Upload
arthur-harris
View
32
Download
0
Tags:
Embed Size (px)
DESCRIPTION
MIPing the Probabilistic Integer Programming Problem. Anureet Saxena ACO PhD Student, Tepper School of Business, Carnegie Mellon University. (Joint Work with Vineet Goyal and Miguel Lejuene). Why Probabilistic Programming?. Fixed Cost. Transportation Cost. Demand Constraints. - PowerPoint PPT Presentation
Citation preview
MIPing the Probabilistic Integer MIPing the Probabilistic Integer Programming ProblemProgramming Problem
Anureet SaxenaACO PhD Student,
Tepper School of Business,
Carnegie Mellon University.
(Joint Work with Vineet Goyal and Miguel Lejuene)
Why Probabilistic Programming?Why Probabilistic Programming?
Set of Customers
Demand Constraints
Capacity Constraints
Transportation Cost
Set of Facilities
Fixed Cost
Why Probabilistic Programming?Why Probabilistic Programming?
Set of CustomersSet of Customers
Demand ConstraintsDemand Constraints
Capacity ConstraintsCapacity Constraints
Transportation CostTransportation Cost
Set of FacilitiesSet of Facilities
Fixed CostFixed Cost
Uncertain Future• Population Shift• Evolution of Market Trends• Ford opens a manufacturing unit• Google closes its R&D center
Why Probabilistic Programming?Why Probabilistic Programming?
A random 0/1 vector which incorporates the uncertain future into the optimization model
Why Probabilistic Programming?Why Probabilistic Programming?
Probabilistic Constraint
Reliability Level
Probabilistic MIP ModelProbabilistic MIP Model
Deterministic Probabilistic
Random 0/1 Vector(Joint Distribution)
Reliability Level
Why Probabilistic Programming?Why Probabilistic Programming?
• Facility Location– Strategic Planning– Population shift– Evolution of market trends– Demographic Changes
• Contingency Service– Minimum Reliability Principle
• Production Design and Manufacturing– Uncertain Demand– Lot Sizing and Inventory Problems
Must Read!
Strategic facility locationby Owen and Daskin
A Simple AlgorithmA Simple AlgorithmRandom 0/1 Vector(Joint Distribution)
Reliability Level
1. Enumerate all possible 0/1 realizations of .
2. For each 0/1 realization whose cdf is greater than or equal to p, solve the deterministic problem
Prekopa, Beraldi, Ruszczynski ApproachPrekopa, Beraldi, Ruszczynski Approach
Prekopa, Beraldi, Ruszczynski ApproachPrekopa, Beraldi, Ruszczynski Approach
000
100 010 001
011101110
111
Prekopa, Beraldi, Ruszczynski ApproachPrekopa, Beraldi, Ruszczynski Approach
p-efficient frontier
2-Phase Algorithm2-Phase Algorithm
Enumeration of p-efficient points
Solving a Deterministic Problem for each p-efficient point
2-Phase Algorithm2-Phase Algorithm
Enumeration of p-efficient points
Solving a Deterministic Problem for each p-efficient point
Independent
Beraldi & Ruszczynski ApproachBeraldi & Ruszczynski Approach
scp41
scp42
Explosive GrowthIn computation
time
2-Phase Algorithm2-Phase Algorithm
Enumeration of p-efficient points
Solving a Deterministic Problem for each p-efficient point
Pitfall
Our ApproachOur Approach
Enumeration of p-efficient points
Solving a Deterministic Problem for each p-efficient point
Integrate the 2-phases
Our ApproachOur Approach
Enumeration of p-efficient points
Solving a Deterministic Problem for each p-efficient point
Integrate the 2-phases
Independent
Our ModelOur Model
Log of cumulative probability of block t
Non-Linear MIPing
Our ModelOur Model
Log of cumulative probability of block t
Our ModelOur Model
Log of cumulative probability of block t
Beraldi & Ruszczynski Approach: Beraldi & Ruszczynski Approach: ComparisonComparison
scp41
scp42
All instances solved in less than 1sec by
CPLEX 9.0. CPLEX enumerated less than
50 nodes solving most instances at the root
node
Key ObservationsKey Observations
• Models any arbitrary distribution• Exponential number of constraints for each block • Linear in the input size for generic distribution• Encodes the enumeration phase as a Mixed Integer
Program• Allows us to exploit state-of-art MIP solvers to perform
intelligent enumeration.
Key ObservationsKey Observations
• Models any Models any arbitraryarbitrary distribution distribution• Exponential number of constraints for each block Exponential number of constraints for each block • Linear in the input size for generic distributionLinear in the input size for generic distribution• Encodes the enumeration phase as a Mixed Integer Encodes the enumeration phase as a Mixed Integer
ProgramProgram• Allows us to exploit state-of-art MIP solvers to perform Allows us to exploit state-of-art MIP solvers to perform
intelligent enumeration.intelligent enumeration.
Research Question
The model has an exponential number of constraints for each block. Is there a way to reduce the number of constraints?
