Informs 2015

93 / 552

INFORMS Philadelphia – 2015
91
SB76
76-Room 204C, CC
Efficient Learning in Stochastic Optimization
Sponsor: Simulation
Sponsored Session
Chair: Ilya Ryzhov, University of Maryland, 4322 Van Munching Hall,
Robert H. Smith School of Business, College Park, MD, 20742,
United States of America,
1 - Cost-efficient Learning for Crowdsourced Ranking
Qihang Lin, The University of Iowa, 21 East Market Street, Iowa
City, IA, 52245, United States of America, ,
Xi Chen, Kevin Jiao
Crowdsourcing is often used as a tool to rank a list of items based on pairwise
comparisons. However, the comparison results by crowdsourcing have a low
quality due to unreliable workers. To reduce the cost and increase the accuracy of
ranking, we propose a multistage strategy where pairs of items are assigned to
workers based on a joint learning of item’s ranking and worker’s reliability. The
performances of our strategies are evaluated over simulated and real data.
2 - Sequential Allocation for Customer Acquisition
Eric Schwartz, University of Michigan, ,
Katie Yang, Peter Fader
To acquire customers, firms allocate resources across a range of sources of
acquisition, but they are uncertain about which ones are best. Over time they
learn about their customers and sequentially reallocate their resources to earn a
better return on acquisition spend. We frame the sequential acquisition decisions
as a multi-armed bandit problem, and comparing a set of acquisition policies to
assess their ability to acquire from the right sources of customers.
3 - Optimal Dynamic Pricing with Demand Model Uncertainty:
A Squared-Coefficient-of-Variation Rule
Bora Keskin, Duke University, Fuqua School of Business,
100 Fuqua Drive, Durham, NC, 27708-0120,
United States of America,
We consider a price-setting firm that sells a product over a continuous time
horizon. The firm is uncertain about the sensitivity of demand to price changes,
and updates its prior belief on an unobservable sensitivity parameter by observing
demand responses. We derive and solve a partial differential equation to show
how the value of learning should be projected onto prices in an optimal fashion.
4 - Expected Improvement is Equivalent to OCBA
Ilya Ryzhov, University of Maryland, 4322 Van Munching Hall,
Robert H. Smith School of Business, College Park, MD, 20742,
United States of America,
We consider ranking and selection with independent normal observations, and
analyze the asymptotic sampling rates of expected improvement (EI) methods in
this setting. EI often performs well in practice, but general rate results have been
largely unavailable. We prove that variants of EI produce simulation allocations
that are essentially identical to those chosen by the optimal computing budget
allocation (OCBA) methodology. This is the first general equivalence result
between EI and OCBA.
SB77
77-Room 300, CC
Supply Chain Management II
Contributed Session
Chair: Ehsan Bolandifar, Assistant Professor, The Chinese University of
HongKong, Room 922, 9/F, Cheng Yu Tung Building, No.12,
Chak Cheung Street, Shatin, N.T., HongKong, Hong Kong - PRC,
1 - Cooperative Replenishment in the Presence of Intermediaries
Behzad Hezarkhani, Assistant Professor, Nottingham University
Business School, Jubilee Campus, Nottingham, United Kingdom,
, Marco Slikker,
Tom Van Woensel
In complex supply chains, individual downstream buyers would often rather
replenish from intermediaries than directly from manufacturers. Direct
replenishment from manufacturers can be a less costly alternative when carried
out by the buyers cooperatively. This talk presents a framework for
cooperative/non-cooperative replenishment in multi-product supply chains with
intermediaries.
2 - Two-class Single-period Inventory Allocation Policies in Smart
Meter Installation Projects
Behzad Samii, Vlerick Business School, Ave de Boulevard 21,
Brussels, Belgium,
Smart meter device are the costliest elements of rollout projects. Complexity
stems from supply inflexibility due to strict tendering procedures and high
holding cost due to fast obsolescence. If some partial information regarding the
bottom line impact of a shortage in one customer class compared to the other can
be conjectured, then we can derive closed form expressions for the expected
number of units short in each demand class under commonly used SN and TN
nesting allocation mechanisms.
3 - Architecting Fail-safe Supply Networks
Shabnam Rezapour, The University of Oklahoma, 2248 Houston
Ave., apt # 2, Norman, OK, 73071, United States of America,
, Amirhossein Khosrojerdi,
Janet K. Allen, Farrokh Mistree
A fail-safe network is one which mitigates the impact of disruptions and provides
an acceptable service level. This is achieved by designing its topology (structurally
fail-safe) and coordinating flow dynamics (operationally fail-safe). We analyze the
importance of being robust, resilient, and controllable in having structurally fail-
safe against disruptions. We show to have an operationally fail-safe supply
network, flow dynamics should be reliable against demand and supply-side
variations.
4 - Waveless Warehousing ? Why and Why Not ?
Adrian Kumar, Exel Inc, 570 Polaris Parkway, Westerville, OH,
United States of America,
Manjeet Singh
E-fulfillment operations constantly struggle with processing peak volumes quickly
due to system, labor and equipment constraints. Waveless is a dynamic order
fulfillment method that pulls demand into a resource/sub-system when it
becomes available. The dynamic order set is built on optimal real time decisions
based on productivity, equipment utilization, etc. This study defines complete and
partial waveless systems and discusses the pros and cons of implementing them.
5 - Component Procurement through Group
Purchasing Organizations
Ehsan Bolandifar, Assistant Professor, The Chinese University of
Hong Kong, Room 922, 9/F, Cheng Yu Tung Building, No.12,
Chak Cheung Street, Shatin, N.T., Hong Kong - PRC,
, Mojtaba Soleimani
This paper studies component procurement in a supply chain setting where two
competing Original Equipment Manufacturers (OEMs) source a common
component from a competitive supply market. We assume that ordering happens
after procurement negotiations, i.e., OEMs first compete in the market before
they negotiate for their component procurement potentially through Group
Purchasing Organizations (GPOs). We show that procurement through a GPO
may hurt an OEM with a lower bargaining power.
SB78
78-Room 301, CC
Supply Chain Practice and Empirics
Contributed Session
Chair: Faraz Ramtin, University of Central Florida, 2011 Puritan Rd,
Orlando, FL, 32807, United States of America,
1 - Is Supply Chain Success Emulatable? A Framework of Analogous
Learning from Supply Chains and a Case Study
Violette Wen, PhD Student, The University of Auckland,
12 Grafton Rd, CBD, Auckland, 1010, New Zealand,
Tiru Arthanari
We explore the feasibility of emulating from one successful supply chain to
another produce line in agri-fresh produce. The case will study the state-of-art
New Zealand kiwifruit supply chain and provide a framework with key enablers
and disenablers for transferring knowledge to the struggling Xinjiang Hami melon
industry in China. The research will provide rich empirical evidence about a
developing country’s agri-fresh supply chain at various levels.
2 - The Inventory Value of Cross Docking in a Supply Chain:
An Empirical Study
Xingyue Zhang, Lehigh University, 621 Taylor Street, Bethlehem,
PA, 18015, United States of America,
Oliver Yao, Jiazhen Huo, Yongrui Duan
Cross docking is a supply chain strategy to enhance supply chain performance by
directly moving inbound orders to outbound shipments without storage. Using a
large-scale, SKU level data set collected from a large retail chain, we find that
cross docking reduces store-level inventory by 101 units on average and that cross
docking is more beneficial to reduce inventories for products with higher prices or
higher demand rate, or for the stores that are closer to their distribution center.
SB78