Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

TC08

n TC07 North Bldg 123 Joint Session OPT/Practice Curated: Network Optimization Models and Applications Sponsored: Optimization/Network Optimization Sponsored Session Chair: Larry J. LeBlanc, Vanderbilt University, Owen Graduate School of Mgmt, Nashville, TN, 37203, United States 1 - Fireline Construction in a Heterogeneous Forest Landscape Xu Yang, Northeastern University, 360 Huntington Avenue, Snell Engineering, Boston, MA, 02115, United States, Emanuel Melachrinoudis Most often wildfires are contained with a construction of fireline around the fire perimeter by clearing of combustible material or sufficiently wetting to prevent fire spread. Existing models in the literature assume homogeneous fire environment. This paper presents a novel way of generating an optimal fireline construction through heterogeneous landscape using a network approach. Voronoi Polygons are utilized to represent the homogeneous areas. Dijkstra’s algorithm is used to find the fastest paths at a safe distance for two crews who work simultaneously in opposite directions to encircle and contain the fire. 2 - Locomotive Assignment Problem: Integrating the Strategic, Tactical and Operational Level Aspects Prashant Premkumar, Doctoral Student, Indian Institute of Management Kozhikode, IIM Kozhikode P.O., Kozhikode, 673570, India, Ram Kumar P. N Over the past couple of centuries, with the increase in the significance of the railways to the economy, the complexity of the railway network and consequently the decision making involved has only increased. Among the host of problems in railways management, we attempt to integrate the strategic, tactical and operational level aspects of one of the most important problems, which is the Locomotive Assignment Problem (LAP). We also demonstrate that by innovatively modelling the problem through the addition of a valid equality, the lower bounds can be improved substantially, thereby reducing the solution time. 3 - Blood Bank Mergers from a Supply Chain Perspective: A Case Study Amir H. Masoumi, Assistant Professor of Management, Manhattan College, O’Malley School of Business, Riverdale, NY, 10471, United States, Jan Hoffmann, Min Yu We utilize a supply chain network model to analyze a recent case of merger between two blood banks in California. Our methodological framework evaluates the synergy associated with the merger from a total cost perspective. The proposed model takes into account the operational cost, discarding cost of waste, as well as the potential capacity overage penalty throughout the blood supply chain. For the case study, clustered blood collection zones are considered in addition to aggregated demand regions representing the hospitals served by the two blood banks. Solution to the proposed models yields the optimal link flows and the link capacity overages corresponding to the pre- and post-merger problems. 4 - Societal Networks in Smart City Rupei Xu, The University of Texas at Dallas, Richardson, TX, 75081, United States, Andras Farago In the modern city, societal networks, such as transportation network, communication network, gas pipeline network, delivery network etc., play an important role for the convenience of citizens. Adopting artificial intelligence and Internet of Things (IoT) to societal networks would lead to better technological services. These networks, however, are usually of extremely large scale and vary frequently, thus bringing a lot of new computational challenges. In this research, we apply new computation techniques to handle computational challenges for societal networks in the smart city, providing efficient and practical solutions. 5 - Using Spreadsheet Maps for Heuristic Site-selection Algorithms Larry J. LeBlanc, Vanderbilt University, Owen Graduate School of Mgmt, Nashville, TN, 37203, United States, Michael Bartolacci, Thomas A. Grossman Because of the well-known difficulty of finding exact solutions to large scale 0-1 location models, heuristic algorithms are often used. We show how to use 3D maps in Excel and Tableau to guide the selection of sites to open in a global setting from a large set of potential sites. Formulas for a different customer service radius for each site are given. Possible heuristics are discussed.

n TC08 North Bldg 124A Primal-Dual Methods for Stochastic Optimization Sponsored: Optimization/Nonlinear Programming Sponsored Session Chair: Qihang Lin, The University of Iowa, Iowa City, IA, 52245, United States 1 - Structured Nonconvex Optimization Models: Algorithms and Iteration Complexity Analysis Tianyi Lin, University of California, Berkeley, 407, Cornell Avenue, Unit 11, Albany, CA, 94706, United States, Bo Jiang, Shiqian Ma, Shuzhong Zhang Nonconvex and nonsmooth optimization problems are encountered in statistics, business and engineering, but are not widely recognized as a technology in the sense of scalability. This paper aims to take one step in this direction. Indeed, we consider some constrained nonconvex models in block variables with coupled affine constraints, and introduce the $\epsilon$-stationarity conditions. We develop two proximal-type variants of the ADMM, assuming the proximal updates can be implemented for all the block variables except for the last block, for which either a gradient step or a majorization-minimization step is implemented. We also establish an iteration complexity bound of $O(1/\epsilon^2)$. 2 - Randomized Primal-Dual Algorithms for Semi-Infinite Programming William Haskell, National University of Singapore, Singapore, Singapore This paper presents a novel algorithm for semi-infinite programming which combines random constraint sampling with the classical primal-dual method. We show that this algorithm achieves an O (1/T^(1/2)) and O (1/T^(1/4)) rate of convergence for bounds in probability, in terms of the optimality gap and constraint violation, respectively, where T is the number of iterations. 3 - Iteration Complexity of Collaborative Subgradient Method under Error Bound Condition and Weak Convexity Runchao Ma, The University of Iowa, 21 E. Market St, S380, Pappajohn Business Building, Iowa City, IA, 52242, United States, Qihang Lin Collaborative subgradient descent (CSGD) method is a generic optimization algorithm for general nonlinear constrained continuous optimization. In this work, we consider using the CSGD method for two important classes of non- smooth constrained optimization problems: (1) convex problem satisfying error bound conditions; (2) non-convex problem with weak convexity. We analyze the iteration complexity of the CSGD method for finding an epsilon-optimal or epsilon-stationary point under each scenario, respectively. 4 - Scalable Bilinear $\pi$ Learning using State and Action Features Yichen Chen, Princeton University, Princeton, NJ, 08540, United States, Lihong Li, Mengdi Wang In this work, we develop a scalable, model-free algorithm called bilinear $\pi$ learning for reinforcement learning when a sampling oracle is provided. The algorithm has several advantages. First, it adopts (bi)linear models to represent the high-dimensional value function and state-action distributions, using state and action features. Its run-time complexity depends on the number of features, not the size of the underlying MDPs. Second, it operates in a fully online fashion without having to store any sample, thus having minimal memory footprint. Third, we prove that it is sample-efficient, solving for the optimal policy with a sample complexity linear in the dimension of the parameter space.

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