAY VAN LINES – The Linear Programming Model

7. 6 6 2 Salt Lake City 1 34 Seattle Boise Portland 599 497 180 432 345 Butte [The number of highways traveled out of Seattle (the start node)] = 1 X 12 + X 13 + X 14 = 1 In a similar manner: [The number of highways traveled into El Paso (terminal node)] = 1 X 9,12 + X 10,12 + X 11,12 = 1 [The number of highways used to travel into a city] = [The number of highways traveled leaving the city]. For example, in Boise (node 4): X 14 + X 34 = X 47. Subject to the following constraints: Non-negativity constraints

8. 7 FAIRWAY VAN LINES – spreadsheet

9. 8 The Dijkstra’s algorithm: –Find the shortest distance from the “START” to each other node, in the order of the closet nodes to the “START”. –Once the shortest route to the m closest node is determined, the (m+1) closest can be easily determined. –This algorithm finds the shortest route from the start to all the nodes in the network. FAIRWAY VAN LINES – The Network Model

10. 9 An illustration of the Dijkstra’s algorithm (See mathematical formulation of the algorithm in Supplement CD 5).

11. 10 SEAT. BUT 599 POR 180 497 BOI 599 180 497 POR. BOI 432 BAK 893 + + = = 612 1073 BOI BOISE. 345 SLC + = 842 BUTTE SLC 420 CHY. 691 + + = = 1119 1290 SLC. SLC … and so on until the whole network is covered. SLC.

12. 11 Dijkstra’s algorithm - continued When all the network is covered, the shortest route from START to every other node can be identified. Trace the path that leads to each node by backtracking from each node toward node START.

13. 12 Problem definition –There is a source node (labeled 1), from which the network flow emanates. –There is a terminal node (labeled n), into which all network flow is eventually deposited. –There are n - 2 intermediate nodes (labeled 2, 3,…,n-1), where the node inflow is equal to the node outflow. –There are capacities C ij for flow on the arc from node i to node j, and capacities C ji for the opposite direction. 4.6 The Maximal Flow Problem

14. 13 The objective is to find the maximum total flow out of node 1 that can flow into node n without exceeding the capacities on the arcs. The Maximal Flow Problem Objective

15. 14 United Chemical produces pesticides and lawn care products. Poisonous chemicals needed for the production process are held in a huge drum. A network of pipes and valves regulates the chemical flow from the drum to different production areas. The safety division must plan a procedure to empty the drum as fast as possible into a safety tub in the disposal area, using a network of pipes and valves. UNITED CHEMICAL COMPANY

16. 15 The plan must determine: which valves to open and shut What is the estimated time for total discharge UNITED CHEMICAL COMPANY

17. 16 UNITED CHEMICAL COMPANY- Network Presentation

18. 17 Data 1 7 2 4 6 5 3 Chemical Drum Safe Tub 10 8 6 1 12 1 4 4 2 2 8 3 3 7 2 Maximum flow from 2 to 4 is 8 Maximum flow from 6 to 3 is 4

19. 18 Decision variables X ij - the flow from node i to node j on the arc that connects these two nodes Objective function – Maximize the flow out of node 1: Max X 12 + X 13 UNITED CHEMICAL COMPANY – The Linear Programming Model

20. 19 Constraints –The constraint on each intermediate node is: Flow out from the node – flow into the node = 0 Node 2: X23 +X24 + X26 - X12 - X32= 0 Node 3: X32 +X35 + X36 - X13 - X23 - X63 = 0 Node 4: X46 + X47 - X24 - X64 = 0 Node 5: X56 + X57 - X35 - X65 = 0 Node 6: X63 +X64 +X65 + X67 - X26 - X36 - X46 -X56= 0 UNITED CHEMICAL COMPANY – The Linear Programming Model 2

21. 20 Constraints – continued –Flow cannot exceed arc capacities X12  10; X13  10; X23  1; X24  8; X26  6; X32  1; X35  15; X36  4; X46  3; X47  7; X56  2; X57  8; X63  4; X64  3; X65  2; X67  2; –All X ij  0 UNITED CHEMICAL COMPANY – The Linear Programming Model

22. 21 UNITED CHEMICAL COMPANY – Spreadsheet 8 2 7 9 8 7 8 1 7 4 2 5 3 6 2

23. 22 1 2 3 7 10 7 2 8 2 7 7 5 6 4 3 3 2 8 6 2 – The value of the maximum flow = the sum of the capacities of the minimum cut. The role of cuts in a maximum flow network This is not a minimal cut This is a minimal cut, and the flow is maximal

24. 23 4.7 The Traveling Salesman Problem Problem definition –There are m nodes. –Unit cost C ij is associated with utilizing arc (i,j) –Find the cycle that minimizes the total cost required to visit all the nodes exactly once.

25. 24 The Traveling Salesman Problem Importance: –Variety of scheduling application can be solved as a traveling salesmen problem. –Examples : Ordering drill position on a drill press. School bus routing. Military bombing sorties. –The problem has theoretical importance because it represents a class of difficult problems known as NP-hard problems.

