2016 INFORMS Annual Meeting Program

POSTER SESSION

INFORMS Nashville – 2016

Inference For The Progressively Type-I Censored Step-stress ALT Under Interval Monitoring David Han, University of Texas at San Antonio, Management Science & Statistics, College of Business, San Antonio, TX, 78249- 0632, United States, david.han@utsa.edu Tianyu Bai A step-stress ALT under progressive Type-I censoring was considered when a continuous monitoring of failures is infeasible but inspections at certain time points is possible. In addition to the accelerated failure time model, a general scale family of distributions was considered for flexible modeling. The MLE of scale parameters and their conditional densities could be derived explicitly. Using the exact distributions of the estimators, confidence intervals for the parameters were obtained and numerically assessed. A Probabilistic Unit Commitment Model Kenneth Bruninx, KU Leuven, Celestijnenlaan 300, Post Box 2421, Leuven, B3001, Belgium, kenneth.bruninx@mech.kuleuven.be, Erik Delarue Stochastic unit commitment models allow calculating an optimal trade-off between the cost of scheduling and activating reserves, load shedding and curtailment, but may become computationally intractable for real-life power systems. Therefore, we develop a probabilistic unit commitment (PUC) formulation, which allows internalizing the reserve sizing and allocation in a deterministic unit commitment problem, considering the full cost of reserve allocation and activation. This PUC formulation yields UC schedules that are nearly as cost-effective as the theoretical optimal solution of the stochastic model in calculation times similar to that of a deterministic equivalent. Understanding Vehicle Movement Patterns With Artificial Neural Networks Burak Cankaya, Lamar University, 13960 Hillcroft St, Apt 724, Houston, TX, 77085, United States, mbcankaya@gmail.com This research evaluates the question if we can understand the vehicle movement patterns depending o their (Geographic Identification Systems)GIS data. The research propose to classify the vehicle movements as the work they are doing with machine learning algorithms. The results label the vehicle movement states and make it possible to evaluate the performance of the vehicles which is an essential need for work vehicles, personal devices and vehicles, and vessels. The poster also explains the data preparation and the algorithms such as decision tree, random forest, and neural networks to classify the geospatial data. Algorithms For Identifying Optimal Inspection Paths In Pipe Networks Thomas Ying-Jeh Chen, University of Michigan, 1780 Broadway Street, Apt S128, Ann Arbor, MI, 48105, United States, tyjchen@umich.edu, Seth Guikema The inspection of aging water distribution pipes is an important process for utilities. Due to limitations on inspection capabilities (~2% length of system is typically inspected annually), an optimization process is needed to suggest inspection paths. This paper examines the use of 3 algorithms (Genetic Algorithm, Simulated Annealing, Greedy Search) in finding paths that maximizes high risk pipes being inspected while reducing the number of pipe feature changes (material, diameter etc.). The algorithms were applied to a grid network and a virtual water distribution network. Both cases demonstrated genetic algorithms were the most effective in identifying strong candidates for inspection. Properties Of Location Based Social Networks And Travelers Destination Choice Ying Chen, Research Assistant Professor, Northwestern University, 600 Foster St, Evanston, IL, 60208, United States, y-chen@northwestern.edu, Hani S. Mahmassani, Fei Zhao The aim of this study is to investigate the relationship between friendship and distance, the possible influence of friendship in travelers’ destination choices, and the importance of this factor in choosing a destination. By analyzing social network properties of two Location based Social Networks (LBSNs), the characteristics of LBSNs are identified. Results show that in general, the distance has the strongest influence on travelers’ destination choices, followed by personal preference and social influence from their friends on-line. For users whose friends are in geographical proximity to each other, a possible synchronization characteristic amongst individuals is investigated. Stadiums And Contraband: A Study On Metal Detectors In The Field Nelson Christie, Rutgers University, Princeton, NJ, United States, christie.l.nelson.phd@gmail.com Sports stadiums are increasingly using walk-through metal detectors for patron screening. We utilized experimental design to understand detection rates of real contraband items. These items were identified through interviews of various subject matter experts. Experiments were carried out on machines borrowed from stadium venues. We also created a testing scheme for the metal detectors to ensure functionality prior to events.