The Answer is YesThe Answer is Yes
p-Inefficient Frontierp-Inefficient Frontier
Refined FormulationRefined Formulation
Add t constraints only for lattice
points above the frontier
Set-Covering Constraint for maximally p-
inefficient points
Refined FormulationRefined FormulationBlock Size10
A Tough Instance - A Tough Instance - p31p31
• SSCFLP instance from the Holmberg test-bed• 30 facilities and 150 customers• Deterministic instance can be solved in 80 sec.• Probabilistic instance has 15 blocks of size 10 each• CPLEX was unable to solve the probabilistic instance
within 2 hours!!
A Tough Instance - A Tough Instance - p31p31
A Tough Instance - A Tough Instance - p31p31
Research Question
Why is this instance so difficult to solve?
AnswerAnswer
Big-M Constraints
Polarity CutsPolarity Cuts
Big-M Constraints model P
Facets of P can strengthen the model
Polarity CutsPolarity Cuts
• We know all the extreme points and extreme rays of P
• Compact description of polar
• Facets of P can be found by solving the linear program derived from the polar
• The linear program has lot more rows than columns – dual simplex algorithm.
A Tough Instance - A Tough Instance - p31p31
Tough Instance Solved
• % Gap closed at Root Node 67.84%• Time Spent in Strengthening 0.83 sec• Time Spent in Solving Separation LP 0.30 sec• Time Taken by CPLEX 9.0 after Strengthening 51.65 sec • No. of Branch-and-Bound enumerated by CPLEX 9.0 2300• Total time taken to solve the instance to optimality 53.04 sec
Computational ResultsComputational Results
• Implementation– COIN-OR Modules– CPLEX 9.0
• Selection Criterion– ORLIB & Holmberg Instances– Instances which can be solved in 1hr
• Computational Power– P4 Processor– 2GB RAM
• Library of Instances – PCPLIB
Test BedTest BedProblem Set Number of Instances # Rows # Columns
OrLib Set Covering 60 50-500 500-5000OrLib Warehouse Location (Cap) 37 66-100 816-2550OrLib p-Median (Cap) 20 101-201 2550-10100Holmberg Facility Location (Cap) 70 60-230 510-6030
• 2 Distributions – as in BR [2002]• 4 Reliability levels – 0.80, 0.85, 0.90, 0.95• 2 Block Sizes – 5, 10 • Total Number of Instances per Deterministic Instance = 16
Computational ResultsComputational Results
Deterministic Problem
Number of Probabilistic Instances
Number of Unsolved Instances
% Relative Gap (Unsolved Instances)
Set Covering 1440 37 11.69CWLP 888 0 -Cap k-Median 480 0 -SSCFLP 1680 22 0.45
Computational ResultsComputational Results
Deterministic Problem
Solution Time (sec)
Number of Branch-and-Bound Nodes
Set Covering 160.81 7440CWLP 0.31 30Cap k-Median 43.79 1464SSCFLP 31.27 2248
Impact of Polarity CutsImpact of Polarity Cuts
Deterministic Problem
% Duality Gap Closed
% Time Spent
Set Covering 23.74 0.22
CWLP 11.44 9.43
Cap k-Median 0.00 0.21
SSCFLP 18.45 0.29
Polarity Cuts' Strengthening
Value of InformationValue of Information
Deterministic Problem
Value of Information (%)
Set Covering 5.75CWLP 15.05Cap k-Median 9.54SSCFLP 4.60
Value of InformationValue of Information
Deterministic Problem Value of Information
(%)Set Covering 5.75CWLP 15.05Cap k-Median 9.54SSCFLP 4.60
Empirical Observation
Probabilistic versions of simple and moderately difficult mixed integer programs can themselves be formulated as MIPs which can be solved in reasonable amount of time.
Structured DistributionsStructured Distributions
Research Question
Is it possible to exploit structure of distributions to design models which are polynomial in the input size?
Stationary DistributionsStationary Distributions
Definition
A distribution function F is said to be stationary if F(z) depends only on the number of ones in z.
Principle of Indistinguishability.
Stationary DistributionsStationary Distributions
000
100 010 001
011101110
111
Stationary DistributionsStationary Distributions
Can be converted to a MIP with linear number of additional variables and constraints!!
Stationary DistributionsStationary Distributions
A model with linear number of variables and constraints!!
Stationary DistributionsStationary Distributions
Deterministic Problem
Number of Probabilistic
Instances
Number of Unsolved Instances
% Relative Gap (Unsolved
Instances)
Solution Time (sec)
Value of Information
Set Covering 1920 127 21.34 112.98 10.42
CWLP 1184 0 - 0.09 25.15
Cap k-Median 640 0 - 2.90 15.25
SSCFLP 2240 17 0.45 9.36 8.51
• 8 Block Sizes: 5, 10, 20, 50, m/4, m/3, m/2, m• 4 Threshold Probabilities: 0.80, 0.85, 0.90, 0.95
Number of Instances per deterministic instance= 32
Stationary DistributionsStationary Distributions
Research Question
What is that unique property of stationary distributions which allowed us to design a linear sized model?
Disjunctive Shattering Property
The lattice of a stationary distribution can be partitioned into polynomial number of pieces each of which has a polynomial sized description.
Stationary DistributionsStationary Distributions
000
100 010 001
011101110
111
Summary
BR Algorithm MIP Model
p-Inefficiency
Polarity Cuts
ComputationalResults
Stationary Distributions
Super LinearSpeedup
Refinement
Strengthening
Our ContributionOur Contribution
Thank you for your attention