2016 INFORMS Annual Meeting Program

WB54

INFORMS Nashville – 2016

WB52

value function is approximated by linear regression using both simulation and the modelling of decision dependent uncertainties. Using the real-world case of district heating network investments in London, we investigate the effects of the consideration of decision dependent uncertainties on both the optimal portfolio value and the underlying optimal strategic and operational decisions. 2 - A Stochastic Program For Debris Collection Problem Derya Ipek Eroglu, Research and Teaching Assistant, Middle East Technical University, Dumlupinar Street N:1, Ankara, 06400, Turkey, eipek@metu.edu.tr, Duygu Pamukcu Our aim is to develop a stochastic program for debris collection problem, which enables us to make decisions regarding different disaster types or different scenarios that can take place for a disaster type. Debris collection is important for human health since uncollected debris may lead to pollution. We develop stochastic programs using appropriate decomposition methods which will be compared in terms of computational efficiency, test the program with different variants, compare the model with pre-developed deterministic model(Celik et al.) and analyse related performance measures. We present results of our computational studies and analysis. 3 - Management Of Scarce Blood Supplies Accounting For Cross-matching Characteristics Nooshin Valibeig, Northeastern University, 360 Huntington Ave, Boston, MA, 02115, United States, n.valibeig@neu.edu, Jacqueline Griffin For blood transfusion, availability of blood of a compatible type is crucial to patient treatment and reductions in mortality rate. In isolated environments, such as in the aftermath of a disaster or combat environment, demand for blood is unpredictable and timing and quantity of stock replenishment is unreliable. Correspondingly, the risk and the cost of blood shortages is high. We develop a stochastic optimization model to develop threshold policies to prevent from blood shortages considering proactive allocation, accounting for cross-matching criteria, to satisfy the requests for various blood types. We assess the effectiveness of these real-time allocation policies via simulation method. 4 - Multistage Power Generation Capacity Expansion Models With Different Risk Measures Shu Tu, lehigh university, 200 West Packer Avenue, Bethlehem, PA, 18015, United States, sht213@lehigh.edu, Boris Defourny When it comes to the multistage problems, the stochastic programming models rely on the convexity property of the problems and the solution approaches usually rely on the stage independence assumption, with Markov decision processes being advantageous from these aspects. Therefore, we adapts the “good- deal” generation capacity expansion model to the form of Markov decision processes and will implement it with C++ which can make use of parallel computing and ILOG Concert Technology. 5 - Control Policies For Queueing Systems With Time Sensitive Jobs Tugce Isik, Clemson University, 277D Freeman Hall, Dept. of Industrial Engineering, Clemson, SC, 29634, United States, tisik@clemson.edu, Bahar Cavdar We consider a queueing system where each job has a preferred time window for service. We assume that all jobs must be served and the jobs are outsourced when the capacity is insufficient. Costs are incurred for both outsourced jobs and early service. For systems with short time windows and a single class of jobs, we show that a class of threshold service policies are optimal. For general systems, we devise heuristic policies based on similar threshold structures. WB54 Music Row 2- Omni Service Science: Uncertainty in Business Processes Sponsored: Service Science Sponsored Session Chair: Genady Grabarnik, St. John’s University, St. John’s University, Queens, NY, United States, genadyg@gmail.com 1 - Continuity Of The Lyapunov Exponenets Under Continuity Of Measures In Sl (2, R) Genady Grabarnik, St. John’s University, genadyg@gmail.com In the business processes composition of processes results in special type of product of the appropriate distributions. In this case it corresponds to product of random matrices. It is well know that behavior of random matrices controlled by its Lyapunov exponents. We are investigating stability of the top Lyapunov exponent under perturbation of defining measure in weak topology.

214-MCC New Advances in Operating Room (OR) Scheduling Sponsored: Public Sector OR Sponsored Session Chair: Gino J Lim, University of Houston, E206 Engineering Building 2, Houston, TX, 77204, United States, ginolim@uh.edu 1 - Integrated Anesthesiologist And Room Scheduling For Surgeries Sandeep Rath, UCLA Anderson School of Management, Sandeep.Rath.1@anderson.ucla.edu, Kumar Rajaram At large hospitals the assignment and scheduling of anesthesiologists and operating rooms is a complex resource allocation decision undertaken daily by the managers of operating room suites. We validate and implement a data-driven decision support system at the UCLA Ronald Reagan Medical Center. We also conduct analyses related to capacity expansion and process improvement efforts. 2 - A Discrete Event Simulation Evaluation Of Distributed Operating Room Scheduling We use discrete event simulation to assess the performance of deterministically optimized OR schedules in a network of collaborating hospitals with shared resources, called distributed OR scheduling (DORS), in the face of uncertain surgical durations, emergency arrivals, and limited downstream resources. We quantify the individual and combined disruptive impact of these stochastic factors on the DORS schedule, using real data obtained from the University Health Network (UHN) in Toronto, Canada. We show that the schedule constructed by DORS results in higher OR utilization and lower average surgery cost compared to UHN’s current schedule. 3 - Scheduling Of Multi-priority Patients With Preference, Cancellation, No Show And Capacity Uncertainty Deepak Agrawal, Penn State University, dua143@psu.edu, Guodong Pang, Priyantha Devapriya, Soundar Kumara Reasons of No-shows and how to stop them, has been focus of the research since more than a decade. No-show adds up to the increases healthcare waste. Therefore, motivated by this we aim to develop advanced scheduling models which can reduce No-shows by scheduling appointments at patients’ preferred day and time with their preferred physicians while simultaneously maximizing the profit for clinics. Patient choices are modeled as mixed-logit model. The dynamic scheduling process is modeled as a Markov decision process. We conduct numerical experiments to test the performance of the dynamic model in comparison to several heuristics proposed in the literature. 4 - A Lagrangian Relaxation Algorithm For Solving Surgery Vahid Roshanaei, PhD Candidate, University of Toronto, 5 King’s College Road, Toronto, ON, M5S3G8, Canada, vroshana@mie.utoronto.ca, Shuo Wang, Dionne Aleman, David Urbach We studied a surgery scheduling and resource allocation problem by considering uncertain durations. A mixed integer programming formulation is developed in order to maximize throughput and minimize overtime. A Lagrangian Relaxation algorithm is applied to solve large-scale problems efficiently. Having a risk-averse attitude, we applied Conditional Value-at-Risk to tackle uncertainty. WB53 Music Row 1- Omni Opt, Stochastic VI Contributed Session Chair: Tugce Isik, Clemson University, 277D Freeman Hall, Dept. of Industrial Engineering, Clemson, SC, 29634, United States, tisik@clemson.edu 1 - Valuing A Portfolio Of Systemic Urban Infrastructure Investments Using Approximate Dynamic Programming With Decision Dependent Uncertainties Sebastian Maier, Imperial College London, Imperial College Road, Skempton Building, London, SW7 2AZ, United Kingdom, s.maier13@imperial.ac.uk, John W Polak, David M Gann We present a new portfolio-based framework for the application of approximate dynamic programming to the valuation and risk-management of urban infrastructure investments with decision dependent uncertainties. We use this framework to formulate a multistage stochastic optimisation model in which the Scheduling Problem Under Uncertain Durations Amirhossein Najjarbashi, University of Houston, amirhossein.najjarbashi@gmail.com, Gino J Lim

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