Research Overview Simge Kkyavuz 11/8/2018 Kkyavuz NUTC-BAC 1/13 - - PowerPoint PPT Presentation

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Research Overview Simge Kkyavuz 11/8/2018 Kkyavuz NUTC-BAC 1/13 - - PowerPoint PPT Presentation

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Research Overview Simge Kkyavuz 11/8/2018 Kkyavuz NUTC-BAC 1/13 About Me PhD in Operations Research, University of California, Berkeley, 2004 Research Associate, HP Labs, 2003 Assistant Professor, University of Arizona,

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Research Overview

Simge Küçükyavuz 11/8/2018

Küçükyavuz NUTC-BAC 1/13

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About Me

  • PhD in Operations Research, University of California, Berkeley, 2004
  • Research Associate, HP Labs, 2003
  • Assistant Professor, University of Arizona, The Ohio State University,

2004-2008

  • Associate Professor, The Ohio State University, University of

Washington, 2009-2016

  • Associate Professor, NU-IEMS, September 2018 -

Küçükyavuz NUTC-BAC 2/13

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Decision Making Under Uncertainty

  • Decision-making in complex systems

Küçükyavuz NUTC-BAC 3/13

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Decision Making Under Uncertainty

  • Decision-making in complex systems
  • Interconnected components (networks) and large scale

Küçükyavuz NUTC-BAC 3/13

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SLIDE 5

Decision Making Under Uncertainty

  • Decision-making in complex systems
  • Interconnected components (networks) and large scale
  • Discrete choices (whether/or not, if/then, indivisible quantities)

Küçükyavuz NUTC-BAC 3/13

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SLIDE 6

Decision Making Under Uncertainty

  • Decision-making in complex systems
  • Interconnected components (networks) and large scale
  • Discrete choices (whether/or not, if/then, indivisible quantities) →

exponential decision space

Küçükyavuz NUTC-BAC 3/13

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SLIDE 7

Decision Making Under Uncertainty

  • Decision-making in complex systems
  • Interconnected components (networks) and large scale
  • Discrete choices (whether/or not, if/then, indivisible quantities) →

exponential decision space

  • High levels of uncertainty: Risk/reliability/resilience/service levels

Küçükyavuz NUTC-BAC 3/13

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SLIDE 8

Decision Making Under Uncertainty

  • Decision-making in complex systems
  • Interconnected components (networks) and large scale
  • Discrete choices (whether/or not, if/then, indivisible quantities) →

exponential decision space

  • High levels of uncertainty: Risk/reliability/resilience/service levels
  • Multiple (often conflicting) performance criteria/multiple decision

makers

Küçükyavuz NUTC-BAC 3/13

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SLIDE 9

Decision Making Under Uncertainty

  • Decision-making in complex systems
  • Interconnected components (networks) and large scale
  • Discrete choices (whether/or not, if/then, indivisible quantities) →

exponential decision space

  • High levels of uncertainty: Risk/reliability/resilience/service levels
  • Multiple (often conflicting) performance criteria/multiple decision

makers

  • Applications in a wide variety of fields:

Supply chain & logistics, homeland security, social networks, energy, finance

Küçükyavuz NUTC-BAC 3/13

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SLIDE 10

General Framework

  • Data → Decisions (Prescriptive Analytics)

Küçükyavuz NUTC-BAC 4/13

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General Framework

  • Data → Decisions (Prescriptive Analytics)
  • Detailed mathematical modeling of the system:

Data, decision variables, objective function, constraints

Küçükyavuz NUTC-BAC 4/13

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General Framework

  • Data → Decisions (Prescriptive Analytics)
  • Detailed mathematical modeling of the system:

Data, decision variables, objective function, constraints

  • Solution methodologies:
  • State-of-the-art software cannot solve such complex problems out of

the box

Küçükyavuz NUTC-BAC 4/13

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General Framework

  • Data → Decisions (Prescriptive Analytics)
  • Detailed mathematical modeling of the system:

Data, decision variables, objective function, constraints

  • Solution methodologies:
  • State-of-the-art software cannot solve such complex problems out of

the box

  • Need to decompose into smaller problems, and coordinate to reach
  • ptimal solutions

Küçükyavuz NUTC-BAC 4/13

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SLIDE 14

General Framework

  • Data → Decisions (Prescriptive Analytics)
  • Detailed mathematical modeling of the system:

Data, decision variables, objective function, constraints

  • Solution methodologies:
  • State-of-the-art software cannot solve such complex problems out of

the box

  • Need to decompose into smaller problems, and coordinate to reach
  • ptimal solutions
  • Advanced optimization methods enable solutions at large scale

Küçükyavuz NUTC-BAC 4/13

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General Framework

  • Data → Decisions (Prescriptive Analytics)
  • Detailed mathematical modeling of the system:

Data, decision variables, objective function, constraints

  • Solution methodologies:
  • State-of-the-art software cannot solve such complex problems out of

the box

  • Need to decompose into smaller problems, and coordinate to reach
  • ptimal solutions
  • Advanced optimization methods enable solutions at large scale
  • Automate decision-support processes, sensitivity (what-if) analysis

Küçükyavuz NUTC-BAC 4/13

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Stochastic Pre-disaster Relief Network Design Problem

Küçükyavuz NUTC-BAC 5/13

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Stochastic Pre-disaster Relief Network Design Problem

  • Decide on the locations and capacities of the response facilities (pre-disaster)

Küçükyavuz NUTC-BAC 5/13

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Stochastic Pre-disaster Relief Network Design Problem

  • Decide on the locations and capacities of the response facilities (pre-disaster)
  • Determine a distribution plan of the relief items through the network

(post-disaster)

Küçükyavuz NUTC-BAC 5/13

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Stochastic Pre-disaster Relief Network Design Problem

