2016 INFORMS Annual Meeting Program

MA43

INFORMS Nashville – 2016

MA40 207B-MCC Supply Chain/ Inventory in Applied Probability I Sponsored: Applied Probability Sponsored Session Chair: Yehua Wei, Duke University, Durham, Durham, NC, 27708, United States, yehua.wei@duke.edu 1 - Online Resource Allocation With Limited Flexibility Arash Asadpour, NYU Stern, New York, NY, 10012, United States, aasadpou@stern.nyu.edu, Xuan Wang, Jiawei Zhang We consider a class of online resource allocation problems in which there are n types of resources with limited initial inventory and n demand classes. In this paper, we focus on a special class of structures with limited flexibility, the long chain design, which was proposed by Jordan and Graves (1995) and has been an important concept in the design of sparse flexible processes. We show the effectiveness of the long chain design in mitigating supply-demand mismatch under a simple myopic online allocation policy. In particular, we provide an upper bound on the expected total number of lost sales that is irrespective of how large the market size is. 2 - Inventory Management For Assemble To Order Systems With General Bill Of Materials And Deterministic Lead Times Qiong Wang, University of Illinois at Urbana - Champaign, qwang04@illinois.edu, Martin I Reiman, Haohua Wan We study inventory management for minimizing the long-run average cost in Assemble-to-Order inventory systems. In our previous work, we have developed a stochastic programming based approach that gives rise to a lower bound on the cost objective, and for systems with identical lead times, asymptotically-optimal replenishment and allocation policies. Here we generalize our policies to prescribe a complete solution for systems with general Bill of Materials and general deterministic lead times. Our discussions focus on the design of a novel replenishment policy for the general case and asymptotic optimality of the entire approach. 3 - Managing Multi-period Production Systems With Limited Process Flexibility Yuan Zhong, Columbia University, 500 W. 120th St., Mudd 344, New York, NY, 10027, United States, yz2561@columbia.edu Cong Shi, Yehua Wei We develop a theory for the design of process flexibility in a multi-period production system. We propose and formalize a notion of “effective chaining” termed the Generalized Chaining Condition (GCC). We show that any partial flexibility structure that satisfies GCC is near-optimal under a class of policies called the Max-Weight policies. Furthermore, we show that GCC can be satisfied using just k arcs, where k is the equal to the number of products plus the number of plants. 4 - A Joint-replenishment System With Convex Purchase Costs Paul H Zipkin, Duke University, Paul.Zipkin@Duke.edu We consider the problem of replenishing inventories of several items facing stochastic demands, where the order cost is jointly convex, representing diseconomies of scale such as capacity limits. We want to find a good policy and also to understand the qualitative behavior of the system. To these ends, we show that the system enjoys a property called M-natural-convexity. This is a powerful analytical tool, but it poses some technical challenges.

1 - An Empirical Analysis Of The Centrally Cleared Credit Default Swaps Market W. Allen Cheng, Columbia University, 500 W 120th st, 333 Mudd, New York, NY, 10027, United States, wc2232@columbia.edu In this talk we present an empirical analysis of regulatory Credit Default Swaps data collected by the U.S. Commodity Futures Trade Commission according to Title 17 Chapter 1 part 39 of the Code of Federal Regulations. We discuss salient features of traded contracts, open positions, and margin posting. We also investigate feedback effects in price volatility and intraday margin calls. 2 - Systemic Risk And Liquidity Provision Xu Sun, Columbia University, xs2235@columbia.edu, Agostino Capponi, David D Yao We introduce an interbanking network model in which banks interact with each via borrowning/lending transactions, and can be assisted by a regulator who injects capital into the system to prevent insolvency. The regulated system follows a reflected multi-dimensional Ornstein-Uhlenbeck process with state dependent drift coefficient. We investigate how the concentration of interbanking activities affects the earliest intervention time as well as the market concentration, i.e the heterogeneity of asset value in the system. 3 - Systemic Risk Under Heterogeneous Beliefs Benjamin Bernard, Columbia University, bb2794@columbia.edu Agostino Capponi We consider an interbank network of bilateral exposures, where each bank only knows its own contracts but not the exposures between other pairs of banks. Defaults are costly and may lead to financial contagion that spreads through the network. We model this spread of insolvency as a dynamic game, where banks have the ability to intervene and join a rescue consortium to save the insolvent banks. Banks will do so only if an intervention is incentive compatible given their beliefs on the network. We analyze the set of sequential equilibria in these games with heterogeneous beliefs and contrast the results to the equilibrium outcomes with complete information. 4 - Model Risk In Financial Networks We introduce a financial network model which accounts for uncertainty in the interbank liabilities. We define the systemic loss uncertainty as the difference between the maximum and minimum loss in the financial system. We investigate how the level of interbank intermediation in the financial system impacts systemic loss uncertainty. Our findings indicate the existence of a threshold above which higher intermediation reduces both systemic loss and the resulting uncertainty. When the intermediation level falls below, further increases reduce the minimum and maximum loss, but this comes at the expenses of higher uncertainty in the loss outcome. Disaster Management I Sponsored: Decision Analysis Sponsored Session Chair: Jun Zhuang, University at Buffalo, SUNY, Buffalo, NY, United States, jzhuang@buffalo.edu Co-Chair: Jing Zhang, University at Buffalo, SUNY, Buffalo, NY, United States, jzhang42@buffalo.edu 1 - Linear Programming Input Output Models For Energy Resilience Adam Ng Tsan Sheng, National University of Singapore, isentsa@nus.edu.sg We develop an approach to study the energy supply resilience of an economy using linear programming and economic input-output analysis. In particular, we propose an energy resilience index by examining the maximum level of energy supply reduction that the economy can endure without sacrificing domestic demands. A mixed integer model is developed to compute the resilience index efficiently. The methodology is applied to a case study using China data to study the energy resilience under different generation portfolio assumptions. We demonstrate how our models can be used to uncover important inter-sectoral dependencies. Extensions of the approach to include recovery effects are also presented. Peng-Chu Chen, Purdue University, West Lafayette, IN, United States, chen621@purdue.edu, Agostino Capponi MA43 208A-MCC Decision Analysis, Game Theory, and

MA41 207C-MCC FSS Tutorial Sponsored: Financial Services Sponsored Session

Chair: Rafael Mendoza, McCombs School of Business, Austin, TX, United States, rafael.mendoza-arriaga@mccombs.utexas.edu

MA42 207D-MCC Systemic Risk and Financial Networks Sponsored: Financial Services Sponsored Session Chair: Agostino Capponi, Columbia University, New York, NY, United States, ac3827@columbia.edu Co-Chair: Agostino Capponi, Columbia University, New York NY, United States, agcappo@gmail.com

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