Assessing Uncertainty: A Model-output Oriented Approach Achim Czerny, Dr, Hong Kong Polytechnic University, Hong Kong, Hong Kong, achim.czerny@polyu.edu.hk Erik T. Verhoef, Anming Zhang The present paper develops the concept of continuous uncertainty types, which are defined by the extent to which uncertainty affects the firm’s optimized price markups and quantities (i.e., “model outputs”). We show that this model-output orientation can cover scenarios where additive, multiplicative and many more stochastic structures all occur with positive probabilities. This approach allows a compact assessment of the impacts of uncertainty. We further show that the optimal inventory level, and the composition of inventory in terms of the number and size of production units, depend strongly on the type of uncertainty and its distribution as defined according to our theory. Explaining Energy Bonds’ Option-adjusted Spread (OAS) Using Multiple Exponential Regression Models Yan Deng, PhD Student, Cornell University, Cornell University, 2406 Hasbrouck Apartment, Ithaca, NY, 14850, United States, yd256@cornell.edu In order to explain the OAS of corporate energy bond, we developed exponential regression models for prediction. We found that as the oil prices drop, the OAS of energy bonds widen significantly. The sensitivity of OAS to oil price varied among energy subsectors in line with leverage. In addition, adding treasuries yield predictor could significantly increase the predicting accuracy. In particular, these two variables can explain 87.4%, 75.6%, 86.5%, 64% and 84.4% of credit spread changes for independent, integrated, midstream, oil field, and refining energy bonds respectively. Our predictive model provided a tool to monitor risk and signal rich or cheap bonds as potential buy/sell candidates. Deep Learning For Sleep Assessment Skyler C Devine, University of Tennessee - Knoxville, Knoxville, TN, 37916, United States, sdevine2@vols.utk.edu Based on the physiological and neurological features, sleep is divided into two main types: Rapid Eye Movement, and non-rapid eye movement. NREM sleep consists of three stages, stages 1-3. Brain activity during sleep stochastically alternates between stages. In order to judge sleep, clinicians record the electrical activity of the brain through an electroencephalogram, and visually inspect the results to classify them into the three stages. This process is referred to as “sleep scoring”. We apply a deep learning algorithm to automatically score sleep and provide monitoring of sleep quality. Inventory Placement Supply Chain With Two Competing Retailers Yi Ding, Southeast University, Sipailou 2, Jiangsu Province, Nanjing, 210096, China, emdy@seu.edu.cn This study examines service time competition in the context of inventory and environmental constraints. We first discuss the case of a downstream duopoly market without regulator, and then we extend the model by including a regulator that is dedicated to carbon emission abatement. We analyse how service time can be affected internally through inventory placement and externally through market competition as well as government regulation of carbon emissions. The results suggest that although expedited service requires higher safety stock, increasing unit inventory holding cost does not seem to slow down service, nor does imposing higher carbon tax. Decision Support Model To Planning A Mobility Scheme For Critical System Services In Urban Networks With Natural Interruptions Andrea Margarita Ditta, Universidad del Norte, km 5 Antigua vía Puerto Colombia, Barranquilla, Colombia, dittaa@uninorte.edu.co, Ruben Yie, Gina Galindo This work aims to design a Decision Support Model (DSM) to planning a mobility scheme in emergency scenarios in urban networks. The DSM seeks to evaluate the transportation between points of incidents and points of care. The research is focused in the area of humanitarian logistics considering natural interruptions like streams, storms, downpours, among others. We undertake emergency response systems with critical services. Fire brigade, police force requirements or urgent medical attention, are examples of critical services.We hope to increase the efficiency in dealing with emergencies, by decreasing attention times and risk of accidents. Supermarket Optimization: Simulation Modeling And Analysis Of A Grocery Store Layout Jessica Peggy Dorismond, University at Buffalo, 3028 Elmwood Avenue, Buffalo, NY, 14217, United States, jpdorism@buffalo.edu This is a study on how to optimize the layout of a supermarket in order to increase its gross profit via the maximization of impulse sales. In most supermarkets many items often get unnoticed because on average customers only walk one-third of the store. Recent advances in marketing research reveal that encouraging customers to walk longer paths can often increase spending because they are exposed to more products. Retailers can then increase their sales by using the store layout—i.e., the design of the aisles and the product location—to extend the customers’ shopping paths and thus indirectly motivate them to purchase items that are not originally on their shopping list.

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