2016 INFORMS Annual Meeting Program

POSTER COMPETITION

INFORMS Nashville – 2016

Using Anom Slicing For Multiway Models With Binomial Or Poisson Data

Evaluation Of Low Carbon Level Of Enterprise Logistics Based On Improved Entropy Law And Cloud Model Mi Gan, Southwest Jiaotong University, Chengdu, China. migan@swjtu.cn, Xiaofan Guo, Shuai Yang, Lei Wang To evaluate the low-carbon level of logistics enterprise, we construct corresponding evaluation index system and introduce the concept of centrality in DEMATEL method to improve the entropy method, use data variability to do objective weighting of evaluation index. Then combine with cloud model theory, use reverse cloud generator to convert evaluation data into cloud parameters, and use index approximation method to construct evaluation cloud, reflect and obtain the evaluation results in the form of normal cloud chart, to solve fuzziness and randomness quantification in the evaluation process. Exhibit Hall Monday Poster Competitition Competition Poster Session A Simple Classification Framework For Discrimination Of Antipsychotic Treatment Resistant And Treatment Responsive Schizophrenia Patients Farnaz Zamani Esfahlani, PHD Candidate, SUNY Binghamton, 4400 Vestal Parkway East, Binghamton, NY, 13902-6000, United States, fzamani1@binghamton.edu, Katherine Frost, Gregory Strauss, Hiroki Sayama Predicting the outcome of different treatment options for patients is essential for effective treatment planning. However, this is a challenging task especially in mental disorders such as schizophrenia where the treatment outcome of patients depends on the complex interaction of various symptoms. In this study, we used analytical tools of network science to study the interaction of the symptoms in schizophrenia and identify symptoms that best discriminate antipsychotic treatment resistant and treatment responsive schizophrenia patients. The features selected based on network analysis provided better classification accuracy when compared to traditional feature selection methods. Anticipatory Dynamic Traffic Sensor Location Problems with Connected Vehicle Technologies John (Hyoshin) Park, Research Associate, University of Massachusetts Amherst, 370 Northampton Rd. Apt B, Amherst, MA, 01002, United States, hyoshin0724@gmail.com, Song Gao, Ali Haghani Despite the potential benefits of sensor technologies, the challenges associated with identifying optimal sensor locations for multiple time stages throughout a day with uncertain demand patterns has received little attention. In this paper, we focus on proactively reducing the network delay by controlling traffic signals through an optimized sensor deployment. The framework is based upon portable sensors that may be repositioned within the day to new locations such that delay savings over multiple time stages will be maximized. To tackle this multi-period stochastic problem, dynamic models are proposed, considering the future sensor locations given budget constraints on the sensor costs and relocation costs. A subproblem decomposed by Lagrangian relaxation enhanced with valid cuts has a better bound and a variable neighborhood search algorithm quickly finds solutions. Two dynamic models that constrain a flexible or restricted relocation present higher savings compared to the stationary model without sensor relocation. The flexible relocation model guarantees higher savings than restricted model by achieving the same maximum savings with fewer number of sensors. Resilient Offgrid Microgrids – Capacity Planning And N-1 Security Sreenath Chalil Madathil, Clemson University, 324 Village Walk Ln, Clemson, SC, 29631, United States, schalil@g.clemson.edu Scott J. Mason, Russell Bent, Harsha Nagarajan, Emre Yamangil, Scott Backhaus, Arthur Barnes, Salman Mashayekh, Michael Stadler Despite the long distance power transmission capabilities, there are some remote communities in Alaska and Hawaii that are not connected to these systems. These communities rely on small, disconnected microgrids to deliver power. These microgrids are not held to same reliability standards as transmission grids and can place many communities at risk for extended black-outs. To address this issue, we develop an optimization model and algorithm for capacity planning and operations of microgrids that includes N-1 security and other modeling features. The effectiveness of the approach is demonstrated using the IEEE 13 node test feeder and a model of the Nome, Alaska distribution system. Poster Competition

