2016 INFORMS Annual Meeting Program

WC59

INFORMS Nashville – 2016

WC57 Music Row 5- Omni Disaster and Emergency Management II Contributed Session 1 - Dynamic Resource Allocation For Effective Distribution Of In Kind Donations Merve Ozen, PhD Student, University of Wisconsin-Madison, 602 Eagle Heights, Apt I, Madison, WI, 53705, United States, mozen@wisc.edu, Ananth Krishnamurthy In the aftermath of a disaster, victim demands for relief items exceed the immediate supply. In-kind donations sent to the affected region help reduce the gap. In most cases, large amounts of cargo of various degrees of priority arrive at a disaster site with in a short period of time. To sort, grade and distribute the critical resources; prioritization and staffing decisions must be made. We model this problem as a discrete time, discrete state space, finite horizon decision problem where the number of resources to be dedicated to sorting operations is decided. We investigate the structure of the optimum policies and provide managerial insights for humanitarian organizations. 2 - Robust Ambulance Allocation Using Risk-based Metrics Kaushik Krishnan, Graduate Research Assistant, We present robust location strategies for an ambulance fleet in order to maximize service levels under unexpected demand patterns. Our work is motivated by the fact that when small parts of networks incur large emergencies (modeled as a heavy-tailed distribution), the entire system behaves in a heavy-tailed manner. We achieve robust allocations by including risk metrics that account for tail behavior as well as average performance. We build an efficient data-driven algorithm that optimizes based on risk metrics. Our computations show that our solutions account for spatiotemporal patterns and prevent the extent of delay cascades that are typically seen in heavy-tailed arrival distributions. 3 - Identifying And Monitoring International Shipments Of Hazardous Materials And Waste Haibo Wang, Killam Distinguished Professorship, Texas A&M International University, 5201 University Boulevard, Laredo, TX, 78045, United States, hwang@tamiu.edu This project will develop a decision support system for identifying and monitoring international shipments of hazardous materials and waste using service-oriented platform, and provide participants with a U.S. domestic and international cross- border pilot program. WC58 Music Row 6- Omni Finance II Contributed Session Chair: Phillip J Lederer, Professor, University of Rochester, Simon School of Bus Admin, Rochester, NY, 14627, United States, Lederer@simon.rochester.edu 1 - Toward A Firm Inefficiency Risk Factor Of Stock Returns: Model And Empirical Analysis Relative operational inefficiency of a firm in responding to supply/demand competition manifests in high distress risk and vulnerability to economic shocks. A set of firm financial variables are used to compute the inefficiency, relative to its competition, having a positive lagged correlation and negative synchronous correlation with stock returns. The proposed new inefficiency risk factor for the market is robust to size, value and momentum risk factors. 2 - Review And Evaluation Of Operations Capital Projects Phillip J Lederer, Professor, University of Rochester, Simon School of Bus Admin, Rochester, NY, 14627, United States, Lederer@simon.rochester.edu A major interface between finance and operations is a firm’s capital justification process by which are set of activities to evaluate and approve a project proposal, and to tie the its performance to managers’ incentives. We study a principal-agent model where the agent is a manager who designs and proposes a project and, if approved, oversees its execution, and where the principal is general management. A unique aspect of this research is the agent’s choice of project, its effort to manage risk and private information project riskiness. The magnitudes of economic losses due to mis-designed compensation structure, observability of effort, and information asymmetry are presented. University of Illinois at Urbana-Champaign, Urbana, IL, United States, kkrishn3@illinois.edu, Lavanya Marla Daqi Xin, PhD Student, Rensselaer Polytechnic Institute, 110 8th St, Troy, NY, 12180, United States, xind@rpi.edu, Chanaka Edirisinghe

WC59 Cumberland 1- Omni Location of Energy-Efficient Facilities Sponsored: TSL, Facility Logistics Sponsored Session Chair: Mohannad Kabli, Mississippi State Univ, MSU, Mississippi State, MS, 39762, United States, mrk297@msstate.edu Co-Chair: Mohammad Marufuzzaman, Mississippi State University, PO Box 9542, Starkville, MS, 39762, United States, marufuzz@dasi.msstate.edu 1 - Stochastic Model For Locating Multiple Type Recharging Station Under Flow Uncertainty Sushil Raj Poudel, PhD Candidate, Mississippi State University, Department of Industrial & Systems Engineering, P.O. Box 9542, Starkville, MS, 39762, United States, srp224@msstate.edu, This study presents a two-stage stochastic mixed-integer programming model to formulate capacitated multiple-recharging station location problem under flow uncertainty. We solve the problem using a hybrid decomposition algorithm combining sample average approximation with an enhanced progressive hedging algorithm We use Washington DC as a testing ground to visualize and validate the modeling results. The computational experiments provide the geographical distribution for multiple types of recharging stations to ensure the completion of Md Abdul Quddus, Sudipta Chowdhury, Mohammad Marufuzzaman, Linkan Bian Javier Faulin, Full Professor, Public University of Navarra, Los Magnolios Bdg. 1st floor, Campus Arrosadia, Pamplona, 31006, Spain, javier.faulin@unavarra.es, Adrian Serrano-Hernandez, Alejandro Garcia del Valle, Javier Belloso The concern about sustainability is gaining importance leading to seek for renewable energy sources to reduce greenhouse gas emissions (GHG) in transportation. Therefore, this work proposes a procedure to determine a biorrefinery location considering its supply chain environmental impact (including, among others, crop selection and stock policy). A Mixed Integer Linear Programming model, coded in GAMS, was solved giving promising results. Thus, some meaningful sensitivity analysis were run in order to have the environmental criteria met at an affordable cost. Finally, a case study of location of a Biorefinery in Navarre, Spain has been solved. 3 - Chance-constrained Stochastic Programming Model For Locating Charging Stations Under Uncertainty In Green Power Availability Sudipta Chowdhury, Mississippi State University, sc2603@msstate.edu, Mohannad Kabli, MD Abdul Quddus, Mohammad Marufuzzaman Due to the scarcity and negative consequences the use of fossil fuel brings, green energy sources are being increasingly used as an alternative clean source of electricity. Electric vehicles are a part of the solution, and their spread is imminent as the technologies of batteries are advancing faster than ever. This calls for plans that regulates the potential increase in the number of charging stations, which will lead to an increase in the demand for electricity. This work presents a chance- constrained stochastic programming model that plans for the expansion of charging stations with limited power supply and chance-constrained green energy availability. 4 - A Stochastic Programming Approach For Ev Charging Station Expansion Plans Mohannad Kabli, MSU, mrk297@msstate.edu, MD Abdul Quddus, Mohammad Marfuzzaman This paper presents a two-stage stochastic programming model that helps making the decisions for expanding and connecting power in anticipation the increase of electric vehicle charging stations under demand uncertainty . We solve the model using a hybrid algorithm that combine Sample average algorithm with an enhanced Progressive hedging (PH) algorithm. Along with SAA and Progressive hedging we applied some heuristics such as Rolling Horizon (RH) algorithm, variable fixing technique to enhance the PH algorithm. We choose Washington DC as a testing ground to visualize and validate the modeling results. overall tours of multiple type of electric vehicles in each path. 2 - Biorefinery Location And Green Perspectives

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