Informs 2015

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INFORMS Philadelphia – 2015
480
WE04
04-Room 304, Marriott
Inventory Management - Stochastic Demand
Contributed Session
Chair: Nicholas Leifker, St. John Fisher College, 3690 East Ave.,
Rochester, NY, 14618, United States of America,
1 - Managing Inventory for a Stochastic and a Deterministic
Demand Stream
Rob Basten, Eindhoven University of Technology, P.O. Box 513,
Eindhoven, 5600MB, Netherlands, ,
Jennifer Ryan
We consider a stock point for an item that observes two streams of demands. Our
motivating example is the maintenance of capital assets. The low priority demand
is observed before parts need to be ordered and thus exhibits perfect advance
demand information (e.g., preventive maintenance), while the high priority
demand is observed afterwards (e.g., corrective maintenance). We characterize
the structure of the optimal inventory control policy and we propose a myopic
heuristic policy.
2 - Percentile Threshold Policies for Inventory Problems with Partially
Observed Markovian Demands
Parisa Mansourifard, PhD Candidate, University of Southern
California, 1820 E Del Mar Blvd., Pasadena, CA, 91107,
United States of America, , Tara Javidi,
Bhaskar Krishnamachari
We consider the case of partially observed demand in the context of a multi-
period inventory problem with lost sales. We present an interesting class of
policies with a percentile threshold (PT) structure which outperforms the myopic
policy and performs close to the optimal policy. We derive the performance
guarantee of PT policies and present the optimal PT policy with a reasonable
performance guarantee.
3 - A One-Warehouse Multi-Retailer Inventory System with
Non-Homogeneous Poisson Demand
Christian Bohner, Technische Universität Mönchen, Arcisstr. 21,
Munich, Germany, , Stefan Minner
Product lifecycles and demand seasonality are important characteristics of
inventory systems. We extend the continuous review one-warehouse multi-
retailer inventory problem to non-homogeneous Poisson demand. Using the
unit-tracking approach, we find optimal time-dependent parameters for base-
stock policies both for the warehouse and the retailers. A numerical study shows
that the exact dynamic solution clearly outperforms the solution obtained from
time decomposition.
4 - Inventory Control of Intermittent Demand Combined with
Economic Indicators
Meng Yang, Tsinghua University, 519 Shunde Building, Beijing,
100084, China, Wanshan Zhu
The inventory cost can be very high for expensive service parts of many
companies, e.g., Caterpillar Inc., because their demand is highly unpredictable
due to its intermittency. One way to reduce the cost is to make use of economic
indicators that have a leading effect on the demand. We develop a Markov
decision model to incorporate the economic indicator information for better
controlling the inventory and quantify the value of this information.
5 - An Integrated Method of Optimization of the Final Order of
Spare Parts
Nicholas Leifker, St. John Fisher College, 3690 East Ave.,
Rochester, NY, 14618, United States of America,
, Timothy Lowe, Philip Jones
At the end of a product’s life cycle, companies may place a final order of spare
parts to satisfy all future demand for the part. Determining the optimal policy can
be complicated when products contain multiple types of parts in which the failure
rates of the parts and products are not independent; in such cases, the optimal
final order quantities for all part types must be determined simultaneously. We
explore the concavity properties of this optimization problem, and present a
solution method.
WE06
06-Room 306, Marriott
Portfolio Analysis II
Contributed Session
Chair: Dhanya Jothimani, Doctoral Student, Indian Institute of
Technology Delhi, Department of Management Studies, New Delhi,
India,
1 - The Robust Merton Problem of an Ambiguity Averse Investor
Mustafa C. Pinar, Bilkent University, Faculty of Engineering,
Ankara, Turkey, Sara Biagini
We derive a closed form portfolio optimization rule for an CRRA investor diffident
about mean return and volatility estimates. Confidence is represented by
ellipsoidal uncertainty sets for the drift, given a volatility realization. The optimal
policy is shaped by a rescaled market Sharpe ratio, computed under the worst
case volatility. The result is based on a max-min HJB-Isaacs PDE, which extends
the classical Merton problem and reverts to it for an ambiguity-neutral investor.
2 - An Orthogonal Genetic Algorithm for Indexing Tracking Problem
Liang Bao, Professor, Xidian University, No. 2 South Taibai Road,
Xi’An, China,
In this paper, we propose an orthogonal genetic algorithm for index tracking
problem. Its significant feature is to incorporate an orthogonal design method into
the initial population generation process and crossover operation. Our algorithm
is more robust and can search the solution space in a statistically sound manner.
We executed our algorithm to 5 datasets drawn from major world markets. The
results compared with other published results show that our method has superior
performance.
3 - Embedded Options in Institutional Investors’ Asset
Allocation Problems
Changle Lin, Princeton University, 10 Lawrence Drive,
Apt 505, Princeton, NJ, 08540, United States of America,
John Mulvey
Various options are embedded in institutional investors’ asset allocation problems.
Pension funds are shorting a put option on the fund itself by requiring sponsors to
contribute if underfunded. Sovereign wealth funds and family offices, have
transfers from state or family businesses. The transfers depend on businesses’
performances and generate embedded options. We model the options and their
implications on asset allocation with real option theory, stochastic control and
dynamic programming.
4 - Modeling Uncertainties in Mean Variance Framework using
Robust Optimization
Dhanya Jothimani, Doctoral Student, Indian Institute of
Technology Delhi, Department of Management Studies,
New Delhi, India, , Ravi Shankar,
Surendra Singh Yadav
The classical mean variance (MV) framework ignores the uncertainties associated
with the estimates of the expected returns; hence, the classical portfolio
optimization problem is often called as error maximizer. In order to model the
data uncertainty in MV framework, this study uses robust optimization technique
to select the portfolios. The excess returns of portfolios obtained using robust
estimators were found to be favorable compared to those obtained using classical
estimators.
WE07
07-Room 307, Marriott
Risk Analysis II
Contributed Session
Chair: Maryam Tabibzadeh, California State University, Northridge,
1157 W., 30th St., Los Angeles, CA, United States of America,
1 - Modular Production Capacity Expansion: An Examination of
Collateral Risk
Martin Wortman, Professor, Texas A&M University, Dept of ISEN,
College Station, TX, 77843-3131, United States of America,
Cesar Malave
Modular production operations are gaining considerable attention within electric
power generation, chemical products, and bio-pharmaceutical industries. Modular
capacity expansion can greatly reduce the financial risk associated with
capitalizing production operations. However, modularized operations can also
present collateral risk that can be greatly exacerbated. We offer an analytical
explanation of this circumstance.
WE04