  • Decide on the locations and capacities of the response facilities (pre-disaster)
  • Determine a distribution plan of the relief items through the network

(post-disaster)

  • Uncertainty in the severity and impact of the disaster: amounts of supply

and demand, transportation network conditions

Küçükyavuz NUTC-BAC 5/13

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Stochastic Pre-disaster Relief Network Design Problem

  • Decide on the locations and capacities of the response facilities (pre-disaster)
  • Determine a distribution plan of the relief items through the network

(post-disaster)

  • Uncertainty in the severity and impact of the disaster: amounts of supply

and demand, transportation network conditions

  • Goals:
  • 1. Efficiency: minimizing cost
  • 2. Efficacy: quick and sufficient distribution
  • 3. Equity: fairness in terms of supply allocation and response times

Küçükyavuz NUTC-BAC 5/13

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Case Study

Disaster preparedness for the threat of hurricanes in the Southeastern part

  • f the United States (Rawls and Turnquist, 2010)

Küçükyavuz NUTC-BAC 6/13

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Model Analysis

  • The proposed risk-averse modeling approach provides
  • A wide range of solutions that consider the trade-offs between

multiple criteria

  • Inclusion of different opinions of multiple decision makers on

the relative importance of criteria

  • Compared to its risk-neutral counterpart:
  • Better solutions in terms of equity and/or responsiveness
  • Compromises from the expected total cost objective

Küçükyavuz NUTC-BAC 7/13

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Computational Results

  • Intel(R) Xeon(R) CPU E5-2630 processor at 2.40 GHz and 32 GB of RAM

using Java and Cplex 12.6.0.

  • 1 hour time limit
  • Risk level: α = 0.05

Existing Methods Proposed Methods # Scenarios Time (s) Time (s) 300 900.71 393.36 400 1992.63 744.18 500 2117.53 979.09 800

∗

763.25

∗: Instances hit the time limit with no feasible solution. Noyan, Merakli∗ and K., “Two-stage Stochastic Programming under Multivariate Risk Constraints with an Application to Humanitarian Relief Network Design," minor revision, Math Prog, 2018. Küçükyavuz NUTC-BAC 8/13

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Disaster Preparedness: Hurricane Rita

  • Cars ran out of fuel during

evacuation

  • Caused third worst traffic jam in

history, 100-mile long, 2.5 mil stuck in cars

  • First-stage: Pre-position

supplies and determine stocking levels of supply (fuel/meals/water/medical kits)

  • Second-stage: Distribution of

supplies following the aftermath

Gao∗, Chiu, Wang∗ and K., Optimal Refueling Station Location and Supply Planning for Hurricane Evacuation," TRR, 2010. Küçükyavuz NUTC-BAC 9/13

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Homeland security budget allocation problem

  • Multiple risk criteria: property losses, fatalities, air departures,

average daily bridge traffic.

  • Urban areas: NYC, Chicago, SF, DC, LA, Seattle, Philly, Boston,

Houston, Newark

  • Allocate limited budget to the urban areas to limit the misallocation
  • f funds (risk) under each criteria
  • Benchmarks: RAND allocation and Government allocation by DHS’s

Urban Areas Security Initiative

Küçükyavuz NUTC-BAC 10/13

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Influence Maximization Problem

  • Spread of information/disease/threat in a network.
  • Identify a few influencers to maximize spread.

Küçükyavuz NUTC-BAC 11/13

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SLIDE 27

Conclusions

  • Complex systems require advanced mathematical models and solution

methods

  • Need to explicitly handle uncertainty, and large decision space (e.g.,

catastrophic disasters, sharing economy, autonomous vehicles, drone delivery)

  • Large-scale stochastic mixed-integer optimization models and

methods are highly effective

Küçükyavuz NUTC-BAC 12/13

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Conclusions

  • Complex systems require advanced mathematical models and solution

methods

  • Need to explicitly handle uncertainty, and large decision space (e.g.,

catastrophic disasters, sharing economy, autonomous vehicles, drone delivery)

  • Large-scale stochastic mixed-integer optimization models and

methods are highly effective

  • These projects are funded by National Science Foundation Grants:
  • Mixed-Integer Programming Approaches for Risk-Averse Multicriteria

Optimization

  • CAREER: Mixed-Integer Optimization under Joint Chance Constraints
  • Stochastic Mixed-Integer Optimization: Polyhedral Theory, Large-Scale

Algorithms and Computations

  • Mixed-Integer Optimization for Multi-Item, Multi-Echelon Production

and Distribution Planning

Küçükyavuz NUTC-BAC 12/13

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Research Team

  • Dinakar Gade, PhD - SABRE
  • Minjiao Zhang, PhD - Kennesaw State University
  • Pelin Damci-Kurt, PhD - Lightning Bolt Solutions
  • Saumya Goel, MS - Bank of America Merrill Lynch
  • Xiao Liu, PhD - United Airlines
  • Hao-Hsiang Wu (current PhD student)
  • Hasan Manzour (current PhD student)
  • Merve Merakli (current post-doc)

Küçükyavuz NUTC-BAC 13/13

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Research Team

  • Dinakar Gade, PhD - SABRE
  • Minjiao Zhang, PhD - Kennesaw State University
  • Pelin Damci-Kurt, PhD - Lightning Bolt Solutions
  • Saumya Goel, MS - Bank of America Merrill Lynch
  • Xiao Liu, PhD - United Airlines
  • Hao-Hsiang Wu (current PhD student)
  • Hasan Manzour (current PhD student)
  • Merve Merakli (current post-doc)

Selected Research Awards: INFORMS Computing Society Prize, George Nicholson Student Paper Prize

Küçükyavuz NUTC-BAC 13/13