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Session 4a. Decision Models -- Prof. Juran2 Overview More Network Models –Assignment Model (Contract Bidding) “Big Cost” trick –Project Management (House.

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Presentation on theme: "Session 4a. Decision Models -- Prof. Juran2 Overview More Network Models –Assignment Model (Contract Bidding) “Big Cost” trick –Project Management (House."— Presentation transcript:

1 Session 4a

2 Decision Models -- Prof. Juran2 Overview More Network Models –Assignment Model (Contract Bidding) “Big Cost” trick –Project Management (House Building) More binary / integer tricks Critical Path / Slack Time Excel trick: Conditional Formatting Cost Crashing –Changing an objective to a constraint –Issues with Integers –Location Analysis (Hospital Location)

3 Decision Models -- Prof. Juran3 Contract Bidding Example A company is taking bids on four construction jobs. Three contractors have placed bids on the jobs. Their bids (in thousands of dollars) are given in the table below. (A dash indicates that the contractor did not bid on the given job.) Contractor 1 can do only one job, but contractors 2 and 3 can each do up to two jobs.

4 Decision Models -- Prof. Juran4 Formulation Decision Variables Which contractor gets which job(s). Objective Minimize the total cost of the four jobs. Constraints Contractor 1 can do no more than one job. Contractors 2 and 3 can do no more than two jobs each. Contractor 2 can’t do job 4. Contractor 3 can’t do job 1. Every job needs one contractor.

5 Decision Models -- Prof. Juran5 Formulation Decision Variables Define X ij to be a binary variable representing the assignment of contractor i to job j. If contractor i ends up doing job j, then X ij = 1. If contractor i does not end up with job j, then X ij = 0. Define C ij to be the cost; i.e. the amount bid by contractor i for job j. Objective Minimize Z =

6 Decision Models -- Prof. Juran6 Formulation Constraints for all j. for i = 1. for i = 2, 3.

7 Decision Models -- Prof. Juran7 Solution Methodology

8 Decision Models -- Prof. Juran8 Solution Methodology Notice the very large values in cells B4 and E3. These specific values (10,000) aren’t important; the main thing is to assign these particular contractor-job combinations costs so large that they will never be in any optimal solution.

9 Decision Models -- Prof. Juran9 Solution Methodology

10 Decision Models -- Prof. Juran10 Optimal Solution

11 Decision Models -- Prof. Juran11 Conclusions The optimal solution is to award Job 4 to Contractor 1, Jobs 1 and 3 to Contractor 2, and Job 2 to Contractor 3. The total cost is $182,000.

12 Decision Models -- Prof. Juran12 Sensitivity Analysis 1.What is the “cost” of restricting Contractor 1 to only one job? 2.How much more can Contractor 1 bid for Job 4 and still get the job?

13 Decision Models -- Prof. Juran13

14 Decision Models -- Prof. Juran14 Conclusions The sensitivity report indicates a shadow price of –2 (cell E29). (Allowing Contractor 1 to perform one additional job would reduce the total cost by 2,000.) The allowable increase in the bid for Job 4 by Contractor 1 is 3. (He could have bid any amount up to $43,000 and still have won that job.)

15 Decision Models -- Prof. Juran15 Con. 3Con. 2Con. 1 Network Representation Job 3Job 2Job 4Job 1

16 Decision Models -- Prof. Juran16 Optimal Solution Con. 1 Job 3 Con. 2Con. 3 Job 2Job 4Job

17 Decision Models -- Prof. Juran17 House-Building Example

18 Decision Models -- Prof. Juran18 Managerial Problem Definition Find the critical path and the minimum number of days needed to build the house.

19 Decision Models -- Prof. Juran19 Network Representation 1 0 Start 43 2 A5A5 B8B8 C 10 E4E4 D5D5 G3G3 F6F6 5 End

20 Decision Models -- Prof. Juran20 Formulation Decision Variables We are trying to decide when to begin and end each of the activities. Objective Minimize the total time to complete the project. Constraints Each activity has a fixed duration. There are precedence relationships among the activities. We cannot go backwards in time.

