Informs Annual Meeting 2017

TE43

INFORMS Houston – 2017

TE43

2 - Maximizing Social Welfare in Vaccine Procurement for Multiple International Group Buying Entities with or with out Cooperation Bruno Alves Maciel, Rochester Institute of Technology, 5000 Nathaniel Rochester Hall 248, Rochester, NY, 14623, United States, ba8641@rit.edu, Kushal Mehta, Ruben Proano This study models the global vaccine market for a set of antigen bundles from different manufacturers. Those are sold to international group-buying entities (GAVI, PAHO, etc.) and/or individual countries organized as markets that may or may not cooperate. Through a sequence of optimization problems, vaccine prices and allocations are compared in different scenarios of cooperation to maximize social welfare and ensure profit for the manufacturers. 3 - Assessing Decisions in Medical Referral Networks from Empirical Data Mojtaba Araghi, Wilfrid Laurier University, 438 King Street W, Apt 1208, Toronto, ON, M5V. 3T9, Canada, maraghi@wlu.ca, Michael Pavlin Informal referral networks are central to the allocation of medical resources in many healthcare systems. In this paper we assess decisions in a cataract surgery referral network. The system is modeled as a bipartite queueing network and empirical techniques are developed to estimate decision making parameters from aggregate data. 4 - Application of Kolmogorov-sinai Entropy for Estimating Uncertainty in Infectious Disease Spread Model Anna Paula Galvão Scheidegger, Texas A&M.University, College Station, TX, 77843-3131, United States, apscheidegger@tamu.edu, Amarnath Banerjee Kolmogorov-Sinai (KS) entropy has been widely applied for image texture and biological signal analyses. Consequently, it has been proposed as an alternative tool to diagnose chronic diseases such as epilepsy and Parkinson. Despite the similarities between chronic and infectious diseases with respect to complexity, uncertainty and dynamic components, to the best of our knowledge KS entropy has not yet been applied to infectious diseases. Therefore, the goal of this study is to use KS entropy to investigate whether infectious disease spread models are sensitive to initial conditions and to quantify the average amount of uncertainty in the model. 360A Military Applications Contributed Session Chair: Tulay Flamand, Colorado School of Mines, Division of Economics and Business, Engineering Hall 816 15th Street, Room 313, Golden, CO, 80401, United States, tulayvarol@gmail.com 1 - Distinguishing Temporal Associations from Coincidences Thomas R.Willemain, Senior VP, Smart Software, Inc., 4 Hill Road, Belmont, MA, 02478, United States, tomw@smartcorp.com Consider two binary time series. In some time periods, both series will be “on” simultaneously. How do we use this type of data to distinguish between associations and mere coincidences? The answer has operational significance in logistics and other military functions. We review several alternative technical approaches to this problem and outline an application to spare part logistics. 2 - Social Networks and Life Satisfaction: the Interplay of Cultural Orientation and Network Density Jacob Do-Hyung Cha, Seoul National University, Room 204, 935-Dong, Family Housing, SNU, Seoul, 150-742, Korea, Republic of, research.dohyung@gmail.com, Yun Ha Cho We present that an individual’s cultural orientation moderates the influence of social network density on life satisfaction. Study 1 demonstrates that high collectivism effectiveness participants report higher life satisfaction when embedded in a high-density network, whereas high individualism effectiveness participants report lower life satisfaction in the same condition. Study 2 elaborates the underlying mechanism; a high-density network facilitates interdependent self-oriented goals pursuits while it hampers independent self-oriented goals pursuits. We discuss the implications for studying the interplay among social Tulay Flamand, Colorado School of Mines, Division of Economics and Business, Engineering Hall 816 15th Street, Room 313, Golden, CO, 80401, United States, tflamand@mines.edu, Mohamed Haouari, Ghaith Rabadi We address the problem of deploying multiple types of commodities or assets from various origins to various destinations across a network of multiple modes of transportation. Applications of this problem exist in military, commercial and humanitarian contexts. A B&P algorithm is proposed for this hard problem to minimize the total travel time. TE42 networks, cultural psychology, and positive psychology. 3 - A Branch-and-price Algorithm for the Optimal Deployment Problem