26. 25 The Traveling Salesman Problem Complexity Both writing the mathematical model and solving it are cumbersome (a problem with 20 cities requires over 500,000 linear constraints.)

27. 26 THE FEDERAL EMERGENCY MANAGEMENT AGENCY A visit must be made to four local offices of FEMA, going out from and returning to the same main office in Northridge, Southern California. Data Travel time between offices (minutes)

28. 27 FEMA traveling salesman Network representation

29. 28 30 25 40 35 80 65 45 50 40 Home 1 2 3 4

30. 29 FEMA - Traveling Salesman Solution approaches –Enumeration of all possible cycles. This results in (m-1)! cycles to enumerate. Only small problems can be solved with this approach. –A combination of the Assignment problem and the Branch and Bound technique. Problem with up to m=20 nodes can be efficiently solved with this approach.

31. 30 Possible cycles CycleTotal Cost 1. H-O1-O2-O3-O4-H210 2. H-O1-O2-O4-O3-H 195 3. H-O1-O3-O2-O3-H 240 4. H-O1-O3-O4-O2-H 200 5. H-O1-O4-O2-O3-H 225 6. H-O1-O4-O3-O2-H 200 7. H-O2-O3-O1-O4-H 265 8. H-O2-O1-O3-O4-H 235 9. H-O2-O4-O1-O3-H 250 10. H-O2-O1-O4-O3-H 220 11. H-O3-O1-O2-O4-H 260 12. H-O3-O1-O2-O4-H260 Minimum For this problem we have (5-1)! / 2 = 12 cycles. Symmetrical problems need to enumerate only (m-1)! / 2 cycles. FEMA – full enumeration

32. 31 30 25 40 35 80 65 45 50 40 Home 1 2 3 4 FEMA – optimal solution

33. 32 Refer to the nodes designated “From” as “Workers” nodes. Refer to nodes designated by “To” as “Jobs” nodes. Assign “Workers” to “Jobs” at minimum cost. FEMA – The Assignment problem approach

34. 33 Data FEMA – The Assignment problem approach

35. 34 FEMA – The assignment solution 30 25 40 35 80 65 45 50 40 Home 1 2 3 4 O3 – O4 – O3 Sub-tour O1 – O2 – H – O1 Sub-tour

36. 35 We can prevent the situation of sub-tours by adding certain constraints. This makes the problem extremely large. Another approach, that combines the Assignment model with the Branch and Bound solution methodology can efficiently solve problems up to m = 20 nodes. FEMA – The assignment solution

37. 36 4.8 The Minimal Spanning Tree This problem arises when all the nodes of a given network must be connected to one another, without any loop. The minimal spanning tree approach is appropriate for problems for which redundancy is expensive, or the flow along the arcs is considered instantaneous.

38. 37 THE METROPOLITAN TRANSIT DISTRICT

39. 38 THE METROPOLITAN TRANSIT DISTRICT The City of Vancouver is planning the development of a new light rail transportation system. The system should link 8 residential and commercial centers. The Metropolitan Transit District needs to select the set of lines that will connect all the centers at a minimum total cost.

40. 39 METROPOLITAN TRANSIT – Network presentation The network describes: – feasible lines that have been drafted – minimum possible cost for taxpayers per line.

41. 40 5 26 4 7 8 1 3 West Side North Side University Business District East Side Shopping Center South Side City Center 33 50 30 55 34 28 32 35 39 45 38 43 44 41 37 36 40

42. 41 SPANNING TREE NETWORK PRESENTATION 5 26 4 7 8 1 3 West Side North Side University Business District East Side Shopping Center South Side City Center 33 50 30 55 34 28 32 35 39 45 38 43 44 41 37 36 40

43. 42 Solution - a network approach (See Supplement CD 5) The algorithm that solves this problem is a very trivial greedy procedure. Two versions of the algorithm are described. The algorithm – version 1: –Start by selecting the smallest arc, and adding it to a set of selected arcs (currently contains only the first arc). –At each iteration, add the next smallest arc to the set of selected arcs, unless it forms a cycle. –Finish when all nodes are connected. THE METROPOLITAN TRANSIT DISTRICT

44. 43 The algorithm – version 2: –Start by selecting the smallest arc creating the set of connected arcs. –At each iteration add the smallest unselected arc that has a connection to the connected set, but do not create a cycle. –Finish when all nodes are connected See demonstration of version 2 next THE METROPOLITAN TRANSIT DISTRICT

45. 44 Shopping Center Loop 26 4 7 8 West Side North Side University Business District East Side South Side City Center 50 30 55 34 28 32 35 39 45 38 43 44 41 37 36 40 Total Cost = $236 million OPTIMAL SOLUTION NETWORK REPRESENTATION 5 Loop 1 3 33 28 32 30 33

46. 45 Shopping Center 4 7 8 West Side North Side University Business District East Side South Side City Center 50 30 55 34 28 32 35 39 45 38 43 44 41 37 36 40 Total Cost = $236 million OPTIMAL SOLUTION NETWORK REPRESENTATION 5 3 33 Loop 1 62

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