2016 INFORMS Annual Meeting Program

INFORMS Nashville – 2016

414

2 - IT Processes Improvement

Larisa Shwartz, IBM,

lshwart@us.ibm.com

With the advent of cognitive computing, new generations of cognitive systems

and services are being conceived. In IT service management, cognitive approaches

used for optimization and automation of IT Service management processes. We

discuss an integrated framework for problem resolution that enables an

automated discovery of informative phrases from IT incident tickets which are

later used to construct knowledge and facilitate automation of IT service

management. The effectiveness and efficiency of our framework are evaluated by

an extensive empirical study of a large scale real ticket data.

3 - Business-driven Optimization Of It Service Configuration In

Public And Hybrid Clouds Based On Performance Forecasting

Genady Grabarnik, St. John’s University, Queens, NY, Mauro

Tortonesi, Larisa Shwartz

Modern Cloud environments are rapidly evolving, leading to a growing adoption

of dynamic pricing for virtual resources and of speedier deployment tools and to

the emergence of hybrid Cloud scenarios. The need to address these challenges is

paving the way to a new generation of Cloud services, capable of adapting to

changes in their operating conditions and deployment environments by dynami-

cally realigning their configuration. However, their management will require

new and more sophisticated tools. This paper presents a new optimization solu-

tion for Cloud-based IT services, that tries to address these issues by using queu-

ing analysis of the services’ workflows and ILP optimization.

WB55

Music Row 3- Omni

Inventory Management VIII

Contributed Session

Chair: Yue Zhang, Duke University, 5507 Butterfly Ln Apt 207,

Durham, NC, 27707, United States,

yueyue.zhang@duke.edu

1 - Deep Learning For Newsvendors

Afshin Oroojlooyjadid, Lehigh University, 200 West Packer Ave,

Bethlehem, PA, 18015, United States,

afo214@lehigh.edu

Martin Taká , Lawrence Snyder

We study a newsvendor problem in which each demand observation also has a

set of features. We propose an algorithm based on deep learning that optimizes

the order quantity based on the features. It integrates the forecasting and

inventory-optimization steps, rather than solving them separately. The algorithm

does not require probability distributions. Numerical experiments on real-world

data suggest that our algorithm outperforms approaches from the literature,

including data-driven and SVM approaches, especially for volatile demands.

2 - Inventory Repositioning In Product Sharing Networks

Xiang Li, University of Minnesota, 2508 Delaware St SE,

Apt 473A, Minneapolis, MN, 55414, United States,

lixx1315@umn.edu

, Saif Benjaafar, Xiaobo Li

We study a product sharing network in which customers can pick up a product

without reservation, and are allowed to keep the product for as long as they

want, without committing to a specific return time or location. We model the

periodic inventory re-positioning as a Markov decision process. We characterize

the qualitative properties of the optimal policy .

3 - Dynamic Inventory And Price Control In The Face Of

Unknown Demand

Tingting Zhou, Rutgers university, Newark, NJ, 07102, United

States,

tingzhou@rutgers.edu,

Michael N Katehakis, Jian Yang

We study adaptive policies that combat unknown demand in a dynamic inventory

and price control setting. Inventory control is achieved by targeting newsvendor

ordering quantities for empirical demand distributions learned over time. On top

of that, demand-affecting prices are selected in a fashion that balances between

exploration and exploitation. When burdened with the task of selecting the most

profitable price, bounds for the regret can range between the orders of T1/2and

those of T2/3. Simulation studies are conducted as well.

4 - Approximating Optimal Inventory Policies For Assemble To Order

Manufacturing Systems

Levi DeValve, Duke University, 716 Turmeric Lane, Durham, NC,

27713, United States,

levi.devalve@duke.edu

, Yehua Wei,

Sasa Pekec

We study the classical one-period assemble-to-order problem, modeled as a two

stage stochastic integer program with recourse. We leverage a primal-dual

approach to develop several approximation methods based on newsvendor

solutions. We identify co-monotone demand and symmetric hierarchy systems as

special cases where a component newsvendor solution is optimal under a

constant mark-up assumption, and provide closed-form bounds on sub-optimality

for more general cases. Further, we establish closed-form bounds for systems

where components serve many products and show asymptotic optimality.

5 - Serial Inventory Systems With Markov-modulated Demand:

Derivative Bounds, Asymptotic Analysis, And Insights

Yue Zhang, Duke University, 5507 Butterfly Ln Apt 207, Durham,

NC, 27707, United States,

yueyue.zhang@duke.edu

, Li Chen,

Jing-Sheng Jeannette Song

We consider the inventory control problem for serial supply chains with

continuous, Markov-modulated demand. We perform a derivative analysis and

develop general, analytical solution bounds for the optimal policy. We further

derive a simple procedure for computing near-optimal heuristic solutions. We

next perform asymptotic analysis with long replenishment lead time. We show

that the relative errors between our heuristics and the optimal solutions converge

to zero as the lead time becomes sufficiently long, with the rate of convergence

being the square root of the lead time.

WB56

Music Row 4- Omni

Predictive Analytics in eBusiness