peter wludyka, CEO, Wludykaandassociates, 4285 Baltic Street, Jacksonville, FL, 32210, United States, pwludyka@unf.edu, John Noguera Results from “Using ANOM Slicing for Multi-Way Models with Significant Interaction” (Wludyka, JQT 2015) are extended to binomial and Poisson data. Analysis of Means (ANOM) slice charts using an overall estimate for the proportion/rate (which will often have more power than by analysis charts) are presented. Data-driven Decision Making In The Sharing Economy Qiaochu He, Assistant Professor, University of North Carolina, Charlotte, 9201 University City Blvd., Univ. North Carolina, Charlotte, NC, 28223, United States, qhe4@uncc.edu Yun Yang In this presentation, we propose several new models related to the sharing economy. We investigate this industry from a data-driven perspective, and focus on the following issues: (1) Delayed matching mechanism in a two-sided market; (2) The value of forecasting in matching with uncertainty; (3) Emerging service mechanism in the sharing economy. Optimization Problems In The Design And Control Of Internet Fulfillment Warehouses Sevilay Onal, PhD Candidate, New Jersey Institute of Technology, Internet Fulfillment Warehouses (IFWs) are facilities that have been designed and built exclusively to process online retail orders. The nature of e-commerce is described with a high number of transactions in small quantities. Therefore, human controlled systems are being replaced with total digital control. Thus, a revision is required for existing methodology such as adoption of pick and storage policies, resource allocation strategies, warehouse design and control. We aim to introduce the operational and design environment of IFWs and summarize associated emerging optimization problems Container Port Selection In West Africa A Multicriteria Decision Analysis Rivelino De Icaza, PhD Candidate, University of Arkansas, 1301 N Prairie Dunes Trail, Apt 305, Fayetteville, AR, 72704, United States, rdeicaza@uark.edu West Africa gross domestic product is expected to grow to 6.2 percent in 2016 and port capacity will increase by over 12 million TEUs by 2020. Despite the region economic potential and the steady grow of container traffic over the years, port selection decision by shipping lines is complex because the region still has poor shipping infrastructure and political instability that impact transportation security, and consequently the logistics and supply chain services. This research applies a multiattribute value theory (MAVT) with valued-focused thinking (VFT) and an alternative-focused thinking (AFT) methodologies to develop a shipping lines’ container port selection decision models. Dynamic Data-driven Physician Rostering Under Variable Availability Monique Bakker, City University of Hong Kong, Hong Kong SAR, Hong Kong. mbakker2-c@my.cityu.edu.hk, Kwok L. Tsui Efficient staff rostering and patient scheduling to meet outpatient demand is a very complex and dynamic task. Medical specialists are typically restricted in sub- specialization, serve several patient groups and are the key resource in a chain of patient appointments at the outpatient clinic, endoscopy unit, and surgical unit. We present a new, data-driven algorithmic approach to automatic allocation of specialists to activities and patient groups. This approach minimizes variability in specialist activity rosters. It outperforms traditional cyclic scheduling with increased patient service level (% patients served in time) and capacity utilization, and decreased patient wait time (days). How Can Mathematical Modelling Quantify Future Fishing Risks Under Climate Change Scenarios? Sara Rezaee, Dalhousie University, Halifax, NS, Canada, BC, Canada. sara.rezaee@dal.ca, Ronald P. Pelot, Christian Seiler, Alireza Ghasemi Studies have shown that extreme weather factors can affect fishing safety significantly. Changes in weather patterns due to climate change effects will add uncertainty to fishing safety systems. This study proposes a framework to quantify fishing incident risks in the future due to changes in weather conditions. The framework builds relationships between fishing incidents and weather conditions based on historical data using mathematical modelling and data mining techniques and then predicts future risks according to these relationships with respect to potential changes in weather patterns. 323 Dr Martin Luther King Jr Blvd, Newark, NJ, 07102, United States, so59@njit.edu, Jingran Zhang, Sanchoy Das

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