2016 INFORMS Annual Meeting Program

MD45

INFORMS Nashville – 2016

4 - DAS Ramsey Medal Jeffrey M Keisler, University of Massachusetts - Boston, jeff.keisler@umb.edu The Ramsey Medal of the Decision Analysis Society is awarded for distinguished contributions in decision analysis. Distinguished contributions can be internal, such as theoretical and procedural advances in decision analysis, or external, such as developing or spreading decision analysis in new fields. We will introduce the 2016 Ramsey Medal winner, followed by a presentation by the winner. MD45 209A-MCC Simulation-based Optimization Sponsored: Simulation Sponsored Session Chair: Tahir Ekin, Texas State University, San Marcos, TX, United States, tahirekin@gmail.com 1 - Nested Augmented Simulation For Stochastic Optimization Tahir Ekin, Texas State University, t_e18@txstate.edu, Refik Soyer, Nick Polson This talk presents nested augmented probability simulation to solve for decision making problems with uncertainty. The focus will be on stochastic programs with recourse under decision dependent (endogenous) uncertainty. Augmented probability simulation is based on the idea of treating the decision variable as random and investigating the optimal decision in the joint space of decision and random variables. We present the use of Nested Sampling for simulation from this joint distribution. An illustration is provided on a two stage news-vendor problem. We provide performance comparisons with traditional Monte Carlo simulation and present computational insights. 2 - Bayesian Inference And Augmented Probability Simulation In Call Center Staffing Tevfik Aktekin, University of New Hampshire, Tevfik.Aktekin@unh.edu We consider the issue of short term staffing in a queuing system such as a call center where the system rates are dependent random variables. We consider their estimation using Bayesian inference and the well-known Erlang A queueing model. We formulate the optimization model such that both the objective function and the constraints are random due to the uncertainty in the system rates and propose the use of an augmented probability simulation approach. In our numerical illustration, we consider both real and simulated data examples. In each case, we divide the day into discrete time intervals to determine staffing levels and discuss further implications of our approach. 3 - A Simulation Optimization Framework For Scheduling Preventive Maintenance In Wind Energy Systems Eduardo Perez, Texas State University, eduardopr@txstate.edu Because of the continuously escalating costs of wind farms O&M in the United States, determining methods for using available data in conditioning-monitoring systems is critical to decreasing wind farms operational costs. To accomplish this objective, we have developed a data-driven integrated stochastic online optimization and discrete event simulation methodology that takes into account data uncertainties in turbines status, weather conditions, and resources availability in scheduling maintenance and resources. Discrete event simulation coupled with optimization provides a powerful instrument for assessing and revising schedules prior to actual implementation. 4 - On The Sample Average Approximation Of The Two Stage Chance Constrained Staffing Problem In Call Centers With Arrival Rate Uncertainty Anh Thuy Ta, PhD Student, University of Montreal, CP 6128 Succursale Centre-Ville, Montreal, QC, H3W1C5, Canada, tathuyanh1989@gmail.com, Wyean Chan, Pierre L’ecuyer, Fabian Bastin We consider a chance constrained two stage stochastic staffing problem for multi- skill call centers with arrival rate uncertainty. The aim is to minimize the total cost of agents under some chance constraints, defined over the randomness of the service level in a given time period. We use the Monte Carlo method to generate M scenarios of arrival rates and we perform N simulation runs to get the estimates of probabilities that the service level is satisfied. We then obtain a sample average approximation (SAA) of the problem. We investigate the convergence of the optimal solution of the SAA to that of the original one when the sample size increases and present numerical illustrations on the sample sizes M and N.

(iii) highest probability of yielding the shortest travel time. In each case, the goal is to determine the number of experiments that one has to perform in order to satisfy a certain indifference-zone requirement on the probability of correctly selecting the best path. The experiments can involve actual observed travel times or simulated realizations of travel times. 2 - Production Scheduling Of Jobs Limited By The Number Of Machines And Workers With Setup Times And Route Flexibility Alejandro Garcia del Valle, University of A Coruna, Head Modeling & Simulation UMI Navantia, Ferrol, Spain, alejandro.garcia.delvalle@udc.es, Javier Faulin, Jose Antonio Muina Dono Planning in dual resource constrained job shops like shipyards, is a very difficult task, due to the large number of tasks and limited resources and the setup times. In this paper, we develop a methodology using discrete event simulation models, together with efficient and robust dispatching rules. This models are going to be applied in the Navantia shipyard (Ferrol, Spain) in the context of a virtual shipyard following the concept of Industry 4.0. Results will be presented explaining the influence of jobs priority, assignation of workers to machines, and the reduction of setup times. 3 - Stress Testing For Supply Chain Risk Management Using Simulation Modeling Harrison Luvai, University of Missouri - St. Louis, Saint Louis, MO, 63005, United States, hl6d9@umsl.edu, L. Douglas Smith” We employ a discrete-event simulation model with embedded MILP and heuristics to test the resilience of a three-tier supply network when a blend of strategies is used for mitigating risk. Production and material flows are adjusted in response to daily simulated events. We investigate supply-network vulnerability to system contagion by adjusting correlations in demands downstream and in material deliveries upstream. 4 - MetaSimLab: A Laboratory For Validating And Calibrating Agent-based Simulation For Business Analytics Janina Knepper, Research Group Advanced Analytics, Janina.knepper@ada.rwth-aachen.de Agent-based simulations are frequently used to develop and evaluate new and improved approaches for business analytics and decision support. To be reliable, they have to be empirically calibrated and validated. Existing calibration approaches are rarely automated; however, manual calibration is costly in terms of time and effort. Therefore, this contribution introduces the laboratory environment MetaSimLab, which is designed to evaluate the efficiency and effectiveness of alternative calibration approaches. We present numerical examples to illustrate MetaSimLab’s functionality, novel calibration methods, and an outlook on further research. Chair: Eric Bickel, Associate Professor & Director, The University of Texas at Austin, 204 E Dean Keeton St, Stop C2200, Austin, TX, 78712, United States, ebickel@utexas.edu 1 - DAS Student Paper Award Emanuele Borgonovo, Bocconi University, Milano, Italy, emanuele.borgonovo@unibocconi.it, Robert Hammond The Student Paper Award is given annually to the best decision analysis paper by a student author, as judged by a panel of the Decision Analysis Society of INFORMS. Students who did not complete their Ph.D. prior to May 1, 2015 are eligible for this year’s competition. 2 - DAS Publication Award Kenneth Charles Lichtendahl, University of Virginia, lichtendahlc@darden.virginia.edu This award is given annually to the best decision analysis article or book published in the second preceding calendar year (i.e. calendar year 2014 for consideration in 2016). The intent of the award is to recognize the best publication in “decision analysis, broadly defined.” This includes, but is not limited to, theoretical work on decision analysis methodology (including behavioral decision making and non-expected utility theory), descriptions of applications, and experimental studies. 3 - DAS Practice Award Franklyn Koch, Koch Decision Consulting, kochfg@gmail.com The Decision Analysis Practice Award is awarded to the best example of decision analysis practice as judged by the Decision Analysis Practice Award Committee. The purpose of the award is to publicize and encourage outstanding applications of decision analysis practice. We will present the finalists and this year’s winner. MD44 208B-MCC DAS Awards Session Sponsored: Decision Analysis Sponsored Session

223