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Modelling with Max Flow

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Title: Slide 1 Author: bromille Last modified by: Dit brugernavn Created Date: 1/6/2005 10:52:38 AM Document presentation format: Sk rmshow (4:3) Company – PowerPoint PPT presentation

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Title: Modelling with Max Flow


1
Modelling with Max Flow
2
The Max Flow Problem
3
Modeling with Max FlowA scheduling problem
  • A set of jobs must be scheduled on M identical
    machines.
  • Each job j has an release (arrival) date rj, a
    required due date dj and a processing time pj
    dj - rj.
  • A job can be preemptively moved from one machine
    to another.
  • Can the jobs be scheduled on the machines so that
    the deadlines are met?

4
M 3
5

6
Basic property of model
  • Feasible (legal) schedules correspond to flows
    that saturate all outgoing arcs of s.
  • correspond to time spent on a particular job
    on a particular set of dates can be read off from
    flow along arcs in middle layer.

7
Integrality Theorem (26.11)
  • If a flow network has integer valued
    capacities, there is a maximum flow with an
    integer value on every edge. The Ford-Fulkerson
    method will yield such a maximum flow.
  • The integrality theorem is often extremely
    important when programming and modeling using
    the max flow formalism.

8
Reduction Maximum Matching ! Max Flow
What is the maximum cardinality matching in G?
9
G
10
s
t
G
All capacities are 1
11
Relating G and G
  • Matchings in G correspond exactly to integral
    flows of G
  • Correspondence
  • Arcs with a flow of 1 correspond to edges in the
    matching.
  • Arcs with a flow of 0 correspond to non-edges
  • A max flow which is integral correspond to a
    maximum matching

12
Integrality essential
13
Finding a balanced set of Representatives
(Ahuja, Application 6.2)
  • A city has clubs C1, C2,,Cn and parties P1,
    P2,,Pm. A citizen may be a member of several
    clubs but may only be a member of one party.
  • A balanced city council must be formed by
    including exactly one member from each club and
    at most uk members from party Pk.

14
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15
Max Flow Min Cut Theorem
  • The value of the maximum flow in G is equal to
    the capacity of the minimum cut in G.

16
Distributed Computation on Two-Processor Computer
(Ahuja, Application 6.5)
  • Processes p1, p2, , pn must be assigned to one
    of two processors.
  • Assigning pi to processor k gives computation
    cost aik.
  • If pi and pk are assigned to different
    processors, communication cost cik is incurred.
  • Minimize the total cost.

17
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18
but there is a lot of power of in modeling with
directed cuts
19
Königs theorem
  • The size of the largest matching in a bipartite
    graph is equal to the size of the smallest vertex
    cover.

20
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21
Find a subset of regions to mine so that the
total profit is maximized.
22
When solving exam problems
  • Flow networks is a graphical formalism. This does
    not mean that a sloppy drawing is sufficient to
    specify a model.
  • . remember that max flow networks are directed
    graphs.
  • .. remember that arcs in a max flow network have
    capacities that much be specified.
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