21 Decision Models -- Prof. Juran21 Formulation Decision Variables Define the nodes to be discrete events. In other words, they occur at one exact point in time. Our decision variables will be these points in time. Define t i to be the time at which node i occurs, and at which time all activities preceding node i have been completed. Define t 0 to be zero. Objective Minimize t 5.

22 Decision Models -- Prof. Juran22 Formulation Constraints There is really one basic type of constraint. For each activity x, let the time of its starting node be represented by t jx and the time of its ending node be represented by t kx. Let the duration of activity x be represented as d x. For every activity x, For every node i,

23 Decision Models -- Prof. Juran23 Solution Methodology

24 Decision Models -- Prof. Juran24 Solution Methodology The matrix of zeros, ones, and negative ones (B12:G18) is a means for setting up the constraints. The sumproduct functions in H12:H18 calculate the elapsed time between relevant pairs of nodes, corresponding to the various activities. The duration times of the activities are in J12:J18.

25 Decision Models -- Prof. Juran25

26 Decision Models -- Prof. Juran26 Optimal Solution

27 Decision Models -- Prof. Juran27 Conclusions The project will take 26 days to complete. The only activity that is not critical is the electrical wiring.

28 Decision Models -- Prof. Juran28 CPM Jargon Any activity for which is said to have slack time, the amount of time by which that activity could be delayed without affecting the overall completion time of the whole project. In this example, only activity D has any slack time (13 – 5 = 8 units of slack time).

29 Decision Models -- Prof. Juran29 CPM Jargon Any activity x for which is defined to be a “critical” activity, with zero slack time.

30 Decision Models -- Prof. Juran30 CPM Jargon Every network of this type has at least one critical path, consisting of a set of critical activities. In this example, there are two critical paths: A-B-C-G and A-B-E-F-G. 1 0 Star t 43 2 A5A5 B8B8 C 10 E4E4 D5D5 G3G3 F6F6 5 End

31 Decision Models -- Prof. Juran31 Excel Tricks: Conditional Formatting

32 Decision Models -- Prof. Juran32 Critical Activities: Using the Solver Answer Report

33 Decision Models -- Prof. Juran33 House-Building Example, Continued Suppose that by hiring additional workers, the duration of each activity can be reduced. Use LP to find the strategy that minimizes the cost of completing the project within 20 days.

34 Decision Models -- Prof. Juran34 Crashing Parameters

35 Decision Models -- Prof. Juran35 Managerial Problem Definition Find a way to build the house in 20 days.

36 Decision Models -- Prof. Juran36 Formulation Decision Variables Now the problem is not only when to schedule the activities, but also which activities to accelerate. (In CPM jargon, accelerating an activity at an additional cost is called “crashing”.) Objective Minimize the total cost of crashing.

37 Decision Models -- Prof. Juran37 Formulation Constraints The project must be finished in 20 days. Each activity has a maximum amount of crash time. Each activity has a “basic” duration. (These durations were considered to have been fixed in Part a; now they can be reduced.) There are precedence relationships among the activities. We cannot go backwards in time.

38 Decision Models -- Prof. Juran38 Formulation Decision Variables Define the number of days that activity x is crashed to be R x. For each activity there is a maximum number of crash days R max, x Define the crash cost per day for activity x to be C x Objective Minimize Z =

39 Decision Models -- Prof. Juran39 Formulation Constraints For every activity x, For every node i,

40 Decision Models -- Prof. Juran40 Solution Methodology

41 Decision Models -- Prof. Juran41 Solution Methodology G3 now contains a formula to calculate the total crash cost. The new decision variables (how long to crash each activity x, represented by R x ) are in M12:M18. G8 contains the required completion time, and we will constrain the value in G6 to be less than or equal to G8. The range J12:J18 calculates the revised duration of each activity, taking into account how much time is saved by crashing.

42 Decision Models -- Prof. Juran42 Solution Methodology

43 Decision Models -- Prof. Juran43 Optimal Solution

44 Decision Models -- Prof. Juran44 Conclusions It is feasible to complete the project in 20 days, at a cost of $ Star t 43 2 A3A3 B5B5 C9C9 E3E3 D5D5 G3G3 F6F6 5 End

45 Decision Models -- Prof. Juran45 Conclusions

46 Decision Models -- Prof. Juran46 An Alternative Solution

47 Decision Models -- Prof. Juran47 Excel Tricks: VLOOKUP Looks for a specific value in the left column of a table and finds the row where that value appears, then returns the value corresponding to another specified column in that row.

