UPS George D. Smith Prize

The UPS George D. Smith Prize is created in the spirit of strengthening ties between industry and the schools of higher education that graduate young practitioners of operations research. INFORMS, with the help of CPMS, will award the prize to an academic department or program for effective and innovative preparation of students to be good practitioners of operations research, management science, or analytics.

The 2017 finalists will compete on Sunday, April 2, they are:

 12:30–1:30pm Operations Research ProgramUnited States Air Force Academy

 1:50–2:50pm Haslam College of Business MSBA, University of Tennessee

 3:10–4:10pm Institute for Advanced AnalyticsNorth Carolina State University


2016 INFORMS Prize Winner

General Motors
Michael Harbaugh, Jonathan H. Owen, CAP


General Motors, 2016 INFORMS Prize Winner, will survey its sustained application of analytics and operations research. Highlights will include:

Vehicle Health Management: using advanced analytics to predict failure of certain automotive systems before customers are affected,
Optimizing New Vehicle Inventory: determining first how much, and second what mix of vehicles to hold in dealer inventory, and
Revenue Management for Vehicle Content and Packaging: leveraging customer preferences to package and price vehicle content that will sell best.


2016 Wagner Prize Winner Reprise

Calibrated Route Finder – Social, Environmental And Cost-effective Truck Routing

Finding the best route with many conflicting objectives is very difficult. The online system Calibrated Route Finder has been developed in collaboration among many companies and organizations and successfully addresses the problem. A key component is an inverse optimization process that establishes more than 100 weights to balance social values, environmental impacts, traffic safety, stress, fuel consumption, CO2 emissions, and costs. In addition, methodological and analytic developments now enable measurement and inclusion of perceived hilliness and curviness as well as strict rules where to drive. The system has been in operations since 2009 and is today used by about 100 companies.


INFORMS OR & Analytics Student Team Competition

Finalist Competition – Conference attendees invited!
Monday, 9:10am – 12:45pm, Salerno Room

Eight student teams from five countries across the world will compete as finalists in the inaugural INFORMS OR & Analytics Student Team Competition. This new INFORMS prize recognizes outstanding solutions to real-world problems developed by undergraduate and master’s student teams. First Prize is $7,500.

A panel of 18 academic and industry experts judged written submissions to select the eight finalists; the eight teams will then be judged on their oral presentations and written entries on Monday. Winners will be announced at the conference luncheon on Tuesday.

Finalist presentations:
9:10am – University of Cincinnati, U.S.
9:35am – University of Warwick, Nottingham University, Cardiff University; UK
10:00am – Université Catholique de Louvain (Team 1), Belgium
10:30am – Ozyegin University, Turkey
10:55am – National University of Singapore, Singapore
11:30am – Université Catholique de Louvain (Team 2), Belgium
12:00pm – University of North Carolina at Chapel Hill, U.S.
12:25pm – Drexel University, U.S.

For this inaugural year, the business problem was supplied by Syngenta, one of the world’s leading biotech companies. Syngenta is serving as Host Sponsor, generously supporting the competition with funding as well as providing the problem statement and company data. Twelve technology companies offered complimentary access to their software and are named as software sponsors: AMPL, FICO, Frontline Solvers, GAMS, GUROBI Optimization, LINDO Systems, MathWorks, Palisade, SAS, Simio, SIMUL8, Tableau.

To find out more:


Innovative Applications in Analytics Finalist

Wes Chaar, Turner Broadcasting System, Inc.
José Antonio Carbajal, Turner Broadcasting System, Inc.
Peter Williams, Turner Broadcasting System, Inc.

Audience Targeting Solutions Powered by Advanced Analytics

For decades, television advertisement deals had been guaranteed using only primary demographic metrics specified by age and gender. In the last few years, data fusion has allowed viewership data to be fused with frequent shopper card data, credit card data, or even custom surveys to construct targeted TV audience segments such as “cereal buyers” or “auto intenders.” These new, more granular audience segments have challenged traditional ways of forecasting, scheduling, and managing inventory in the media industry. Through advanced analytics, Turner has taken the lead in offering targeted ad products that better address the needs of our advertiser and agency partners.

