Sunday

SB25
■ SB25
25- West 213 B- CC
Radiation Therapy Treatment Planning I
Sponsor: Health Applications Society
Sponsored Session
Chair: Edwin Romeijn, Professor, University of Michigan, IOE
Department, 1205 Beal Avenue, Ann Arbor, MI, 48109-2117,
United States of America, romeijn@umich.edu
1 - A Column-generation-based Technique for Multi-criteria Direct
Aperture Optimization
Ehsan Salari, Research Associate, Massachusetts General Hospital
and Harvard Medical School, Francis H Burr Proton Therapy,
55 Fruit Street, Boston, MA, 02114, United States of America,
salari.ehsan@mgh.harvard.edu, David Craft, Jan Unkelbach
Multi-criteria optimization (MCO) has proved to be a promising approach to
radiation therapy treatment planning. MCO is typically employed in the Fluencemap
optimization stage to find a Pareto-optimal solution that yields the desired
trade-off between treatment evaluation criteria. In this study we investigate the
extension of the MCO approach to the direct aperture optimization problem and
develop heuristics to obtain a collection of apertures that can approximate the
Pareto surface.
2 - Determining Convex Objective Functions and Importance
Factors in Multi-criteria IMRT Planning
Taewoo Lee, University of Toronto, 5 King’s College Road, Toronto,
Canada, taewoo.lee@utoronto.ca, Michael Sharpe, Timothy Chan,
Tim Craig
In multi-criteria optimization, objective function parameters are often determined
by a trial-and-error process. Multi-criteria IMRT planning typically involves many
objective functions, which leads to a large parameter space to search over. We
develop an inverse optimization method to determine convex objective functions
and parameters that are most critical in treatment planning. Results show the
potential to both streamline the planning process and increase the treatment
effectiveness.
3 - The Effect of Tumor Repopulation on Fractionation Schedules in
Radiation Therapy
Jagdish Ramakrishnan, Massachusetts Institute of Technology, 77
Massachusetts Avenue, Cambridge, MA, United States of America,
jagdish@mit.edu, David Craft, Thomas Bortfeld, Jan Unkelbach,
John N. Tsitsiklis
We consider optimizing the fractionation schedule and the number of treatment
days for radiation therapy. The tumor control probability is maximized subject to
a constraint on the normal tissue complication probability. We consider both
exponential and gompertzian tumor repopulation between treatment fractions in
the linear-quadratic formalism. Such a framework provides insights as to which
types of treatment protocols (e.g., hypo-fractionation) are beneficial for various
disease sites.
4 - A Method for Improving the Dose Distribution Quality of
Multi-criteria Radiation Therapy Plans
Rasmus Bokrantz, KTH Royal Institute of Technology, SE-100 44,
Stockholm, Sweden, bokrantz@kth.se
This talk considers an approach to radiation therapy planning where possible
treatment options are explored through realtime interpolation over precomputed
solutions. A method is presented that improves the quality of interpolated
solutions by minimizing a projective distance to the nondominated frontier under
constraints on maintained dose distribution quality. Also, minimization of dose
changes during conversion into deliverable machine settings are discussed in view
of the presented method.
■ SB26
26- North 221 A- CC
Emerging Topics in Supply Chain Management
Sponsor: Manufacturing & Service Oper Mgmt
Sponsored Session
Chair: Li Chen, Assistant Professor, Duke University, 100 Fuqua Drive,
Durham, NC, 27708, United States of America, li.chen@duke.edu
1 - Integrating Inventory Replenishment and Cash Payment
Decisions in Supply Chains
Wei Luo, Duke University, 100 Fuqua Drive, Durham, NC,
United States of America, wei.luo@duke.edu, Kevin Shang
We provide a modeling framework that integrates financial flows into a two-stage
supply chain where each location procures inventory based on cash available. We
consider different payment schemes and derive joint optimal and near-optimal
INFORMS Phoenix – 2012
88
inventory and cash policies. Our study demonstrates that an effective cash
payment policy can mitigate the supply disruption risk and improve the overall
supply chain efficiency.
2 - On the Profitability of an Eco-Friendly Supply Chain
Yang Li, PhD Student, Duke University, 100 Fuqua Drive,
Durham, NC, 27708, United States of America,
yang.li2@duke.edu, Fernando Bernstein, Kevin Shang
We study a two-stage supply chain for eco-friendly problems. The production
technology for eco-friendly products is more costly, but these products use
components with less fossil-fuel content than regular products. In particular, ecofriendly
products are less exposed to the price volatility of petroleum. We
examine scenarios in which eco-friendly products are more profitable.
3 - Competitive Quality Choice and Remanufacturing
Adem Orsdemir, Kenan Flagler Business School, University of
North Carolina, Chapel Hill, NC, 27599, United States of America,
adem_orsdemir@kenan-flagler.unc.edu, Eda Kemahlioglu Ziya,
Ali Parlakturk
We consider an Original Equipment Manufacturer who faces competition from an
Independent Remanufacturer. We explicitly characterize how OEM competes
with IR in equilibrium. IR’s entry threat as well as its entry can decrease
consumer and social surplus. We show either weak IR or strong IR is desirable for
reducing the environmental impact. Comparing our results with benchmarks in
which OEM remanufactures suggests that encouraging IRs to remanufacture in
lieu of OEMs may not benefit environment.
4 - Fixing Phantom Stockouts: A POS-Based Shelf Inspection
Model
Li Chen, Assistant Professor, Duke University, 100 Fuqua Drive,
Durham, NC, 27708, United States of America, li.chen@duke.edu
”Phantom stockout” is a retail stockout phenomenon caused by shelf execution
failure and/or product shrinkage (e.g., theft and spoilage). In this paper, we
propose a simple but effective partially-observable Markov decision process
(POMDP) model to tackle this problem. We show that the optimal shelf
inspection policy is a threshold policy based on the number of consecutive zerosales
periods. We further extend the analysis to models with time-varying
parameters.
■ SB27
27- North 221 B- CC
Supply Chain Models with Multi-sourcing and
Information Updates
Sponsor: Manufacturing & Service Oper Mgmt
Sponsored Session
Chair: Eylem Tekin, Instructional Associate Professor, University of
Houston, Department of Industrial Engineering, Houston,
United States of America, etekin@central.uh.edu
1 - Inventory Replenishment and Demand Allocation Decisions for
Multi-Sourced Items
Abhilasha Katariya Prakash, PhD Student, Texas A&M University,
503 nagle st, apt 102, college station, TX, 77840, United States of
America, abhilashapk@neo.tamu.edu, Eylem Tekin, Sila Cetinkaya
We consider a manufacturer using multi-sourced parts from a contractual vendor
(CV) and a spot market (SM). The CV holds inventory at a vendor managed hub
and charges a fixed unit price under a quantity commitment contract. Inventory
procured from the SM with volatile prices is held at an advance purchase hub.
The overall problem deals with when and how much to order from each source
and how to allocate demand to the alternative hubs.
2 - Dynamic Inventory Replenishment Decisions with Bayesian
Learning of Supply Yield Uncertainty
Baykal Hafizoglu, Arizona State University, 699 S Mill Avenue,
Tempe, AZ, 85281, United States of America, baykal@asu.edu,
Sibel Salman, Esma Gel
We consider a periodic-review inventory replenishment problem with yield
uncertainty, where the quantity that the supplier ships in response to an order is
random. We assume that the parameters that govern the supplier’s yield are not
known with certainty in advance. We propose a Bayesian updating scheme that
learns supplier’s yield parameters over time. We compare performance of
Bayesian learning scheme with other strategies such as safety stock policy, and
simple heuristics.