2016 INFORMS Annual Meeting Program

367 / 561

INFORMS Nashville – 2016
367
4 - Optimization Of Cascading Processes In Multiscale Networks
With Stochastic Interactions
Oleg A Prokopyev, University of Pittsburgh, 1037 Benedum Hall,
Pittsburgh, PA, 15261, United States,
Juan Borrero, Pavlo Krokhmal
We study the problem of optimal cascade propagation in a network, where the
cascade’s spread depends on a vector of given attributes. Given there are costs
associated with changing the attributes’ values, a decision-maker desires to
minimize the time until all network nodes receive influence, subject to a budget
constraint. To this end, we propose a stochastic optimization model based on
Markov chains. Under simple assumptions, we derive analytical solutions for the
optimal budget allocation in terms of a minimum spanning arborescence on an
auxiliary graph. These results establish that optimal solutions have a hierarchical
structure, and show that they can be found in polynomial time.
WA12
104B-MCC
Optimization in Cyber Defense
Sponsored: Optimization, Integer and Discrete Optimization
Sponsored Session
Chair: Les Servi, MITRE, 202 Burlington Road, Bedford, MA, 01730,
United States,
Co-Chair: Doug Altner, MITRE Corporation, 7525 Colshire Drive,
McLean, VA, 22102, United States,
1 - A Supply Chain Network Game Theory Model Of Cybersecurity
Investments With Nonlinear Budget Constraints
Shukla Shivani, University of Massachusetts, Amherst, MA,
United States, Anna B Nagurney,
Patrizia Daniele
In our paper, we develop a supply chain network game theory model consisting
of retailers that compete noncooperatively to maximize their expected profits and
reduce network vulnerability by determining their optimal product transactions
as well as cybersecurity investments subject to nonlinear budget constraints that
include the cybersecurity investment cost functions.
2 - Optimal Scheduling Of Cybersecurity Analysts For
Minimizing Risk
Rajesh Ganesan, George Mason University, Fairfax, VA, 22030-
4422, United States, , Ankit Shah,
Sushil Jajodia, Hasan Cam
The talk presents a generalized optimization model for scheduling cybersecurity
analysts to minimize risk (a.k.a maximize significant alert coverage by analysts)
and maintain risk under a pre-determined upper bound. The paper tests the
optimization model and its scalability on a set of given sensors with varying
analyst experiences, alert generation rates, system constraints, and system
requirements.
3 - Dynamic Scheduling Of Cybersecurity Analysts For Minimizing
Risk Using Reinforcement Learning
Rajesh Ganesan, George Mason University, Fairfax, VA, 22030-
4422, United States, , Ankit Shah,
Sushil Jajodia, Hasan Cam
The talk presents a reinforcement learning-based stochastic dynamic
programming optimization model that incorporates the estimates of future alert
rates and responds by dynamically scheduling on-call cybersecurity analysts to
minimize risk (a.k.a maximize significant alert coverage by analysts) and maintain
the risk under a pre-determined upper bound.
4 - A Two-stage Stochastic Shift Scheduling Model For Cybersecurity
Workforce Optimization With On Call Options
Doug Altner, MITRE Corporation, McLean, VA, United States,
, Les Servi
This talk proposes a two-stage stochastic program for optimizing staffing and shift
scheduling decisions at a 24/7 cybersecurity operations center with three shifts
per day, several staffing and scheduling constraints, uncertain workloads and on
call staffing options. We then show how near optimal solutions can be obtained in
a few minutes without a full branch-and-price implementation.
WA13
104C-MCC
Recent Developments in Optimization Software
Sponsored: Optimization, Computational Optimization and Software
Sponsored Session
Chair: Hans Mittelmann, Arizona State University, Box 871804,
Tempe, AZ, 85287-1804, United States,
1 - Selected Benchmark Results
Hans Mittelmann, Arizona State University,
We will present results from selected benchmarks we are maintaining in both
continuous and discrete optimization by both commercial and open source
software.
2 - Recent Advances In The New Mosek 8
Andrea Cassioli, MOSEK, Erving
Anderson
In this talk we present the new features and improvements in the new MOSEK 8
solver. Improved presolving, automatic dualization for conic quadratic problems
and other new developments in the core routines has lead to a significant
improvements of the solver performance both in terms of speed and accuracy.
Computational results will be presented and discussed.
3 - Recent Advances In The SCIP Optimization Suite
Gregor Hendel, Zuse Institute Berlin, Takustrasse 7, Berlin, 14195,
Germany,
The general-purpose branch-and-cut solver SCIP is one of the fastest
noncommercial software tools for solving mixed integer linear optimization
problems. In this talk, we will give an overview of algorithmic advances in the
upcoming release with a special focus on new and extended primal heuristics of
SCIP.
4 - UG[PIPS-SBB, MPI]: A Massively Parallel Branch-and-bound
Solver For Stochastic Mixed-integer Programs
Yuji Shinano, Zuse Institute Berlin, , Lluis-Miquel
Munguia, Geoffrey Malcolm Oxberry, Deepak Rajan
PIPS-SBB is a LP-based branch-and-bound solver using a distributed-memory
simplex algorithm that leverages the structure of stochastic mixed-integer
programs (MIPs). However, it does not parallelize its branch-and-bound tree
search. The Ubiquity Generator (UG) is a general framework for the external
parallelization of mixed-integer programming solvers. It has been used to develop
ParaSCIP, a massively parallel version of the academic constraint integer
programming solver SCIP. In this talk, we will introduce a parallel solver ug[PIPS-
SBB, MPI] in which PIPS-SBB’s branch-and-bound tree search is parallelized on
top of the parallel solution of the LP relaxations.
WA14
104D-MCC
Inventory Management
Contributed Session
Chair: Reha Uzsoy, North Carolina State University, Dept. of Industrial
& Systems Engg, 300 Daniels Hall Camps Box 7906, Raleigh, NC,
27695-7906, United States,
1 - Inventory Control Policy For A Periodic Review System
With Expediting
Yi Tao, Assistant Professor, Guangdong University of Technology,
161 Yinglong Road, Tianhe District, Guang Zhou, 510520, China,
, Loo Hay Lee, Ek Peng Chew, Gang Sun,
Charles Vincent
We study a periodic review inventory system where two modes, regular and fast
mode, are available to obtain replenishment. A firm can choose fast mode with
shorter lead time at a higher cost when necessary. A two-replenishment-mode
model, with random expediting points is established and an ordering policy (S,e)
which replenishes the inventory to S in every cycle and expedites part of the
order using fast mode when the inventory drops below e, is proposed. A
simulation optimization based heuristic which uses infinitesimal perturbation
analysis (IPA) method and gradient search algorithm is employed to find the best
(S,e). Numerical experiments have shown our new policy outperforms existing
policies.
WA14