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Finalists for the 2018 Wagner Prize for Excellence in Operations Research Practice

  • 1.  Finalists for the 2018 Wagner Prize for Excellence in Operations Research Practice

    Posted 08-10-2018 13:13

    Finalists for the 2018 Wagner Prize for Excellence in Operations Research Practice

    INFORMS, the leading international association for professionals in operations research and analytics, has selected six finalists for the 2018 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 strong analytical content, good writing, and verifiable practice successes (

    The competition is held each year in the fall at the INFORMS Annual Meeting, this year it will be on Monday, November 5th in Phoenix AZ.


    The finalist for the 2018 Wagner Prize listed in alphabetical order are:

    Analytics and Bikes: Riding Tandem with Motivate to Improve Mobility

    Bike-sharing systems are now ubiquitous across the U.S. We have worked with Motivate, the operator of the largest such systems, including in New York, Chicago and San Francisco, to innovate data-driven approaches for bike-sharing. With them we have developed methods to improve their day-to-day operations and also provide insight on central issues in the design of their systems. This work required the development of a number of new optimization models, characterizing their mathematical structure, and using this insight in designing algorithms to solve them. In our presentation, we focus on two particularly high-impact projects, an initiative to improve the allocation of docks to stations, and the creation of an incentive scheme to crowdsource rebalancing. Both of these projects have been fully implemented to improve the performance of Motivate's systems across the country: Motivate has moved hundreds of docks in its systems nationwide and the Bike Angels program now aids rebalancing in San Francisco and NYC. In NYC, Bike Angels yields improvement comparable to that obtained through Motivate's traditional rebalancing efforts, at far less financial and environmental cost.

    Centralized admissions for Engineering Colleges in India

    We designed and implemented a new joint seat allocation process for undergraduate admissions to over 500 programs spread across 80 technical universities in India, including the Indian Institutes of Technology (IITs). Our process is based on the well known Deferred Acceptance algorithm, but complex rules regarding affirmative action seat reservations led us to make a number of algorithmic innovations, including (i) a carefully constructed heuristic for incorporating non-nested common quotas that span multiple programs, (ii) a method to utilize unfilled reserved seats with no modifications to the core software, and (iii) a robust approach to reduce variability in the number of reserved category candidates admitted, while retaining fairness. Our new seat allocation process went live in 2015, and based on its success, including significant and provable reduction in vacancies, it has remained in successful use since, with continuing improvements. 

    Collaborative Human-UAV Search and Rescue for Missing Tourists in Nature Reserves

    The use of unmanned aerial vehicles (UAVs) is becoming commonplace in search and rescue tasks in complex terrains. In the literature, there are a number of studies on UAV search with the objective of minimizing search time and/or maximizing detection probability. However, little effort has been devoted to collaborative human and UAV search, which is necessary in many applications where the target has to be ultimately reached by human rescuers. In this paper we present a collaborative human-UAV search planning problem with the aim of minimizing the expected time at which the target is reached by human rescuers. The presented problem is of high complexity, and thus traditional exact algorithms would be very time-consuming or even impractical for solving even relatively small instances. We propose an evolutionary algorithm which uses biogeography-inspired operators to efficiently evolve a population of solutions to find the optimum or a near-optimum within an acceptable time. Computational experiments demonstrate the advantages of our algorithm over a number of other popular algorithms. The proposed method has been successfully applied to two real-world operations for searching and rescuing missing tourists in a nature reserve in China. It is estimated that, compared to the old method used by the organization, our method shortened the time required for reaching the targets by 79 minutes and 147 minutes in the two cases, respectively, providing a great improvement in the life-critical operations.

    Combinatorial Exchanges for Trading Fishery Access Rights

    Overfishing is a prime environmental concern. Catch share systems have recently been shown to be effective tools to combat overfishing. Yet, the allocation of catch shares has always been a challenging policy problem. There is an active discussion about market-based solutions for the allocation and re-allocation of fishery shares. Unfortunately, until yet there have not been adequate market designs to address the specific requirements in these markets. The recent subsidized share trading market in New South Wales (NSW) is a first-of-a- kind market design for the reallocation of catch shares and the largest combinatorial exchange to date. The market design needed to address several non-standard requirements, most importantly the lack of participation and fair payments. While these features were crucial for the adoption of the proposed design, they led to computationally challenging allocation and pricing problems.

    The implemented exchange illustrates how computational optimization and market design can provide new policy tools, able to solve complex policy problems considered intractable only a few years ago. The exchange operated from May to July 2017 and effectively reallocated shares from inactive fishers to those who needed them most. It can provide a template for the reallocation of catch shares in other fisheries world-wide as well.

    Primal-Dual Algorithms for Order Fulfillment at Urban Outfitters, Inc. 

    We formulate the omni-channel fulfillment problem as an online optimization problem. We propose a novel algorithm for this problem based on the primal-dual schema. Our algorithm is robust: it does not require explicit demand forecasts. This is an important practical advantage in the apparel-retail setting where demand is volatile and unpredictable. We provide a performance analysis establishing that our algorithm admits optimal performance guarantees in the face of adversarial demand. We describe a large-scale implementation of our algorithm at Urban Outfitters, Inc. This implementation processes on average eighteen thousand customer orders a day, and as many as one hundred thousand orders on peak demand days. We estimate conservatively that the system has saved at least six million dollars annually relative to an incumbent industry standard fulfillment optimization implementation. This saving is achieved through optimal order-fulfillment decisions that simultaneously increase turn and lower shipping costs.

    What's Wrong with My Dishwasher: Advanced Analytics Improve the Diagnostic Process for Miele Technicians

    Miele, a leading appliance manufacturer, is looking to optimize the ways in which they solve customer problems quickly and efficiently. A crucial part of this task is precise diagnosis of faults, before and during technician visits. A correct diagnosis allows technicians to take with them the necessary parts and complete the repair with a minimal spending of time, effort, and spare parts. We created a decision-support system to help Miele optimize its service process, based on statistics learned from historical data about technician visits, containing both structured and unstructured (textual) data that had to be combined to create the probabilistic model. We used a novel process in which a semantic model informed the creation of the probabilistic model, as well as the analysis pipelines for the structured and unstructured data, combining expert knowledge with large heterogenous data. The results of a pilot study demonstrated a significant improvement of efficiency, concomitant with an increase of an already very high first-fix rate.


    Patricia Neri
    Principal Analytical Consultant
    SAS Institute, Inc.
    Cary NC