360B Proactive Planning Against Natural Disasters Sponsored: Public Sector OR Sponsored Session Chair: Ozlem Ergun, Northeastern University, Boston, MA, 02115, United States, o.ergun@neu.edu Co-Chair: Mahsa Ghanbarpour, mahsa.ghanbarpour@gmail.com 1 - Considering Disaster Volunteer Behavior in Management Decisions During Relief Events: A Simulation Approach Abdelwahab Alwahishie, Clemson University, Clemson, SC, United States, aalwahi@g.clemson.edu, Kevin M. Taaffe Following disasters, non-governmental organizations (NGOs) rely heavily on their volunteers to provide assistance to the impacted communities. While their main goal is to serve community needs, NGOs should not ignore disaster volunteer’s dissatisfaction and intention to quit. We propose a simulation that models a volunteer’s behavior and a manager’s decisions through the effects of job mismatch, workload and group conflict. Embedding a volunteer’s in the NGO’s decisions can reduce volunteer dissatisfaction and intention to quit while not negatively impacting the level of unmet community needs. The model information is based on survey data from two relief organizations in the US. 2 - A Robust Chance Constraint Programming Approach for Evacuation Planning under Uncertain Demand Distribution Ayda Darvishan, University of Houston, Houston, TX, United States, ayda.darvishan@gmail.com, Gino Lim, Mukesh Rungta, Mohammad Reza baharnemati@gmail.com We consider an evacuation planning problem where the demand (the number of evacuees) is uncertain and use a chance-constrained model to ensure a reliable evacuation plan. In the context of mass evacuation, we make the assumption that only partial information of the demand distribution, such as moment, support, or symmetry information is known. A distributionally robust chance-constrained model is proposed that ensures the demand constraints are satisfied for any probability distribution consistent with the known properties of the underlying unknown demand. Numerical experiments suggest that our model provides excellent results in terms of solution feasibility and robustness. 3 - Evaluating the Hurricane Decision Simulator Cameron MacKenzie, Iowa State University, We previously developed the Hurricane Decision Simulator to help the U.S. Marine Forces Reserve in New Orleans train to make decisions in advance of a hurricane. In the simulation, a user must decide when and which preparatory decisions to make (e.g., evacuating the headquarters). This presentation will provide the results of a behavioral study in which college students trained using the simulation. We assess how their decisions change after practicing with the simulation. Users believe their decisions improve after practicing with the simulation, and training on the simulation makes users more likely to wait to evacuate. 4 - Proactive Surgery Cancellation Based on Weather Forecasts Mahsa Ghanbarpour, Northeastern University, Boston, MA, 02115, United States, ghanbarpourmamagh.m@husky.neu.edu, Ozlem Ergun Severe weather such as hurricanes and snowstorms can dramatically disrupt the delivery of many essential services within a community, including healthcare services. This study introduces proactive surgery cancellation planning due to impending snowstorms and considers weather forecast updates as time progresses. Since weather events can be predicted more accurately closer to the event, the framework we develop is a decision support system for the evaluation of the tradeoff between making better decisions later due to more accurate forecasts and the increased cost of cancellations as the event gets closer. This tradeoff is captured by a dynamic planning approach over particular time horizons. 5 - Cooperative Strategies for Emergency Services under Resource Constrained Settings Lavanya Marla, University of Illinois, Urbana, IL, lavanyam@illinois.edu, Jungeun Shin We consider a case where emergency medical services compete as well as collaborate to serve a population. Settings like these are inspired from the cases of mutual aid between neighboring counties, and from emerging economies, where multiple EMSs have to interact strategically and tactically. We aim to understand the cooperative and competitive impacts when EMS agencies interact and share resources or information. We embed simulation-optimization frameworks into game-theoretic settings to understand the impact of such cooperative and competitive interactions. 3029 Black Engineering, Ames, IA, 50014, United States, camacken@iastate.edu, Eva D.Regnier, Anna Prisacari, Sophia Hetherington

425