48 Decision Models -- Prof. Juran48

49 Decision Models -- Prof. Juran49 Integer Programs We have seen models where the decision variables had to be integers, but we didn’t have to impose a Solver constraint to make that happen (a special attribute of some transportation models) In general, you do need to impose a Solver constraint to force an integer solution No more Simplex algorithm! Binary = Special case of integer

50 Decision Models -- Prof. Juran50 Difficulties with Integer Programs Linear approximations are not necessarily feasible or optimal Integer optimal solutions require much more complicated algorithms Integer algorithms do not yield a sensitivity report

51 Decision Models -- Prof. Juran51

52 Decision Models -- Prof. Juran52 Optimal linear-programming solution: X = 1.857, Y = 0 Rounded to X = 2, Y = 0 is infeasible Rounded to X = 1, Y = 0 is not optimal Optimal integer-programming solution: X = 0, Y = 3

53 Decision Models -- Prof. Juran53 Hospital Location Example A county is going to build two hospitals. There are nine cities in which the hospitals can be built. The number of hospital visits per year made by people in each city and the x-y coordinates of each city are listed in the table below. The county's goal is to minimize the total distance that patients must travel to hospitals. Where should it locate the hospitals?

54 Decision Models -- Prof. Juran54 Hospital Location Example

55 Decision Models -- Prof. Juran55 Hospital Location Example

56 Decision Models -- Prof. Juran56 Managerial Problem Definition Decision Variables We need to decide whether or not to build a hospital in each of nine cities. We also need to decide how many visits there will be from each city to each hospital. Objective We want to minimize the total distance traveled to the hospital by all county residents.

57 Decision Models -- Prof. Juran57 Managerial Problem Definition Constraints The cities can’t be moved. Exactly two hospitals will be built. All of the planned hospital visits must be accounted for and included in the total distance calculation. No hospital visits are allowed to a city that has no hospital.

58 Decision Models -- Prof. Juran58 Network Representation

59 Decision Models -- Prof. Juran59 Network Representation One Hospital

60 Decision Models -- Prof. Juran60 Formulation

61 Decision Models -- Prof. Juran61 Formulation

62 Decision Models -- Prof. Juran62 Solution Methodology

63 Decision Models -- Prof. Juran63 Solution Methodology The 9 Xj decision variables are in the range C3:K3. The 81 Vij decision variables are in the range C7:K15. The objective function is in cell P3. The matrix in the range N18:V26 calculates the distance between each pair of cities using a long and ugly Excel function based on the famous Pythagorean theorem. Cell N3 is used to keep track of constraint (1). Cells L7:L15 and N7:N15 are used to keep track of constraint (2). Cells C16:K16 and C18:K18 are used to keep track of constraint (3).

64 Decision Models -- Prof. Juran64

65 Decision Models -- Prof. Juran65 Optimal Solution

66 Decision Models -- Prof. Juran66 Optimal Solution If the county wants to build two hospitals, then the optimal locations are in City 4 (Lowthersburg) and City 8 (Rothsboro). The total miles traveled would be 132,500.

67 Decision Models -- Prof. Juran67 Network Representation Two Hospitals

68 Decision Models -- Prof. Juran68 Network Representation Three Hospitals

69 Decision Models -- Prof. Juran69 Network Representation Four Hospitals

70 Decision Models -- Prof. Juran70 Network Representation Five Hospitals

71 Decision Models -- Prof. Juran71 Network Representation Six Hospitals

72 Decision Models -- Prof. Juran72 Network Representation Seven Hospitals

73 Decision Models -- Prof. Juran73 Network Representation Eight Hospitals

74 Decision Models -- Prof. Juran74 Network Representation Nine Hospitals

75 Decision Models -- Prof. Juran75 Summary More Network Models –Assignment Model (Contract Bidding) “Big Cost” trick –Project Management (House Building) More binary / integer tricks Critical Path / Slack Time Excel trick: Conditional Formatting Cost Crashing –Changing an objective to a constraint –Issues with Integers –Location Analysis (Hospital Location)


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