INFORMS 2021 Program Book

INFORMS Anaheim 2021

SB14

talk proposes a sliding scale between expressivity and classical optimization. On one side, an ansatz may have high expressivity and theoretical performance, but difficult classical optimization. On the other it may have low expressivity and performance, but easy classical optimization. In the middle, a good hybrid algorithm balances between the two. Such a “classically optimal” hybrid algorithm may best utilize both classical and quantum resources by precomputing problem instance-specific circuits to increase expressivity, and draw inspiration from classical algorithms for classically-derived guarantees. By maximally leveraging both classical and quantum resources, these algorithms may be our first instance of quantum advantage. What is left to do is a creative merger of classical optimization algorithms, and quantum variational circuits. 3 - On the Structure of DD-representable MIPs with Application to Unit Commitment Hosseinali Salemi, Iowa State University, Ames, IA, United States, Danial Davarnia Over the past decade, a powerful solution framework called Decision Diagrams (DDs) was introduced and successfully employed to solve integer programs. However, the question on possibility of extending DDs to model mixed integer programs (MIPs) has been unanswered. In this talk, we first address this question by providing both necessary and sufficient conditions for a general MIP to be modeled by DDs, and then present a DD-based method to model and solve general MIPs. To show the practicality of our framework, we apply it to a stochastic variant of unit commitment problem. Computational experiments show that the proposed method improves the solution times in comparison to the outcome of modern solvers. SB14 CC Room 201B In Person: Complex Systems Modeling and Decision Making General Session Chair: Arvind Krishna, Georgia Institute of Technology, Atlanta, GA, 30318-5599, United States 1 - Does When and How Matter? Information Disclosure Strategy in Online Crowdfunding Yoonseock Son, University of Notre Dame, Notre Dame, IN, United States Crowdfunding has become an important financing model to help project creators get financial support from backers at an early stage. Most of the time, product quality is unknown to the backers, and this information asymmetry issue often leads to the failure of crowdfunding campaigns. To reduce the uncertainty of the backers, project creators can disclose project updates throughout the process. This study examines when and how the information disclosure timing influences the success of the crowdfunding project. Moreover, text analysis is conducted to understand the impact of information richness and content similarity on the funding results. 2 - A Regression-optimization Framework for Sequential Decision-making Long Vu, IBM T.J. Watson Research Center, Yorktown Heights, NY, United States, Pavan Murali This talk focuses on system-wide planning problems, wherein regression models are used to capture the dynamic behavior of various subcomponents. We model system dynamics using piecewise linear regression models, neural networks and random forests, and formulate the planning problem as a mixed-integer linear program that can additionally consume system and flow-based constraints. We demonstrate the use of this regression-optimization framework in generating policies that optimize system output, as well as in sequentially refining the policy trajectory by controlling for prediction error propagation.

SB12 CC Room 304D In Person: Emerging Traffic Management Techniques in Manned and Unmanned Aviation System General Session Chair: Ang Li, University of California-Berkeley, Berkeley, CA, 94720- 2392, United States 1 - Optimization Models for Flights Arrival Scheduling Incorporating Carrier Preferences Yeming Hao, University of Maryland-College Park, College Park, MD, 20740-3161, United States David J. Lovell, Michael O. Ball, Sergio Torres This study presents results of a simulation of strategies to incorporate business- driven airline preferences in Time-based Flow Management metering operations. Traffic flow systems that balance demand versus capacity at airports assign Controlled Times of Arrival (CTAs) to incoming flights. We evaluate optimization models and heuristics to assign these CTAs based on user-provided information and priority preferences in a way that minimizes the total CTA delay cost. We quantify potential savings by comparing the results with the default first-come- first-served (FCFS) scheme. Simulations under a variety of realistic scenarios show that our proposed heuristic could reduce CTA delay costs between 20% and 30% relative to the FCFS baseline scheme. 2 - Using Flight Shifting to Mitigate Delay in Multiple Airport Regions Ang Li, University of California-Berkeley, Berkeley, CA 94720-2392, United States, Mark M. Hansen, Bo Zou This study aims to improve operational performance of a multiple airport region (MAR) by analyzing interdependent capacity scenarios of that MAR airports and redistributing airport traffic to make more efficient use of the available capacity. We identify MARs based on temporal distance between airports. Capacity interdependence in MAR is demonstrated by conducting clustering analysis on daily capacity profiles. Flight shift models are proposed in both tactical and strategic levels to reduce flight delays of all flights serving airports in the same MAR. Results show that by rescheduling flight landing airport and landing time, the total flight delay in the New York MAR could be significantly reduced in both models. SB13 CC Room 201A In Person: Global Optimization for MINLPs and its Applications General Session Chair: Harsha Nagarajan, Los Alamos National Laboratory, Los Alamos, NM, 87544-2747, United States 1 - Uncertainty Measures and Hierarchical Acquisition Functions for Tree-based Black-Box Optimization Alexander Thebelt, Imperial College London, London, United Kingdom, Robert M. Lee, Nathan Sudermann-Merx, David Walz, Ruth Misener Our recent work uses tree-based models, e.g., gradient-boosted trees, to optimize black-box functions with various input data types, e.g. discrete and categorical. Off-the-shelf solvers can globally optimize acquisition function containing such models. This presentation extends our existing approach ENTMOOT by proposing discrete uncertainty measures for search-space exploration that natively integrate with tree-based models. Moreover, we utilize hierarchical acquisition functions for usage in Bayesian optimization explicitly leveraging global solvers for simplified hyperparameter tuning. 2 - Post-QAOA Variational Quantum Algorithms: Balancing Classical Optimization and Quantum Expressivity Joseph John Wurtz, Tufts University, Medford, MA, 68134, United States Recently, variational quantum algorithms (VQA) have come under intense study as a means of using tomorrow’s near term quantum devices for practical quantum advantage. Under the VQA approach, solutions to combinatorial optimization problems are encoded into the Hilbert space of some variational wavefunction ansatz, then parameters are classically optimized to yield good approximate solutions. Several ansätze have been proposed, most prevalently the quantum approximate optimization algorithm (QAOA) and quantum machine learning (QML) algorithms. The QAOA uses a repeated application of two unitaries, and is exact in the infinite depth limit. However, recent results suggest that the experimentally feasible low-depth regime has poor performance due to restrictions of locality and under-expressivity. Conversely, the more general ansätze of QML algorithms has the opposite problem: by being far more expressive, they may easily access good approximate solutions, but classical optimization may be difficult through phenomena such as barren plateaus. This

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