1. Integer Programming Integer programming is a solution method for many discrete optimization problems Programming = Planning in this context Origins go back to military logistics in WWII (1940s). In a survey of Fortune 500 firms, 85% of those responding said that they had used linear or integer programming. Why is it so popular? –Many different real-life situations can be modeled as integer programs (IPs). –There are efficient algorithms to solve IPs.

2. Standard form of integer program (IP) maximize c 1 x 1 +c 2 x 2 +…+c n x n (objective function) subject to a 11 x 1 +a 12 x 2 +…+a 1n x n  b 1 (functional constraints) a 21 x 1 +a 22 x 2 +…+a 2n x n  b 2 …. a m1 x 1 +a m2 x 2 +…+a mn x n  b m x 1, x 2, …, x n  Z + (set constraints) Note: Can also have equality or ≥ constraint in non-standard form.

3. Example of Integer Program (Production Planning-Furniture Manufacturer) Technological data: Production of 1 table requires 5 ft pine, 2 ft oak, 3 hrs labor 1 chair requires 1 ft pine, 3 ft oak, 2 hrs labor 1 desk requires 9 ft pine, 4 ft oak, 5 hrs labor Capacities for 1 week: 1500 ft pine, 1000 ft oak, 20 employees (each works 40 hrs). Market data: Goal: Find a production schedule for 1 week that will maximize the profit. profitdemand table$12/unit40 chair$5/unit130 desk$15/unit30

4. Production Planning-Furniture Manufacturer: modeling the problem as integer program The goal can be achieved by making appropriate decisions. First define decision variables: Let x t be the number of tables to be produced; x c be the number of chairs to be produced; x d be the number of desks to be produced.

5. Production Planning-Furniture Manufacturer: modeling the problem as integer program  Objective is to maximize profit: max 12x t + 5x c + 15x d  Functional Constraints capacity constraints: pine: 5x t + 1x c + 9x d  1500 oak: 2x t + 3x c + 4x d  1000 labor: 3x t + 2x c + 5x d  800 market demand constraints: tables: x t ≥ 40 chairs:x c ≥ 130 desks:x d ≥ 30  Set Constraints x t, x c, x d  Z +

6. Solutions to integer programs A solution is an assignment of values to variables. A feasible solution is an assignment of values to variables such that all the constraints are satisfied. The objective function value of a solution is obtained by evaluating the objective function at the given point. An optimal solution (assuming maximization) is one whose objective function value is greater than or equal to that of all other feasible solutions. There are efficient algorithms for finding the optimal solutions of an integer program.

7. Next: IP modeling techniques Modeling techniques:  Using binary variables  Restrictions on number of options  Contingent decisions  Variables with k possible values Applications:  Facility Location Problem  Knapsack Problem

8. Example of IP: Facility Location A company is thinking about building new facilities in LA and SF. Relevant data: Total capital available for investment: $10M Question: Which facilities should be built to maximize the total profit? capital neededexpected profit 1. factory in LA $6M$9M 2. factory in SF $3M$5M 3. warehouse in LA $5M$6M 4. warehouse in SF $2M$4M

9. Example of IP: Facility Location Define decision variables ( i = 1, 2, 3, 4): Then the total expected benefit: 9x 1 +5x 2 +6x 3 +4x 4 the total capital needed: 6x 1 +3x 2 +5x 3 +2x 4  Summarizing, the IP model is: max 9x 1 +5x 2 +6x 3 +4x 4 s.t. 6x 1 +3x 2 +5x 3 +2x 4  10 x 1, x 2, x 3, x 4 binary ( i.e., x i  {0,1} )

10. The Facility Location Problem: adding new requirements Extra requirement: build at most one of the two warehouses. The corresponding constraint is: x 3 +x 4  1 Extra requirement: build at least one of the two factories. The corresponding constraint is: x 1 +x 2 ≥ 1

11. Modeling Technique: Restrictions on the number of options Suppose in a certain problem, n different options are considered. For i =1,…,n Restrictions: At least p and at most q of the options can be chosen. The corresponding constraints are:

12. Modeling Technique: Contingent Decisions Back to the facility location problem. Requirement: Can’t build a warehouse unless there is a factory in the city. The corresponding constraints are: x 3  x 1 (LA) x 4  x 2 (SF) Requirement: Can’t select option 3 unless at least one of options 1 and 2 is selected. The constraint:x 3  x 1 + x 2 Requirement: Can’t select option 4 unless at least two of options 1, 2 and 3 are selected. The constraint:2x 4  x 1 + x 2 + x 3

13. More on Integer Programming and other discrete optimization problems and techniques: Math 4620 Linear and Nonlinear Programming Math 4630 Discrete Modeling and Optimization