2016 INFORMS Annual Meeting Program

TD78

INFORMS Nashville – 2016

TD78 Legends F- Omni Opt, Network IV Contributed Session

TD79 Legends G- Omni Opt, Stochastic 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, shelard@mit.edu 1 - Cost Minimization Of Government Issued Cell Phones Alexander Reid Barclay, Slippery Rock University, 20439 Hillview Road, Saegertown, PA, 16433, United States, axb1106@sru.edu, 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, sgoldan@ncsu.edu, 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, hadifeyz@buffalo.edu, 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, leibu04168@gmail.com, 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, shelard@mit.edu, 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.

Chair: Junfeng Zhu, University of Minnesota, 1019 29th Ave SE Unit C, Apt 103, Saint Paul, MN, 55414, United States, zhuxx793@umn.edu 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, mq90@msstate.edu, 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, azghandi.r@husky.neu.edu, 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 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, zhuxx793@umn.edu 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. dell’Insubria, Via Monte Generoso, 71, Varese, 21100, Italy, giovanni.crespi@uninsubria.it, Matteo Rocca, Davide Radi

360