2016 INFORMS Annual Meeting Program

360 / 561

INFORMS Nashville – 2016
360
TD78
Legends F- Omni
Opt, Network IV
Contributed Session
Chair: Devendra Anil Shelar, Graduate Research Assistant,
Massachusetts Institute of Technology, 550 Memorial Drive,
Tang Residence Hall, 8D2, Cambridge, MA, 2139, United States,
1 - Cost Minimization Of Government Issued Cell Phones
Alexander Reid Barclay, Slippery Rock University, 20439 Hillview
Road, Saegertown, PA, 16433, United States, ,
John Yannotty
Increasing costs associated with cell phone circuits has led the United States
Pentagon to study consolidation of its wireless network in attempt to minimize
annual expense while maximizing efficiency. During consolidation, Private Virtual
Channels (PVCs) are transferred from either low utilized or expensive circuits to
existing circuits with higher utilization and lower annual cost. The consolidation
process is further constrained by region, service package (VCI code), and
utilization capacity per circuit. Through the use of an optimized network annual
expenses are decreased from approximately $11 million to $3.1 million.
2 - Bi-objective Maximal-covering Minimal-tour Problem With
Applications In Disaster Relief
Sanaz Goldani, PhD. Student, North Carolina State University,
2127A Gorman Street, Raleigh, NC, 27606, United States,
, Yahya Fathi
The Bi-objective Maximal-covering Minimal-tour Problem (BCTP) is defined on a
graph G = (V W, E), where W is a set of vertices associated with the demand. The
BCTP aims at determining a Hamiltonian cycle on a subset of V so as to
simultaneously minimize the cycle length and the total uncovered demand. A
demand is covered if its associated vertex lies within a pre-specified distance from
a vertex of the cycle. The problem is formulated as a bi-objective IP and a branch-
and-cut algorithm is proposed to solve this problem in the context of the
-constraint method. Computational results are presented.
3 - A Dynamic Programming Approach For Solving The
Orienteering Problem With Time Windows Stochastic Profits And
Risk Constraints
Hadi Feyzollahi, State University of New York at Buffalo, Buffalo,
NY, 14260, United States,
Jose Luis Walteros
Given a graph with stochastic profits, risk levels and time windows associated
with stopping at each of its nodes, we tackle the problem of finding a route that
visits a subset of nodes, within a predefined time, so that the expected sum of the
prizes collected is maximized, without exceeding a limit on the observed risk. We
model the random nature of the profits and risk levels as mixed probability
distributions and propose a dynamic programming approach to solve the resulting
problem. We test our approach by solving a test bed of instances arising from the
context of airborne sensor routing.
4 - Pedestrian-vehicle Mixed-flow Routing Problem In Emergency
Evacuation Network For Public Places
Lei Bu, Institute for Multimodal Transportation, Jackson, MS,
United States, , Chuanzhong Yin,
Wang Feng, Wenchao Shen, Liang Zou
Pedestrian-vehicle mixed-flow routing problem is studied at a public place to
decrease the traffic delay at intersection based on a network planning strategy. An
integer linear programming formulation is proposed to optimize the
representation of space-time status, intersection selection, signal timing, turning
strategy, walkway capacity and roadway capacity constraints with an objective
function to minimize the total cost in the evacuation network. A case study using
traffic microsimulation S-Paramics for pedestrian-vehicle mixed-flow evacuation
around Tianhe Sports Centre Stadium in Guangzhou, China verifies the
effectiveness of the formulation.
5 - Vulnerability Analysis Of Optimal Power Flow Problem Under Data
Manipulation Attacks
Devendra Anil Shelar, Graduate Research Assistant, Massachusetts
Institute of Technology, 550 Memorial Drive, Tang Residence Hall,
8D2, Cambridge, MA, 02139, United States,
Saurabh Amin
A transmission network operator (TSO) solves the classical optimal power flow
(OPF) problem to ensure supply-demand balance, subject to the constraints on
generator outputs, line capacities, and power flows. We study the effects of
malicious parameter manipulations on the OPF solutions using a sequential game
formulation. The defender is the TSO who minimizes the cost of generation. The
attacker is a malicious adversary who can manipulate certain parameters of the
network to introduce capacity bounds violations. We show that an approximately
optimal attack can be computed using a MILP.
TD79
Legends G- Omni
Opt, Stochastic IV
Contributed Session
Chair: Junfeng Zhu, University of Minnesota, 1019 29th Ave SE Unit C,
Apt 103, Saint Paul, MN, 55414, United States,
1 - A Chance-constrained Two-stage Stochastic Program For A
Reliable Microgrid System
Md Abdul Quddus, PhD Student, Mississippi State University,
Department of Industrial & Systems Engineering, PO Box 9542,
Starkville, MS, 39762, United States, ,
Carlos Marino, Ridvan Gedik, Mohammad Marufuzzaman
Curtailment of renewable resources generation during the Microgrid operation
affects revenues and increases greenhouses gas emissions. Researchers pay little
attention in scalable stochastic models for Microgrid for multiple nodes
considering the variability of renewable resources. This study bridges the research
gap by developing a scalable a chance-constrained two-stage stochastic program
to ensure that a significant portion of the renewable resource power output at
each operating hour will be utilized.
2 - Tackling Drug Shortages By Examining Resiliency And
Robustness In Pharmaceutical Supply Chains
Rana Azghandi, PhD Candidate, Northeastern University,
360 Huntington Avenue, Boston, MA, 02115, United States,
Jacqueline Griffin, Ozlem Ergun
Over the past five years, there has been an epidemic of drug shortages. While the
drug shortage problem is widespread, there is a poor understanding of the
features of disruptions in the complex system that lead to these shortages, which
are difficult to recover from. Using a stochastic optimization modeling framework,
we identify system features and policies that are needed to operate a robust and
resilient pharmaceutical supply chain, with minimal drug shortages and quick
recovery from shortages.
3 - Robust And Optimistic Games With Bounded Polyhedral
Uncertainty Sets
Giovanni Paolo Crespi, Associate Professor, Universita’ Degli Studi
dell’Insubria, Via Monte Generoso, 71, Varese, 21100, Italy,
, Matteo Rocca, Davide Radi
We introduce a distribution-free model of incomplete information for finite games
with bounded polyhedral payoff uncertainty sets. We assume players adopt either
a robust or an optimistic approach to contend with payoff uncertainty. When all
players adopt a robust optimization approach, we obtain a robust game as in
Aghassi and Bertsimas in 2006. When all players adopt an optimistic optimization
approach, we define an optimistic game. Existence of equilibrium in both
approaches is proven. Further, we propose an algorithm for optimistic-
optimization equilibria. Both equilibria are identifiable by a method analogous to
those used for Nash equilibria of a finite game with complete information.
4 - Robust Optimization For Chronic Myeloid Leukemia Treatment
Under Uncertainty
Junfeng Zhu, University of Minnesota, 1019 29th Ave SE Unit C,
Apt 103, Saint Paul, MN, 55414, United States,
We propose an approach to deal with parameter uncertainty for multistage mixed
integer optimal control problem(MIOCP) in CML applications. We first build a
model to describe the dynamics of leukemic cells and side effects during CML
treatment. The nominal optimization problem is to minimize the cumulative
leukemic cell size over a planning horizon. We then consider about how the
parameter uncertainty affects the optimal solution. In this project, we only
consider about uncertainty for parameter with the most important factor. Finally,
we propose the robust mixed integer problem and transform it into a mixed
integer linear problem which is solvable.
TD78