2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
135
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,
1 - Online Resource Allocation With Limited Flexibility
Arash Asadpour, NYU Stern, New York, NY, 10012, United States,
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,
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,
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,
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.
MA41
207C-MCC
FSS Tutorial
Sponsored: Financial Services
Sponsored Session
Chair: Rafael Mendoza, McCombs School of Business, Austin, TX,
United States,
MA42
207D-MCC
Systemic Risk and Financial Networks
Sponsored: Financial Services
Sponsored Session
Chair: Agostino Capponi, Columbia University, New York, NY,
United States,
Co-Chair: Agostino Capponi, Columbia University, New York NY,
United States,
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,
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, ,
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,
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
Peng-Chu Chen, Purdue University, West Lafayette, IN,
United States, Agostino Capponi
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.
MA43
208A-MCC
Decision Analysis, Game Theory, and
Disaster Management I
Sponsored: Decision Analysis
Sponsored Session
Chair: Jun Zhuang, University at Buffalo, SUNY, Buffalo, NY,
United States,
Co-Chair: Jing Zhang, University at Buffalo, SUNY, Buffalo, NY,
United States,
1 - Linear Programming Input Output Models For Energy Resilience
Adam Ng Tsan Sheng, National University of Singapore,
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.
MA43