INFORMS, the leading international association for professionals in operations research and analytics, has selected six finalists for the 2017 Wagner Prize competition.
The Daniel H. Wagner Prize for Excellence in Operations Research Practice emphasizes the quality and coherence of the analysis used in practice. Dr. Wagner strove for strong mathematics applied to practical problems, supported by clear and intelligible writing. This prize recognizes those principles by emphasizing good writing, strong analytical content, and verifiable practice successes (https://www.informs.org/Recognizing-Excellence/INFORMS-Prizes/Daniel-H.-Wagner-Prize-for-Excellence-in-Operations-Research-Practice)
The competition is held each year in the fall at the INFORMS Annual Meeting, this year it will be on Monday, October 22nd.
The finalist for the 2017 Wagner Prize listed in alphabetical order are:
BNSF Railway's desire to replace the current manual crew planning process with a systematic and efficient approach, motivated his analytical team to develop an optimization model and solution methodology, and implemented a system called Crew Decision Assist (CDA) to support crew scheduling for single-ended districts at BNSF. Crew costs account for a significant portion of train operating expenses, and so effective crew deployment is an important priority for railroad companies. Their approach is novel because it incorporates many practical details in train crew scheduling and accounts for uncertainty in train schedules. CDA interfaces with existing systems to quickly generate effective crew deployment plans, and is expected to yield cost savings of several million dollars per year.
Researchers from Duke University and MIT presented the entry Optimized Scoring Systems: Towards Trust in Machine Learning for Healthcare and Criminal Justice. Questions of trust in machine learning models are becoming increasingly important, as these models are starting to be used widely for high-stakes decisions in medicine and criminal justice. Transparency of models is a key issue affecting trust. The topic of transparency in modeling is being heavily debated in the media, and there are conflicting laws on the use of black box models between the European Union and the United States. This paper reveals that: (1) There is new technology to build transparent machine learning models that are often as accurate as black-box machine learning models. (2) These methods have had impact already in medicine and criminal justice. This work calls into question the overall need for black box models in these applications.
Georgia Tech, Grady Health Systems and Morehouse School of Medicine worked on a drug-effect-based personalized approach to improve treatment outcome for diabetic patients. First, a pharmacokinetic and pharmacodynamics (PK/PD) model is established to uncover drug effect based on analysis of anti-diabetic drug dosage and the blood glucose level recorded in the titration period of each patient. This personalized evidence is then utilized within a treatment plan model that optimizes the glycemic control and drug dosage. The optimized plans provide better glycemic control while using less drug.
Lehigh University developed the inmate assignment project, in close collaboration with the Pennsylvania Department of Corrections. This project took five years from start to successful implementation. Their novel Inmate Assignment Decision Support System (IADSS) is designed with the main goal of simultaneously, and system-wide optimally, assigning the inmates to the correctional institutions. IADSS includes a new hierarchical multi-objective MILO model, which accurately describes the inmate assignment problem. This is the first time that OR methodologies have been used to optimize the operations, and built into the routine business practice, of a correctional system, thus it opens a rich and untouched area for the application of OR.
All modes of freight transportation are subject to flow imbalances that impact the efficiency of asset utilization. The use of Mathematical Programming optimization models has a rich history of application to this problem. Schneider National Incorporated addressed a particularly difficult variant of this problem that occurs in bulk chemicals transport. This difficulty is created by a large volume of activity, requiring 1,000 tractors and 1,600 tankers, coupled with complex product-sequencing constraints, tanker wash and preparation processes, and driver work rule constraints. To address this problem the engineering group at Schneider has designed and implemented a multi-phase, multi-dimensional matching algorithm and associated software system that enables business planners to leverage optimized solution recommendations.
Turner Broadcasting System developed and implemented innovative, integrated forecasting and optimization models that forecast audiences in the 24/7 programming schedule, generate media plans across all Turner's networks, and schedule commercials holistically. These methods power Turner's audience targeting solutions: TargetingNOW and AudienceNOW, which have produced significant sales and advertisement efficiencies for Turner and its clients, and moved the industry forward in the emerging audience targeting landscape.
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Patricia Neri
Principal Technical Consultant
SAS Institute, Inc.
Cary NC
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