Turner has developed two core audience targeting solutions: TargetingNOW and AudienceNOW. TargetingNOW takes an existing advertising deal, which is still guaranteed on demographic viewership and maintains its original media mix, and optimizes its spot placements to increase the delivery of a secondary targeted segment. AudienceNOW relaxes many, but not all, of the traditional mix constraints to produce fully optimized deals and spot placements that maximize targeted audience delivery across the entire portfolio of Turner’s networks.

A suite of advanced analytics tools power these targeting solutions. On the descriptive side, analysis and visualization tools present detailed historical or predictive information for particular segments and allow comparison of Turner networks against competitors. On the predictive side, a scalable, accurate, and data-agnostic forecasting approach called Competitive Audience Estimation can build granular estimates for virtually any audience segment. Finally, on the prescriptive side, large-scale, mixed-integer-programming models optimize deal composition and spot placements.


Innovative Applications in Analytics Finalist

Valentina Ferretti, London School of Economics and Political Science

How to Regenerate Disused Railways? An Integrated Analytics Approach

Inactive railway lines and, together with them, disused station buildings, constitute an increasingly important heritage asset and are thus becoming the focus of regeneration processes worldwide. However, the decision of what to do in order to reuse abandoned railways represents a complex decision making problem, involving heterogeneous impacts and multiple stakeholders leading to conflicting objectives. Such a context calls for the use of analytics able to support transparent, replicable and justifiable processes/results.

This project proposes a combination of different analytics to effectively support collaborative decision making processes where a decision has to be made among competing options. In particular, the study developed and tested an integrated analytical approach by combining:
– preference elicitation analytics with visualization analytics in the descriptive phase of the process;
– sensitivity analysis with visualization analytics in the predictive phase of the process;
– prescriptive decision analysis with facilitated modelling throughout the whole process.

The proposed framework has been tested on a real case study dealing with transportation systems’ planning in Northern Italy, where several passenger railway lines have recently been abandoned and replaced by bus services.
The main objective of the study was to investigate which role integrated decision analytics can play to support heterogeneous impacts’ aggregation in territorial planning, by discussing in particular the operability, the applicability and the transparency of the developed methodological framework.

The contribution brought by the study is twofold and refers to: (i) improved operability of the proposed tools obtained by combining visualization analytics with consolidated preference elicitation protocols for assessing multiple impacts and (ii) the provision of a replicable working tool for policy makers.


Innovative Applications in Analytics Finalist

Tori Rollings, Caterpillar

Assurance of Supply Center – Excellence through Supply Network Optimization

Caterpillar’s Assurance of Supply Center (ASC) combines world class supply network modeling and monitoring of past, present and future conditions throughout a global network of suppliers, assembly locations, dealers and customers.  This gives Caterpillar a strategic advantage through complete situational awareness to issues that would otherwise impact complete, timely, and profitable product delivery.  In this session we will examine the radical change in supply network performance management made possible by Caterpillar’s ASC initiative, and discuss some of the critical obstacles faced and opportunities discovered on our journey from concept to ongoing deployment.



Syngenta Crop Challenge in Analytics

The 2017 Syngenta Crop Challenge in Analytics focuses on a different decision maker – the seed retailer. The seed retailers sell soybean seed varieties to the farmers. The farmers require different soybean seed varieties based on expected growing conditions. Thus, seed retailers are expected to have available the right supply of soybean seed varieties to meet the demand of farmers. In other words, to maximize yield in a region with a large number of farmers, the retailers need to predict and stock the soybean variety seeds that will thrive best in the farmer’s most common growing conditions. It is difficult for a seed retailer to predict which seed varieties to stock almost a year in advance to the soybean crop planting by the farmers.

Congratulations to the 2017 Finalists!

9:10–9:40am “A Decision Making Approach for Soy Seed Variety Selection via Hedging Against Weather Risk” Zhongshun Shi, Peking University/University of Wisconsin-Madison

9:50–10:20am “Portfolio Optimization for Seed Selection in Diverse Weather Scenarios” Oskar Marko and Marko Panic’, University of Novi Sad

10:30–11:00am “Seed Stocking Via Multi-Task Learning” Wenjun Zhou, University of Tennessee

 11:10–11:40am “Soybean Portfolio Selection with LASSO Model Averaging and Integer Linear Programming” Benjamin Harlander and Taylor Thiel, Illinois University

11:50am–12:20pm “A Hierarchical-Ensemble of Machineries to Optimize the Choice of Soybean Varieties” Durai Sundaramoorthi, Washington University of St. Louis