ISE480 Sequencing and Scheduling

1. ISE480 Sequencing and Scheduling Izmir University of Economics ISE480 2011 -2012 Fall Semestre

2. Branch and Bound ISE480 Sequencing and Scheduling 2012 – 2013 Fall semestre

3. Branch and Bound • Enumerative method • Guarantees finding the best schedule • Basic idea: • Look at a set of schedules • Develop a bound on the performance • Discard (fathom) if bound worse than best schedule found before (incumbent) ISE480 Sequencing and Scheduling

4. OK, now what? • So we’ve got a IP formulation of the problem, how do we solve it? • Using standard IP solution techniques such as branch-and-bound • Doesn’t mean the problem is easy • Will now talk about branch-and-bound (B&B) which can be used to solve IPs and other hard problems ISE480 Sequencing and Scheduling

5. Branch-and-Bound • Idea • Systematically search through possible variable values • Use heuristics to pick a decision to try (“branch”) • Use lower bounds on solutions to “bound” the search (fathom) • Creates a search tree ISE480 Sequencing and Scheduling

6. Branch a = 0 a = 1 b = 0 b = 1 b = 0 b = 1 c = 0 c = 1 c = 0 c = 1 c = 0 c = 1 c = 0 c = 1 B&B Search Tree: Branching • Imagine a problem with 3 variables • a, b, c є{0, 1} 100 90 110 115 80 90 100 110 ISE480 Sequencing and Scheduling

7. a = 0 a = 1 b = 0 b = 1 b = 0 c = 0 c = 1 c = 0 Bound B&B Search Tree: Bounding • Imagine I have a way to calculate a lower bound on the cost at each node 50 70 80 85 95 80 100 90 80 ISE480 Sequencing and Scheduling

8. B&B • Branch: assign a heuristic value to a variable • Creates two subproblems • Bound: compare lower bound at node with best known solution • If LB > best, you can backtrack right away ISE480 Sequencing and Scheduling

9. B&B for IP • Usually lower bound is found by solving the linear relaxation of the IP • LP formed by ignoring integral constraints • Branch on one of the integer variables with a non-integer value to be: • greater than or equal to the next highest integer, or • Less than or equal to the next lowest integer ISE480 Sequencing and Scheduling

10. Classic Result • The EDD rule is optimal for • If jobs have different release dates, which we denote then the problem is NP-Hard • What makes it so much more difficult? ISE480 Sequencing and Scheduling

11. 1 2 3 0 5 10 15 Can we improve? ISE480 Sequencing and Scheduling

12. Delay Schedule EDD Add a delay 1 3 2 0 5 10 15 What makes this problem hard is thatthe optimal schedule is not necessarilya non-delay schedule ISE480 Sequencing and Scheduling

13. Final Classic Result • The preemptive EDD rule is optimal for the preemptive (prmp) version of the problem • Note that in the previous example, the preemptive EDD rule gives us the optimal schedule • Note that if rj = 0, then the above does not hold. ISE480 Sequencing and Scheduling

14. Branch and Bound • The problem cannot be solved using a simple dispatching rule so we will try to solve it using branch and bound • To develop a branch and bound procedure: • Determine how to branch • Determine how to bound ISE480 Sequencing and Scheduling

15. Data ISE480 Sequencing and Scheduling

16. Branch and bound • Enumeration in asearchtree • each node is a partial solution, i.e. a part of the solution space Level 0 root node Level 1 (n nodes) child nodes ... Level 2 (n*(n-1)) child nodes ... ISE480 Sequencing and Scheduling

17. Branching (•,•,•,•) (1,•,•,•) (2,•,•,•) (3,•,•,•) (4,•,•,•) ISE480 Sequencing and Scheduling

18. Branching (•,•,•,•) (1,•,•,•) (2,•,•,•) (3,•,•,•) (4,•,•,•) Discard immediately because ISE480 Sequencing and Scheduling

19. Branching (•,•,•,•) (1,•,•,•) (2,•,•,•) (3,•,•,•) (4,•,•,•) Need to develop lower bounds on these nodes and do further branching. ISE480 Sequencing and Scheduling

20. Bounding (in general) • Typical way to develop bounds is to relax the original problem to an easily solvable problem • Three cases: • If there is no solution to the relaxed problem there is no solution to the original problem • If the optimal solution to the relaxed problem is feasible for the original problem then it is also optimal for the original problem • If the optimal solution to the relaxed problem is not feasible for the original problem it provides a bound on its performance ISE480 Sequencing and Scheduling

21. Relaxing the Problem • The problem is a relaxation to the problem • Not allowing preemption is a constraint in the original problem but not the relaxed problem • We know how to solve the relaxed problem (preemptive EDD rule) ISE480 Sequencing and Scheduling

22. Bounding • Preemptive EDD rule optimal for the preemptive version of the problem • Thus, solution obtained is a lower bound on the maximum delay • If preemptive EDD results in a non-preemptive schedule all nodes with higher lower bounds can be discarded. ISE480 Sequencing and Scheduling

23. Lower Bounds • Start with (1,•,•,•): • Job with EDD is Job 4 but • Second earliest due date is for Job 3 Job 1 Job 2 Job 3 Job 4 0 10 20 ISE480 Sequencing and Scheduling

24. Branching (•,•,•,•) (1,•,•,•) (2,•,•,•) (3,•,•,•) (4,•,•,•) (1,2,•,•) (1,3,•,•) (1,3,4,2) ISE480 Sequencing and Scheduling